AI Toolkit 2025 Report

Artificial Intelligence Toolkit 2025

Leverage our established global network.

We generate high levels of engagement for our clients and members by leveraging our established global wealth management community. Our user base includes representatives from leading financial services institutions, consultants, and investors from around the world.

285,000+ Annual Users

35,000+ Contacts

30m Annual Impressions

15,000+ Subscribers

15,000+ Social Followers

AI TOOLKIT Contents

The report examines how AI is reshaping the future of wealth management, driving innovation across advisory, investment, and client service models, analysing its impact on the strategies and operations of wealth management firms.

Topic

The first part of each Showcase is the Topic. Each contributor establishes a Topic, introduces it, highlights its relevance to wealth management, and explains why a wealth management business should consider it for their business.

Solution

The second part of each Showcase is the Solution. The Solution is designed to support the Topic, with each contributor tasked with highlighting how their Solution delivers on the Topic. This includes elements such as its features and functions, use cases and other relevant elements of their offering.

Showcase #1 Adviser Efficiency

In today’s fast-paced financial environment, wealth managers and advisers are increasingly time-poor. This is where AI, is starting to show its value.

Contributed by aveni

Showcase #2 Future Foundation

To unlock AI's full potential, wealth managers must first address a foundational requirement: data.

Contributed by Point

Showcase #3 Generative AI

GenAI gives financial firms a powerful way to process and respond to unstructured and structured data, driving efficiencies and growth.

Contributed by InvestCloud

Showcase #4 AI-powered Video

Leverage personalised AI-powered videos to increase the value and frequency of client engagement, and acquire incremental AUM.

Contributed by Storyline AI

Showcase #5 Portfolio Intelligence

AI is not changing the principles of sound wealth management –but it is redefining what is possible.

Contributed by Pebble

Showcase #6 Portfolio Management

Investment portfolio optimisation has long relied on historical data analysis to model uncertainty and guide decisions. These traditional methods are being disrupted by AI.

Contributed by Raise Partner

Showcase #7 AI Agents

Investment teams are rethinking their technology strategies to stay competitive and future-proof their processes. Traditional operating models are being replaced by new approaches that harness AI.

Contributed by Jacobi

Showcase #8 Intelligent Workflows

AI is unlocking growth opportunities through intelligent workflows.

Contributed by Zeplyn

Showcase #9 Future Proof

Today, thanks to major advancements in Big Data, chatbots and deep learning, AI is no longer speculative; it’s essential.

Contributed by Objectway

Showcase #10 GenAI Portfolio Solutions

Wealth managers operate in an increasingly complex landscape where clients expect tailored, datadriven investment insights. This is where Gen-AI portfolio advisory solutions come into play.

Contributed by GPTadvisor

Showcase #11 AI For The Next Gen

AI’s successful implementation hinges on robust cloud infrastructure.

Contributed by AWS

FOREWORD

The direction of travel is clear. From digitalisation to intelligence. From the experience economy to the AI-native economy. Rethinking wealth management completely with intelligence baked in every dimension and aspect of your business is no longer optional –it’s essential. Start the journey now.

The extraordinary pace of recent Artificial Intelligence (AI) innovation marks a significant departure from the previous major technology cycles of cloud and mobile, promising a profound transformation in wealth management. We are at the cusp of shifting from digital-native business archetypes to AInative businesses. This is a paradigm shift in our technological and business strategies which is being born as we speak. These businesses go beyond just using AI tools here and there. They are set up and managed as businesses with AI baked-into every aspect, from the enterprise itself to everything that is customer facing, and also in the ways they operate within their ecosystem.

This evolution aligns with "Thinking like an AI native business" a concept I explored in "The Fast Future Blur: Discover Transformative Interconnections Shaping the Future". In this future-proofing book, we suggest viewing AI's integration through interconnected dimensions – customer, enterprise, and ecosystem – alongside elements like discovery, design, decision, dexterity, and deduction. This framework is crucial for understanding AI's groundbreaking potential.

The acceleration imperative

The velocity of change is staggering. Several large industry players began their AI journeys as early as two years ago. Morningstar launched Mo in 2023, the first personified next-gen chatbot for investment research, for both enterprise and customer discovery. BloombergGPT, trained on Bloomberg's proprietary data, became available for Bloomberg customers, and Moody's launched GenAI Research Assistant. Also, JP Morgan developed LLM Suite by finetuning OpenAI to augment its employees (enterprise/ discovery, decision) and designed IndexGPT, an AIpowered tool for personalised investment strategies (enterprise/design and customer/dexterity).

There is a clear buy-in from decision makers. Similar to all major transformative technology cycle, time is needed. Doing nothing is the riskiest choice of all.

We are at the cusp of shifting from digital-native business archetypes to AInative businesses, a paradigm shift in our technological and business strategies.

The extraordinary pace of recent AI innovation marks a significant departure from the previous major tech cycles of cloud and mobile, promising a profound transformation in wealth management.

Beyond efficiency: strategic AI adoption

The future requires a dual focus. First, start focusing on gaining efficiencies as a stepping-stone and a training ground while always aiming to go beyond efficiencies. Explore AI's potential to create new businesses, transform customer experiences, and redefine value propositions. Decide on a systematic and strategic roadmap. Second, establish guardrails and ethical frameworks while preserving dexterity as the technologies are evolving.

It’s

time to rethink your business…again!

While AI clearly offers immediate efficiency gains especially at the discovery dimension, true differentiation and real economic value lies at leveraging AI at the interconnections of all other dimensions.

As in the digital era, companies that thrive will adopt an AI-native mindset and fundamentally rethink their businesses. Start now, experiment, and learn from both successes and failures. Don't expect that knowledge retrieval efficiencies will lead to sustainable differentiation.

Those who will be more successful will be those that leverage AI to unlock economic value both for the customer and their business. This combination will create long-lasting value with network effects.

Global FinTech & Technology Thought Leader

Proudly human authored and inspired by my WealthTech passion.

Dr. Efi Pylarinou

INTRODUCTION

Welcome to the AI Toolkit Report 2025

The integration of Artificial Intelligence (AI) into wealth management is revolutionising the industry. Wealth managers are using new, AIdriven technologies to drive enhanced efficiency, greater levels of personalisation in their investment recommendations, and in their decision-making.

This report features 16 articles contributed by a range of industry participants – from wealth managers, to vendors, to consultants focused on financial services. In the case of the contributions from technology vendors, each has also been tasked with providing a Showcase formed of a Topic consistent with the AI theme of the report, alongside a Solution that supports the delivery of that Topic.

Where we are today...

One prominent trend is the growing use of AI-powered tools, which offer automated, algorithm-driven financial planning services. These provide tailored investment recommendations based on client data, risk tolerance, and financial goals, democratising access to high-quality financial advice. By integrating and leveraging Natural Language Processing (NLP) and Machine Learning (ML), wealth managers are able to leverage technology to continuously refine their investment strategies and adapt to market fluctuations in real time, offering clients more precise and more cost-effective solutions.

Another significant trend is the application of AI in portfolio management and risk assessment. Advanced analytics tools harness AI to identify patterns and predict market movements, enabling wealth managers to make data-driven investment decisions. These tools also allow for more granular risk profiling, helping firms better align investment strategies with client needs.

As we saw during the course of last year with contributions to our CX Toolkit Report, and discussions during the related CX Toolkit roadshow events, AI is also reshaping client engagement through enhanced personalisation and communication. By analysing significant amounts of data, AI-driven systems provide wealth managers with insights into client behaviour, preferences, and life events. This in turn enables hyper-personalised recommendations and timely interventions, fostering deeper client relationships. Additionally, the integration of NLP-powered chatbots and virtual assistants ensures seamless, 24/7 support, improving the client experience.

As AI continues to advance, its ability to combine human-like interaction with sophisticated analytics is setting a new standard for the wealth management industry, driving innovation and delivering greater value to both clients and advisers.

Against this exciting and fast-paced backdrop of ever evolving technology, we are pleased to introduce you to our AI Toolkit Report 2025. This report follows on the heels of an initial AI Toolkit Roadshow event hosted by AWS in Zürich late last year, and our first Toolkit Roadshow event held in New York earlier this year. We have captured some of the insights from the discussions and exchanges at these events, and have included them in this, our second Toolkit Report in what is an ongoing series of reports.

Where we are headed tomorrow...

Looking ahead, it is clear to all that the future of AI in wealth management holds transformative potential, driven both by advancements in technology and evolving client expectations. One key development will be the integration of generative AI, enabling wealth managers to create custom financial models and reports that address unique client scenarios.

Artifical Intelligence Toolkit 2025

The AI Toolkit 2025 is the second report within our new Toolkit Report Series. If the application of AI is becoming central to the business of wealth management, the purpose of this report is to highlight relevant technology Topics and Solutions, packaged into eleven individual Showcase entries, which could or should be part of a wealth manager's thinking when considering the potential application of AI related technology, to benefit their advisers and also, their end clients.

What is a Showcase?

Each Showcase is intended to focus on a specific Topic with the contributor introducing their topic and explaining its relevance to the report's overall relevance to the theme of AI. Supporting the Topic is a Solution entry which then highlights the firm's supporting technology offering. The overall contribution of each firm features a Topic and a Solution, and both together should educate and inform the reader on an aspect (and potential application) of AI that they may consider for their business.

This capability will streamline complex processes, making wealth management more accessible and efficient while allowing advisers in turn to focus on higher-value tasks.

Another emerging trend is the convergence of AI and sustainability in investment strategies. As environmental, social, and governance (ESG) considerations become increasingly important, AIpowered tools will help wealth managers assess the impact of investments more accurately. By analysing ESG data and forecasting long-term sustainability outcomes, these tools will empower clients to align their portfolios with their values, fostering more responsible and impactful investing practices.

Finally, the future of AI in wealth management will be marked by increased collaboration between human advisers and AI systems. Rather than replacing human expertise, AI will act as an augmentative tool, providing actionable insights and automating routine tasks. This relationship will enhance the decisionmaking process, enabling advisers to deliver even more highly personalised and strategic advice. As AI continues to evolve, its role in wealth management will expand, ensuring a more adaptive, client-centric, and innovative industry landscape.

Toolkit Report Series

Following this, our second Toolkit Report focused on AI, our Toolkit series will feature ongoing reports mixing thematic, geography and wealth manager-segment focused releases. In the series planned for 2025, we will be publishing:

• UK Toolkit

• Future View Toolkit

• Adviser Toolkit

• US RIA Toolkit

• APAC Toolkit

Toolkit Roadshow events

Following each report, we will look to deliver supporting Roadshow events. For the AI Toolkit Roadshow, we have so far hosted events in Zürich, New York and shortly in London too. We plan further events in the Middle East and APAC later in the year. These events are free to attend for any form of wealth manager and provide technology vendors an opportunity to sponsor or demo.

Editorial Programme Toolkit Reports 2024/2025

The Toolkit Report Series covers thematic, geography and wealth manager segment-focused reports, each tasked with delving into the topics and supporting technologies of relevance to help wealth managers of all types better understand how they should bring technology into their business and in which areas.

UK Toolkit

Geographic

Q2 2025

Focused on the United Kingdom, the UK Toolkit 2025 Report will seek to bring in 10-15 Showcases from contributors, each tasked with highlighting how an area of technology and their offering can support the business needs of a wealth management firms in the region. The report will be supported by a Toolkit Roadshow event in London in September 2025.

Future View

Toolkit

Thematic

Q2 2025

This report will look at the technologies that will support wealth managers in future proofing their businesses. Regardless of positioning on the infrastructure map, each participant vendor needs to highlight why and how their topic and solution is relevant. The report will be supported by Toolkit Roadshow events in Q2-Q3 2025.

US RIA Toolkit

Segment

Q3 2025

The US Registered Investment Advisor market plays a significant role in the wider US wealth management sector, with a wide range of technologies built to support its needs. This Toolkit will showcase a range of topics and solutions focused on the needs of the US RIA market.

CX in WEALTH

Showcasing the application of CX in wealth management

SCENE SETTER Data Insights

Welcome to our Data Insights section. This is designed to provide a curated collection of relevant insights and data points, sourced from reputable third parties, to help contextualise the role of AI in wealth management. The section serves as a foundation for understanding key trends, challenges, and opportunities, offering valuable perspectives for industry professionals navigating the AI-driven shift in wealth management.

For any wealth management firm, as well as any technology vendor offering services to the sector, the potential impact of Artificial Intelligence (AI) on the industry is too significant to overlook. AI is rapidly transforming financial services, enabling firms to enhance decision-making, improve operational efficiencies, and deliver highly personalised client experiences, among a wide range of potential benefits for those that engage.

Beyond wealth management, AI’s impact extends across society, economies, and businesses worldwide. From automating complex workflows to generating predictive analytics, AI is reshaping industries at an unprecedented pace. As firms adopt AI-powered tools, they must balance innovation with ethical considerations, ensuring transparency, security, and compliance. Understanding these dynamics is essential for any organisation aiming to remain competitive in an increasingly digital and data-driven financial landscape.

The rapid rise of AI in wealth management

A recent study by Accenture found that 84% of wealth managers believe AI will significantly transform the industry within the next five years. This transformation is already evident, with firms leveraging AI-powered analytics to optimise portfolio management, automate compliance processes, and provide hyperpersonalised investment recommendations.

AI is also reshaping regulatory compliance and risk management within wealth management. Compliance costs have been a major burden for financial firms, but AI is helping mitigate these expenses by streamlining processes and improving accuracy. AI-driven tools can process vast amounts of data in real time, identifying patterns and anomalies that might indicate fraudulent activity or regulatory breaches. This capability is essential as regulatory requirements continue to evolve and become more complex.

The shift toward AI-powered solutions is also evident in client engagement and advisory services. AI chatbots and virtual assistants are now handling routine client inquiries, freeing up advisers to focus on high-value interactions. Predictive analytics is enabling firms to anticipate client needs, offering tailored financial strategies based on real-time market data and behavioural insights. As AI continues to develop, its role in wealth management will only expand, driving greater efficiency, innovation, and client-centric services across the industry.

AI’s impact on wealth management

The influence of AI on wealth management is expected to be profound. PwC’s 2023 Global Asset and wealth management Survey predicts that AI-enabled digital investment platforms will manage assets worth nearly US$6 trillion by 2027 – almost double the US$3 trillion figure from 2022. This shift highlights the increasing role of algorithm-driven platforms in wealth management, as they provide data-driven insights, risk assessment, and personalised investment strategies with unparalleled precision.

PwC's most recent 2024 Asset & wealth management Report further highlights the transformative impact of AI on the industry. Key predictions include:

• Revenue growth: 80% of investment and wealth management firms anticipate that disruptive technologies like AI will drive revenue growth. Firms that rapidly adopt 'tech-as-a-service' models could see a 12% revenue boost by 2028.

• AI as a transformational technology: 73% of investment and wealth management organisations view AI as the most transformative technology over the next two to three years, underscoring its pivotal role in shaping the industry's future.

Moreover, AI's role extends beyond investment management to operational efficiency. A 2025 survey by NVIDIA indicates that nearly 70% of financial services professionals have observed revenue increases of 5% or more due to AI, with many witnessing boosts between 10-20%. Furthermore, over 60% of respondents reported AI-driven cost reductions of at least 5% annually, demonstrating the technology’s potential to enhance profitability while reducing expenses. Nearly a quarter of surveyed firms are also leveraging AI to create new business opportunities and revenue streams, positioning AI as a key driver of innovation in the financial sector.

As AI continues to reshape wealth management, financial institutions must prioritise investments in machine learning, automation, and data analytics to stay competitive.

84% of wealth managers believe AI will significantly transform the industry within the next five years (Accenture).

DATA INSIGHTS

AI and client experience –the power of personalisation

AI is transforming client experiences by enabling unprecedented levels of personalisation. Businesses across industries are leveraging AI-driven insights to understand customer preferences, anticipate needs, and deliver tailored solutions in real time.

One of AI’s key strengths is its ability to analyse vast amounts of data, identifying patterns in customer behaviour that would be impossible for humans to process efficiently. This allows companies to offer hyper-personalised recommendations, whether in e-commerce, financial services, or healthcare. For instance, AI-powered chatbots can provide instant, context-aware responses, while recommendation engines, like those used by streaming services and online retailers, enhance user satisfaction by suggesting relevant content or products.

AI also improves client experience through predictive analytics. Businesses can leverage AI to better anticipate and in many cases predict customer needs before they arise, offering proactive solutions. Banks, for example, use AI to detect spending habits and suggest financial products tailored to individual users. Similarly, AI-driven personalisation in healthcare helps doctors recommend customised treatment plans based on patient history and genetic profiles.

However, while AI-driven personalisation enhances customer engagement, it also raises concerns about data privacy and ethical use. Businesses must balance customisation with transparency, ensuring clients understand how their data is used while maintaining security and trust.

Businesses can leverage AI to better anticipate and in many cases predict customer needs before they arise.

In the future, AI will continue refining client experiences, making interactions more intuitive and efficient. Companies that invest in responsible AIdriven personalisation will not only enhance customer satisfaction but also gain a competitive edge in an increasingly digital marketplace.

Wealth management firms employing AI broadly are also leading the charge in revolutionising client engagement, with 65% expecting major AI-driven transformations in client relationship management in the next one to two years (Wipro).

9/10

investors believe AI can be used effectively for researching financial products and services; eight in ten say AI can better support advisers in portfolio management.

KPMG, 2024

63% of investors expect their digital experiences with wealth management firms to match those of leading technology companies, increasing emphasis on omnichannel delivery.

KPMG, 2024

76% of firms report enhanced operational efficiency due to AI, allowing advisers to focus more on personalised client services.

Wipro, 2024

70%

of firms that are leveraging AI report a positive impact on client interactions.

Wipro, 2024

77% of firms acknowledge better decisionmaking capabilities through AI-driven predictive analytics, facilitating more tailored investment strategies.

IBS Intelligence, 2024

72%

of wealth managers say personalisation is the most critical trend shaping the industry.

PwC, 2023

Up to 23%

the level by which AI-based nudges can improve investor decision-making accuracy.

JP Morgan, 2023

Up to 90%

the reduction in client response time that can be driven by AI-powered customer service.

Gartner, 2023

DATA INSIGHTS

AI and portfolio optimisation

AI is revolutionising portfolio optimisation by enhancing data analysis, risk assessment, and decision-making. Traditional optimisation methods have relied more on historical data and statistical models, but AI leverages ML and deep learning techniques that identify patterns, predict market trends, and therefore allow advisers to adapt their investment recommendations to changing conditions in near real time.

One major advantage of the application of AI in portfolio optimisation is its ability to process significant amounts of data, including financial statements, news, and other macroeconomic indicators. AI-driven models can dynamically rebalance portfolios based on market shifts, reducing risk while maximising returns. Additionally, AI algorithms can be used to optimise asset allocation by taking multiple factors into account, such as volatility, correlations, and investor preferences.

Another innovation is reinforcement learning, where AI continuously learns from market feedback to improve trading strategies. This approach enables portfolios to become more adaptive, reducing human biases and emotional decision-making.

Despite its advantages, AI-driven portfolio optimisation does face challenges, such as data biases, model interpretability, and regulatory concerns. However, as AI technology advances, its role in portfolio management will continue to grow, offering investors smarter, more efficient ways to optimise their investments while simultaneously and effectively managing risk.

AI-driven models can dynamically rebalance portfolios based on market shifts, reducing risk while maximising returns.

14%

the potential level of improvement seen in AI-powered portfolio management risk-adjusted returns. BlackRock, 2023

63% of asset managers now incorporate AI-driven predictive analytics in what they do.

Deloitte, 2023

22%

the level of improvement in portfolio resilience generated by AI-driven stress testing. McKinsey, 2023

$19 billion

the sum Deloitte Insights projects global spending by financial services firms on quantum computing will escalate to by 2032, from $80 million in 2022, reflecting a 10-year compound annual growth rate of 72%.

Deloitte, 2023

5.3%

the level by which Pictet's AI-driven fund, based on the MSCI World index, outperformed the benchmark over a six-month period, demonstrating AI's potential in stock selection.

thetimes.co.uk

10-25% increase

is what investment firms utilising AI in their strategies have reported in investment returns compared to traditional methods. algoaiacademy.com

DATA INSIGHTS

AI and risk management / compliance

AI is transforming risk management and compliance by improving accuracy, efficiency, and adaptability. Traditional approaches rely on rule-based systems and manual oversight, but AI introduces advanced analytics, machine learning, and automation to detect risks, ensure regulatory compliance, and enhance decision-making.

In risk management, AI can analyse vast data-sets in real time to identify potential threats, such as market volatility, credit risks, and fraudulent activities. Machine learning models are able to detect patterns and anomalies that advisers might overlook, allowing for proactive risk mitigation. AI-driven predictive analytics therefore also help financial institutions forecast potential risks and adjust strategies accordingly.

Compliance is another area where AI is seen to be having a significant impact. Regulatory requirements are constantly evolving, making manual compliance checks time-consuming and error-prone. AI-powered compliance tools can monitor transactions, flag suspicious activities, and ensure adherence to regulations such as AML (anti-money laundering) and KYC (know your customer). Natural Language Processing (NLP) helps in reviewing legal documents and regulatory changes efficiently.

Despite its advantages, AI in risk management and compliance faces challenges, including data privacy concerns, model transparency, and regulatory acceptance. However, as AI technology advances, it will play an increasingly critical role in strengthening financial security, reducing fraud, and ensuring regulatory adherence.

Regulatory requirements are constantly evolving, making manual compliance checks time-consuming and error-prone.

60 million hours per year that could potentially be saved on compliance and enforcement activities through the efficiencies of AI.

Deloitte, 2023

50%

the reduction in financial losses generated by AI fraud detection.

McKinsey, 2023

60%

the amount by which KYC verification times are improved , driving greater onboarding efficiency, thanks to the use of AI.

Deloitte, 2023

50%

of organisations have not conducted AI risk assessments, indicating a significant gap in addressing potential AI-related risks.

airmic.com

53%

of firms say risk management is a key area where AI has already driven significant disruption.

Wipro, 2024

35-40%

the volume of reduction in regulatory breaches generated thanks to AI-driven compliance

PwC, 2023

25%

the level by which AI-based risk scoring enhances loan approval accuracy.

FICO, 2023

84%

of organisations experienced regulatory compliance issues stemming from a lack of transparency in AI applications within business processes. securitymagazine.com

DATA INSIGHTS

Challenges to adopting AI in wealth management

As has been outlined in earlier sections of our Data Insights section, AI indeed has the potential to enhance efficiency and decision-making in wealth management, but its adoption still faces several challenges.

• Regulatory and compliance concerns are a major hurdle. The financial industry is highly regulated, and AI-driven models must comply with strict legal frameworks. The lack of transparency in some AI algorithms, often referred to as “black box” models, can make it difficult to justify investment decisions to regulators and clients.

• Client trust and acceptance present another barrier. Many investors, particularly high-networth individuals, prefer interacting with human advisers and are reluctant to rely on AI-driven recommendations. Building confidence in AI’s accuracy and reliability is essential for widespread adoption.

• Implementation costs and talent shortages further slow adoption. AI requires significant investment in technology, infrastructure, and skilled professionals, which many firms lack.

• Market unpredictability can also limit AI’s effectiveness. Financial markets are influenced by unforeseen events, making it difficult for AI to adapt in real time (although rapid progress is being made in this area).

• Data security and privacy also pose significant risks. AI systems require significant amounts of client data, increasing the potential for cyber threats and regulatory scrutiny. Consequently, ensuring data integrity and security is critical.

Overcoming these challenges requires regulatory clarity, strong security measures, and strategic investment in AI capabilities.

5 out of 10

advisers feel like their firms are challenged to act on their AI vision. Accenture, 2022

68% of firms underscore the need for AIspecific training and talent acquisition – with many firms citing a significant skills gap in the AI domain.

Wipro

74% of companies struggle to move beyond pilot projects to fully scaled AI implementations, indicating difficulties in achieving widespread impact.

Boston Consulting Group, 2024

78% of businesses face challenges in AI adoption due to inadequate data infrastructures, underscoring the importance of robust data management for successful AI integration.

MIT / businessof.tech

55% of UK investors are unwilling to use AI tools for investment support, indicating a significant trust gap between clients and AI-driven advisory services.

Avaloq / workingexcellence.com

DATA INSIGHTS Summary

AI is poised to transform wealth management by enhancing efficiency, personalisation, and decisionmaking. As AI technologies advance, financial institutions will increasingly leverage them to provide smarter investment strategies, optimise portfolio management, and enhance client experience.

One key application is personalised financial advice, with AI-driven digital platforms using Machine Learning (ML) and NLP to analyse clients’ financial goals, risk tolerance, and spending to offer personalised investment strategies. Over time, these systems will continuously adapt to changing financial conditions and user preferences. AI-driven behavioural finance insights will help advisers better understand client emotions and biases, allowing them to offer tailored guidance that aligns with investors’ long-term financial well-being.

AI-powered predictive analytics will analyse significant data-sets to identify emerging market trends, assess risks, and provide real-time market insights. This will allow wealth managers to anticipate market shifts and adjust portfolios accordingly, leading to more proactive and data-driven investment decisions.

Fraud detection and cybersecurity will also benefit from AI innovations. Advanced AI models will be able to detect suspicious activities in real time, safeguarding clients' assets from fraudulent transactions and cyber threats.

AI will also improve operational efficiency by automating repetitive tasks such as portfolio rebalancing, tax optimisation, and regulatory reporting. This will allow human advisers to focus on strategic decision-making and high-value client interactions.

The above applications are just a snapshot of where AI is relevant in this industry. As AI continues to evolve, its applications in wealth management will dive into all aspects of the business of wealth management, redefining the industry and creating a more intelligent, efficient, and client-centric financial ecosystem.

Conclusion

The integration of AI into wealth management is transforming the industry, offering innovative solutions for personalisation, efficiency, and decision-making. From predictive analytics to hyper-personalised financial planning and blockchain integration, the applications of AI are vast, and grow by the day.

As AI technologies continue to advance, wealth managers must embrace these tools while at the same time addressing challenges such as data privacy and algorithmic biases. By leveraging the power of AI, the wealth management industry can enhance client experiences, democratise access to financial services, and continue their path of digital transformation.

INDUSTRY Perspectives

In this section, we present a diverse range of insights from wealth managers and a leading consulting firm. They offer an industry perspective on the transformative impact of AI, highlighting both the vast opportunities and the pressing challenges ahead. From optimising investment strategies to navigating regulatory complexities, these experts share their outlook on how AI is reshaping the financial landscape – and what it means for the future of wealth management.

Understanding the industry’s perspective on AI is crucial in today’s rapidly evolving financial landscape. Staying ahead requires keeping a finger on the pulse of AI advancements – what’s being done, what’s working, and where challenges lie. That’s why we’ve gathered insights from a specialist in AI who brings deep, focused expertise from his consulting firm employer offering a broad and multi-client perspective on how AI is reshaping wealth management.

Beyond theory, we explore real-world examples that demonstrate AI in action – how firms are leveraging it to enhance decision-making, streamline operations, and create new value. These insights serve as a foundation for the perspectives that follow, where solution providers will dive deeper into the technologies driving these changes. By combining strategic industry viewpoints with practical applications, this section is designed to give you a clear, informed, and actionable understanding of AI’s transformative role in the future of financial services.

Our aim with this report –as with all our Toolkit Reports – is to bring together a range of relevant, insightful and though-provoking opinions and commentaries from wealth managers, technology vendors, consultants and other players in the sector. Together, these will provide a view of some of the main themes and focus areas around the role and potential application of Artificial Intelligence (AI) in the wealth management industry today, and tomorrow.

The rise of chatbots for exploring specialised business content 1

By Geoffroy de Ridder, Head of Technology and Operations, Lombard Odier Group

Large Language Models (LLMs) now occupy a central position in the AI landscape, particularly in the Machine Learning (ML) branch. Using Deep Learning (DL) architectures, a sub-branch of ML, LLMs are revolutionising natural language processing, enabling significant advances in text comprehension and generation. These systems are trained on huge corpora of text to master the subtleties of natural language, and to learn the contextual relationships between words and sentences.

What are Large Language Models and how do they work?

ML has crucial, proven applications in many fields. In finance, for example, ML is used to detect fraud and analyse risks and trends in financial markets. Unlike other ML-based systems, LLMs do not need to be explicitly trained for each specific task such as translation or text summarisation. By learning from large datasets, they can quickly adapt to new user instructions. By providing an appropriate prompt, a user can therefore obtain accurate responses adapted to the context without needing to retrain or adjust the model.

How do banks interact with generative AI such as LLMs?

Interaction with generative AI technologies takes place mainly through chatbots, which enable users to communicate and explore data in a conversational manner. These virtual assistants facilitate access to a variety of services and information. For example, chatbots automate the analysis of legal contracts with banking counterparties or key service providers (e.g. market data), scanning thousands of pages in seconds to identify key clauses, contractual obligations and potential risks.

What is the level of adoption of these technologies in the banking sector?

Financial institutions are increasingly adopting use of these chatbots. Institutions that prefer a cautious and methodical approach to AI are starting by carefully evaluating solutions in terms of security and compliance, usually starting with less critical

functions. We observe in these cases that opensource solutions are often preferred with in-house development, offering greater control (i.e. flexibility and transparency), but sometimes requiring more time and expertise to implement effectively. This 'cautious' strategy can help minimise risks and ensure a smoother integration process.

On the other hand, banks with a greater appetite for innovation frequently choose cloud-based services that offer ready-to-use solutions, including highperformance models that can be rapidly deployed with full support and regular updates. By adopting these technologies quickly, they aim to gain competitive advantage and capitalise on the benefits of AI sooner.

What are the current limitations of using such technologies?

Chatbots based on generative AI offer great promise for banks, but several considerations must be made before their deployment. Firstly, data confidentiality is crucial. Chatbots handle large amounts of often sensitive information, and banks must therefore maintain robust security measures to ensure that it is treated in compliance with data protection laws.

Ensuring the quality of the data used by these technologies is also essential; poor quality data can lead to erroneous or incomplete results. Moreover, some LLMs are trained on text corpora that may contain biases or unethical information, which can lead chatbots to reflect and perpetuate human biases in their responses. We believe that effective data management and governance contribute to strong chatbot performance.

What approach has Lombard Odier taken to adopting this technology?

When it comes to deploying chatbot technology within an organisation, we distinguish between two strategies:

• Universal access: offer all employees access to chatbot tools and observe the different ways they use the technology to identify its added value.

• Targeted use: start with specific business use cases and a select group of pilot users to refine and perfect the technology before a wider roll-out.

At Lombard Odier, we chose the second approach: targeted use. This allowed us to experiment with and master the technology in a controlled environment, ensuring its reliability and effectiveness before deploying it on a large scale.

Geoffroy de Ridder Head of Technology and Operations Lombard Odier Group

Scan here to get in touch.

Deploying chatbot technology at Lombard Odier

What can this chatbot do?

Our chatbot, primarily aimed at bankers and assistant bankers, is custom-designed and integrated on our servers. It gives employees rapid access to complex and specialised information, and enables them to navigate the rules and procedures for forming new banking relationships and managing the client lifecycle thereafter. This approach improves the efficiency of the process and ultimately speeds it up.

What challenges have you overcome?

It is essential to ensure that chatbots do not produce misleading or incorrect information. Our tool needs to provide clear answers in order to build user confidence. In addition, we have had to deal with teething problems such as ‘hallucinations’ and incomplete responses. Our tool therefore also includes mechanisms to limit the generation of erroneous data.

Why did you opt for in-house development?

By designing the open-source LLM solution in-house, we were able to fully customise it to meet the needs of our core businesses. The tool was designed to understand the nuances of our industry, the specialist terminology and our internal processes. This customisation ensures greater accuracy and relevance in the answers provided, leading to better adoption and an optimised user experience.

What are your plans for the future development and expansion of the tool?

Although this tool is optimised for client lifecycle management, its modular design allows for future expansion, and we have already identified other potential uses for it. As an example, we plan to develop it to cover internal directives and policies, articles and content from our intranet, and our current legal contracts. We are convinced that exciting new use cases, particularly through interactions between multiple chatbots, will soon be a reality.

The role of AI in behavioural analytics: transforming wealth management for the mass affluent 2

By Sahba Hadipour, Founder & CEO, Figg

The wealth management industry is undergoing a profound transformation driven by Artificial Intelligence (AI). While traditional wealth managers have long focused on high-net-worth individuals (HNWIs), the mass affluent segment remains largely underserved. AI-driven behavioural analytics offer a powerful solution, enabling wealth managers to better understand, engage with, and serve this demographic. By leveraging AI, firms can enhance customer acquisition, improve retention rates, drive higher return on investment (ROI), and maintain compliance within regulatory frameworks.

Today, AI’s ability to process vast amounts of data allows wealth managers to deliver personalised investment strategies at scale, bridging the gap between high-touch services for HNWIs and digital solutions for the broader market. The financial industry has recognised AI’s potential, with firms increasingly integrating machine learning models to uncover patterns in customer behaviour, predict financial needs, and automate processes for efficiency. As regulatory environments evolve, AI is also being utilised to ensure compliance and reduce risks associated with financial transactions.

Understanding behavioural analytics in wealth management

Behavioural analytics involves using AI to interpret financial behaviours, spending habits, and investment patterns. By analysing large volumes of client data, AI can predict future behaviours, personalise financial products, and optimise client interactions. AI-driven models assess not just historical transactions but also social, economic, and psychological factors influencing financial decisions.

For the mass affluent, whose financial behaviours often differ from those of HNWIs, AI-driven insights can help wealth managers craft tailored financial strategies. Unlike traditional portfolio management, which relies heavily on static client profiling, AI allows for dynamic, real-time adjustments based on evolving client needs and external market conditions. This continuous monitoring ensures that wealth managers can proactively address client concerns before they escalate.

The business case for AI in wealth management

1. Enhancing customer acquisition

Wealth managers struggle with cost-effective client acquisition, particularly in the mass affluent segment. AI enables predictive lead scoring, identifying highpotential prospects based on their financial activity, online behaviour, and demographic data. Automated marketing campaigns powered by AI further refine engagement strategies, ensuring wealth managers reach clients with relevant offerings at the right time.

AI-driven chatbots and virtual advisors also help with customer acquisition. They provide potential clients with instant information on financial products, increasing conversion rates. Moreover, AI can segment audiences more precisely, enabling firms to create highly targeted marketing campaigns that address the specific needs of different customer groups.

2. Improving client retention and engagement

Traditional retention strategies often fall short due to a lack of personalisation. AI-driven behavioural analytics can create hyper-personalised engagement strategies by monitoring changes in client behaviour. When a client shows early signs of financial stress or disengagement, AI can trigger proactive interventions, such as customised investment recommendations or personalised financial coaching.

AI enhances engagement by enabling wealth managers to provide real-time financial insights and automated alerts on market changes that may impact client portfolios. This level of responsiveness builds trust and encourages long-term relationships between clients and firms.

Use case: predictive retention strategies

A leading wealth management firm implemented an AI-driven client retention system that identified clients at risk of disengagement. By personalising outreach with tailored product offerings, they experienced a 30% improvement in retention rates over six months.

3. Driving higher ROI

AI optimises portfolio performance by continuously analysing risk factors, market trends, and individual client preferences. This enables wealth managers to maximise investment returns while minimising risks. Additionally, automating routine tasks reduces operational costs, allowing firms to reallocate resources to more strategic client-facing activities.

Furthermore, AI provides greater transparency by offering clients detailed reports on investment performance, including risk assessments and potential growth opportunities. By using AI-driven robo-advisors, firms can offer clients more accurate, data-backed recommendations, increasing investor confidence.

Use case: AI-powered investment optimisation

A mid-sized wealth management firm deployed AIdriven portfolio management tools that adjusted asset allocations based on real-time market conditions. Over a one-year period, clients experienced an average portfolio return increase of 12%, compared to 7% for traditional models.

4. Ensuring compliance within regulatory parameters

Regulatory compliance presents a significant challenge in wealth management, given the stringent requirements for transparency and suitability. AI can enhance compliance by monitoring transactions in real-time, identifying potential risks, and ensuring adherence to regulations. Tools driven by AI also generate comprehensive audit trails, minimising the risk of regulatory breaches and fostering trust between clients and institutions.

AI-powered systems assist firms in maintaining compliance by automating Know Your Customer (KYC) and Anti-Money Laundering (AML) processes, which reduces human error and enhances detection rates. Regulatory authorities are also starting to adopt AI for oversight, indicating that firms which proactively implement AI-driven compliance solutions are likely to enjoy smoother regulatory interactions.

Implementation strategies for wealth managers

• Data integration: Aggregating structured and unstructured data from multiple sources to create a holistic client profile.

• AI-driven insights: Deploying machine learning models to identify patterns and predict future client behaviours.

• Automation of client interactions: Employing AIpowered chatbots and robo-advisors to enhance client engagement, while ensuring human oversight for complex advisory requirements.

• Continuous monitoring and optimisation: Implementing AI systems that evolve based on real-time data to refine engagement and investment strategies.

Conclusion

The integration of AI-driven behavioural analytics signifies a paradigm shift for wealth managers, especially in meeting the needs of the mass affluent. By enhancing customer acquisition, improving retention, driving ROI, and ensuring compliance, AI enables wealth managers to provide more personalised, efficient, and effective financial services. As AI technology continues to develop, its role in wealth management will only become more essential – and impactful.

Sahba Hadipour Founder & CEO Figg

Can the world’s largest and fastest growing wealth pools embrace AI? 3

By Phil Watson, CIO, Betteredge Capital, and CEO, Lightbox Wealth

Family offices are experiencing substantial growth. By 2030, the number of single-family offices worldwide is projected to grow a third - reaching 10,720. At the same time, those family offices will manage a total of US$5.4 trillion in assets - an increase of 75% (Deloitte). Both highlight the rising complexity of wealth management within these organisations. With the rising client and / or AUM loads, operational efficiency has become a focal point. With family offices spending up to a fifth of their time on administrative tasks and compliance, it is unsurprising that 40% of family offices plan to hire additional staff. However, maintaining cost discipline remains paramount and AI has emerged as a solution.

During recent years, there has been a growing institutionalisation of family offices – taking a more structured and professionalised approach, similar to institutional investors. Many have adopted sophisticated risk management frameworks, invested in technology-driven portfolio analytics, and hired experienced talent. According to the UBS Global Family Office Report 2024, 44% of family offices have implemented a governance framework, an equal percentage have documented investment processes, and 56% have an investment committee. As this process continues, AI is expected to play an ever more important role.

Away from governance, AI has become a chosen destination for family office investments. Awareness of the opportunities in AI has grown supported by the fact that AI has overtaken HealthTech, food security, and other typical themes the as top-ranked investment theme among family offices. In the recent UBS survey, nearly 80% of respondents in the UBS survey indicated their intention to invest in AI within the next two years.

Altogether, this transformation is also occurring against the backdrop of an intergenerational wealth transfer. By 2045, an estimated US$84 trillion will change hands in the US alone, with a substantial portion transitioning before 2030. As wealth moves from one generation to the next, family offices must adapt to new investment philosophies, risk appetites, and technological expectations.

Despite its promise, AI adoption in family offices is not without challenges. The motivations of family offices vary significantly.

Horses for AI courses

Despite its promise, AI adoption in family offices is not without challenges. The motivations of family offices vary significantly. Many are investment-led, prioritising wealth preservation and growth, while others focus on legacy planning, philanthropy, tax efficiency, or lifestyle management. These differences or relative priorities shape their approach to AI adoption.

Geographical boundaries further influence AI integration. Regulatory environments vary widely, dictating data usage, privacy protections, and compliance obligations. Family offices, typically small teams of fewer than ten members, often rely on personal relationships rather than technological infrastructure. This human-centric approach can slow the adoption of digital tools, let alone advanced AI capabilities.

And although family offices do not have the extensive legacy architecture of large banks, those that do have more advanced technology frequently operate with a fragmented technology stack. Many use multiple systems – from accounting, portfolio management, and reporting, resulting in inefficiencies and siloed data. Moreover, concerns over data privacy and reputational risks make AI adoption a delicate balancing act.

Cost considerations add another layer of complexity. With staffing and asset management costs comprising over 80% of expenses, family offices must weigh the benefits of AI against its implementation costs. While AI is often touted as a juggernaut of efficiency, its societal implications and governance challenges cannot be ignored. The rapid pace of AI innovation must be met with responsible oversight, ensuring that progress is not only swift but also secure and ethical.

Key question: how can AI address these complexities and enhance the efficiency of family offices?

The prevailing wisdom in the AI consulting world has been that family offices should create expansive ‘data lakes’ – centralised repositories where all possible data sources are ingested, waiting to be processed by AI. The problem? A data lake full of unstructured, unreliable, and unverified information is actually just a ‘data swamp’.

A typical family office will accumulate a wide variety of documents pertaining to their client(s), markets, products, office documents, contractual and regulatory obligations and if, however, these documents are simply thrown into a database without a system for standardisation, tagging, and validation, AI models trained on them will produce inconsistent and potentially misleading results. Without rigorous structuring, an AI model might treat a 2018 venture fund report with the same weight as a 2025 real estate projection – without adjusting for time, context, or reliability.

The other issue is duplication and misinformation. A large data lake inevitably includes redundant or conflicting data points – multiple valuations for the same asset, different assumptions across fund reports, overlapping exposure in alternative investments. If AI is working off raw, unfiltered inputs, the insights will be noisy at best, and catastrophically misleading at worst.

A data lake isn’t a strategy. Structured, reliable, and high-quality data is. And for family offices looking to harness AI effectively, that distinction is everything.

The real key: structured, decision-useful data

Instead of chasing volume, family offices should be laser-focused on data quality, structure, and provenance. That means:

1. Creating a standardised data taxonomy

Every data point needs a clear definition, timestamp, and source attribution.

2. Enforcing data validation and cleansing

Before feeding data into AI systems, it needs to be verified, deduplicated, and reconciled. This is especially critical for alternative assets, where valuations can fluctuate based on methodology.

3. Prioritising high-fidelity sources over volume

More data isn’t always better. A handful of high-quality, trusted data sources will always outperform an ocean of unverified information.

4. Integrating contextual and qualitative data

AI models struggle with non-quantitative factors – yet in family offices, these are often the most critical. AI should be designed to ingest and interpret qualitative data, whether from structured interviews, sentiment analysis, or expert annotations.

5. Building a feedback loop for human oversight

AI is not a replacement for human judgment – it’s a tool to enhance it. Every AI-driven insight should be auditable and explainable, with clear pathways for human analysts to validate, challenge, and refine recommendations. The best AI implementations don’t just automate decisions; they augment the expertise of investment teams by surfacing insights they might have overlooked.

AI is not a replacement for human judgement –it’s a tool to enhance it.

Solution: AI as a catalyst for operational efficiency

Family offices are increasingly leaning towards buying rather than building solutions. The proliferation of asset and liability aggregators (consolidators) is a testament to this trend, as family offices seek streamlined access to financial data across multiple custodians.

For family offices that get this right, AI can be a powerful differentiator. Structured, reliable data ensures AI is poised to enhance several key aspects of family office operations, for example:

• Data aggregation and insights

• Deal flow and investment research

• Governance and compliance automation

• Cybersecurity and fraud detection

• Tax optimisation and estate planning

• Cost management and operational streamlining

Beyond operational enhancements, AI is becoming a key investment focus for family offices, with family offices increasingly viewing AI as both a tool and an investment.

Challenges and considerations in AI adoption

Despite its advantages, AI adoption is not without hurdles. A critical factor is data integrity. AI models are only as effective as the data they process. Without structured, high-quality, and validated data, AI can generate misleading insights rather than actionable intelligence.

The governance of AI is another pressing concern. Ethical AI deployment, data security, and compliance with evolving regulations require careful consideration. AI literacy among family office executives and staff is equally important. Without the necessary expertise, AI implementations may fall short of their potential.

Finally, cybersecurity is another area where AI plays a dual role. While AI enhances security protocols, it also introduces new vulnerabilities. AI-powered attacks are an emerging threat, making robust security frameworks a prerequisite for AI adoption.

Conclusion: AI as an enabler, not a replacement

Family offices are uniquely positioned to benefit from AI, but successful implementation requires a structured approach. AI adoption must be aligned with a clear strategy, prioritising structured and decisionuseful data. Without this foundation, AI becomes a liability rather than an asset.

As the investment community continues to explore AI's potential, family offices must strike a balance between innovation and governance. AI’s value is not in replacing human insight but in empowering decision-makers with better tools, deeper intelligence, and enhanced security.

AI is everywhere, and it will transform everything. For family offices, the question is not whether to adopt AI but how to do so in a manner that is intelligent, secure, and sustainable. Steering clear of jargon and approaching it pragmatically would seem a sensible starting point.

Phil Watson

CIO, Betteredge Capital CEO, Lightbox Wealth

The state of AI adoption in wealth management – challenges, opportunities, and a roadmap to success 4

By Vincenzo Chiochia, Head of AI & Data, Banking Practice, Deloitte

A recent survey reveals that 69% of banking executives anticipate AI to significantly impact their organisations, with 42% predicting a reduced reliance on personal investment advisers. However, this sentiment varies based on client demographics and geographical trends, particularly with private banks engaging with younger, tech-savvy wealthy individuals.

What is clear today is that the use of Artificial Intelligence (AI) in the financial market is increasing –and the increasing pace of adoption of AI brings with it both opportunities and risks.

Indeed, regulators expect supervised institutions that use AI to actively consider the impact of this use on their risk profile and to align their governance, risk management and control systems accordingly. Besides the size, complexity, structure and risk profile of the supervised institutions, the materiality of the AI applications used and the probability that the risks resulting from the use of these applications will materialise must be taken into account (FINMA).

In this article, we outline some of the key points related to AI adoption today, touch on some of the challenges that need to be overcome to continue on our collective journey of industry transformation to leverage AI and fully realise the potential it brings.

Key trends in AI adoption

The adoption of AI in client engagement is increasingly shifting towards hybrid models that combine AI-driven tools with traditional personal interactions. This trend reflects a growing recognition of AI's potential to enhance relationship management without replacing the human touch. Over the next three years, 60% of relationship managers anticipate leveraging AI tools to improve client service, while 67% of firms plan to integrate digital solutions alongside face-to-face meetings. This dual approach ensures that AI supplements human expertise, offering a seamless blend of efficiency and personalisation in client interactions.

In these hybrid models, AI operates largely behind the scenes, empowering relationship managers with actionable insights and streamlined workflows. By automating preparatory tasks, such as analysing client portfolios or tracking follow-ups, AI allows managers to focus on deeper, more meaningful engagement during meetings. Additionally, AI-driven tools can generate tailored insights, enabling firms to offer personalised advice that resonates with clients' unique needs and goals. As a result, the hybrid model is poised to redefine client engagement, combining the analytical power of AI with the relational strength of human interaction.

The adoption of AI in client engagement is increasingly shifting towards hybrid models that combine AI-driven tools with traditional personal interactions.

The rising demand for digital experiences

Investors are increasingly driving demand for advanced digital experiences, urging banks to prioritise innovative solutions that cater to their evolving expectations. A significant 68% of investors express a strong preference for banks that emphasise digital tools and platforms, highlighting a clear shift in client priorities. Among the technologies shaping this transformation, AI and cloud computing stand out as top investment areas, owing to their synergistic capabilities. Together, these technologies enable seamless data management, personalised client interactions, and scalable solutions that redefine how financial services are delivered.

Generational dynamics further amplify this trend, particularly in regions like APAC, where younger wealthy individuals are reshaping the landscape of private banking. For Gen Y and Z investors, digitalfirst banking experiences are not just a preference but a baseline expectation. This demographic envisions a future with minimal reliance on traditional advisory services, with 60% predicting a significant shift by 2030. To stay competitive, financial institutions must adapt, embracing transformative technologies and strategies that resonate with this tech-savvy generation’s demands for convenience, speed, and personalisation.

Opportunities for AI application across the bank value chain

AI offers transformative opportunities across multiple dimensions of banking:

• Efficiency and cost optimisation

• Enhanced customer experience

• Risk and compliance

• Support functions

The potential impact of AI on different demographic segments

While different demographic segments have slightly differing expectations on the potential benefits and positive impact of AI, the underlying view is clear: AI has huge potential to transform wealth management (and other industries). For example:

Bank executives

• 69% of bank executives believe AI will significantly change their firm’s work.

• 55% of executives say born-digital firms will transform the wealth industry, and 42% think that personal investment advisers will become less necessary.

Investors

• 68% of investors want their bank to offer digital experiences on par with those from leading borndigital companies.

• In the next three years, AI will be the top tech investment for banks (58%), followed by cloud (44%), and data analytics and collaboration tools (42%).

Relationship managers / investment advisers

• 60% of advisers expect to use AI tools to serve clients over the next three years.

• 67% of firms will move to a hybrid model where digital and AI-enabled tools will augment the RM in direct interactions with clients.

Clients

• 60% of Gen Y and Z do not expect to use advisers due to advances in technology (e.g. AI) by 2030.

Challenges in AI adoption

Despite its significant transformative potential, AI adoption in banking is not without hurdles. As mentioned in a recent FINMA report (specific to the Swiss market, but the following observations can be applied in a broader geographic context): "The risks from the use of AI are mainly in the area of operational risks, in particular model risks (e.g. lack of robustness, correctness, bias and explainability) as well as IT and cyber risks. They also result from a growing dependence on third parties such as providers of hardware solutions, models or cloud services in an increasingly concentrated market. Finally, there are legal and reputational risks as well as challenges in the allocation of responsibilities due to the autonomous and difficult-to-explain actions of these systems and scattered responsibilities for AI applications at supervised institutions" (FINMA).

So, how should wealth managers and financial institutions best go about addressing these risks to overcome the many challenges in pursuing AI adoption? The following points form a non-exhaustive list of areas firms should consider addressing to stay on track as they embark on their AI journey:

Data privacy and security

Banks must address concerns about hosting sensitive client data in the cloud. While some institutions adopt on-premise solutions, others explore hybrid models to strike a balance.

Workforce adaptation

Employees may perceive AI as a threat. Effective change management and training programmes are essential to position AI as a tool that enhances, rather than replaces, their roles.

Regulatory risks

Banks must establish robust governance frameworks to manage AI-related risks, including model reliability, data security, and compliance.

What are the key building blocks for AI success?

To harness AI’s potential while managing risks, banks should focus on eight critical areas:

Operating model

Define roles, including the Chief AI Office, and clarify governance structures.

Value realisation

Track AI initiatives using clear KPIs to measure impact and prioritise resources effectively.

Use case delivery

Assemble skilled teams to implement AI projects.

Platform development

Choose between cloud-based or onpremise solutions, ensuring data security and scalability.

Ecosystem partnerships

Collaborate with FinTechs, tech partners, and other external organisations.

Learning and development

Consider equipping employees with skills including data science, machine learning, and prompt engineering.

Operational excellence

Ensure seamless integration of AI tools into existing workflows.

Change management

Promote AI as an enabler and provide a clear vision for its integration into the workforce.

Strategic steps to accelerate AI adoption

Pragmatically, firms should focus on the following key areas to ensure successful and timely adoption of their AI initiatives:

• Secure leadership support

Gain C-level buy-in to prioritise AI as a strategic advantage.

• Prioritise investments

Identify key focus areas – be it back-office efficiency or client-facing innovation – and allocate resources accordingly.

• Establish governance

Implement a minimum viable governance model to manage risks and ensure regulatory compliance.

• Scale thoughtfully

Start with pilots, refine use cases, and scale successful initiatives while monitoring their impact.

Conclusion:

The future of AI in banking

AI is set to be a defining factor in banking competitiveness, shaping client preferences and employee choices. By addressing challenges and adopting a structured approach, banks can unlock AI’s full potential. The road may be complex, but with the right strategy, AI will transform the banking landscape, offering unparalleled opportunities for growth and innovation.

The successful adoption of AI in wealth management is not a sprint – it’s a marathon with an ever shifting finish line! To be successful, and to stay in the race, firms need to adopt a holistic view to tackle the numerous categories or risk associated with effective AI implementation, and stay consistently stay one step ahead of the competition.

AI is set to be a defining factor in banking competitiveness, shaping client preferences and employee choices. By addressing challenges and adopting a structured approach, banks can unlock AI’s full potential.

Vincenzo Chiochia Head of AI & Data, Banking Practice Deloitte

Five practical steps to help increase organic growth: transforming advisory firms for the next generation 5

By Patrick Hannon, VP of SaaS Commercialisation, Fidelity Labs

The wealth management industry is poised for unprecedented opportunity. Cerulli updated its forecast for assets transferred in the next twenty years from $84 trillion to US$124 trillion through 2048. It projects that US$105 trillion will be passed to heirs, with US$18 trillion going to charity. While US$21 trillion in mass affluent assets remains largely unserved by traditional advisory firms, the industry clings to service models designed for high-net-worth clients. Meanwhile, artificial intelligence promises to revolutionise adviser capacity just as next-generation clients demand a fundamentally different wealth management experience.

The transformation of wealth management comes as many firms struggle with the basics of profitable growth. Fidelity's 2024 RIA Benchmarking Study reveals that even growing firms are grappling with compressed margins and rising expenses. Yet some firms have discovered how to thrive in this changing landscape, achieving superior growth while maintaining stronger profitability.

Transform raw data into actionable intelligence

Today, the traditional advisory firm still operates with surprisingly high levels of inefficiency. Advisers spend up to 70% of their time on manual back-office tasks, leaving limited capacity for client engagement and strategic planning that drives value. This administrative burden can make effectively serving the mass affluent market impossible – the economics may simply not work.

Leading firms are revolutionising this equation through intelligent automation and data integration. Rather than viewing AI as a threat, they are using it to help transform their operational model. The impact appears in their numbers – high-performing firms maintain overall expenses at just 10.5% of revenue, while other firms spend nearly double that amount, at 18.5%

This efficiency isn't about cost-cutting, however. It is about a fundamental transformation of advisory firms' operations. By automating routine tasks and integrating systems effectively, these firms can create capacity for advisers to serve more clients without sacrificing service quality. The result? Higher revenue yields (68 basis points versus 64) and dramatically better operating margins (26% versus 15%).

Understanding growth metrics that matter

The mass affluent market represents both enormous opportunity and serious challenge. Traditional client acquisition approaches, built around high-touch prospecting and relationship building, may simply not scale to serve this market. Yet this segment, representing one-third of U.S. households, demands and deserves quality financial advice.

Leading firms are rethinking growth from the ground up. Rather than scaling traditional prospecting methods, they're building systematic approaches to client acquisition and service. The data validates this strategy: high–performing firms see similar or fewer prospects on average (66 versus 67), but convert them at higher rates (73% versus 68%).

More importantly, these firms generate substantially more referrals – 29 new clients from referrals compared to 20 for other firms. This efficiency in client acquisition becomes crucial when serving the mass affluent market, where client acquisition costs must be carefully managed.

Rather than viewing AI as a threat, leading firms are using it to transform their operational model.

Addressing top client concerns

The next generation of clients brings fundamentally different expectations. They demand transparency in fees, clarity in service offerings, and efficiency in delivery. Most firms attempt to meet these demands by simply adding services, often without corresponding fee increases. The result? Compressed margins and unsustainable service models.

High-performing firms take a different approach. They maintain clear service boundaries and appropriate pricing while using technology to deliver services efficiently. Where 18.5% of traditional firms bundle ten or more services under a single fee, only 10.5% of high-performing firms follow this path. Instead, they focus on delivering core services exceptionally well, using automation to maintain profitability while ensuring quality.

Supporting ageing clients with technology

The industry faces a stark reality: 75% of RIAs don't offer communication beyond e-mail, even though clients increasingly demand seamless digital experiences. This gap between service delivery and client expectations can threaten both current relationships and next-generation transfers.

Leading firms are bridging this gap through strategic technology deployment. Rather than viewing digital transformation as a cost centre, they see it as key to scaling quality advice. They spend less on technology overall but achieve better results through focused deployment on capabilities that matter to clients.

Next-generation engagement strategy

The most significant transfer of wealth in history coincides with the greatest transformation in client expectations. Millennials and Gen Z don't want their parents' wealth manager – they expect tech-first platforms, transparent fees, and seamless digital experiences. Most firms remain woefully unprepared for this shift.

High-performing firms are building for this future today. They're creating service models that naturally accommodate client preferences while maintaining operational efficiency. This approach helps explain their superior growth rates – these firms typically grow faster (13.1% three-year AUM CAGR versus 8.9%) while maintaining higher revenue yields.

The path forward requires fundamental transformation. Firms must leverage AI to dramatically increase adviser capacity while building scalable service models that profitably serve the mass affluent market. They should embrace digital transformation not as a cost to be minimised, but as the key to future growth.

The rewards for making this transition can be substantial. Firms that successfully evolve their approach can capture this opportunity while maintaining profitability. Those that cling to traditional models risk becoming increasingly irrelevant in a rapidly changing market.

The choice facing advisory firms isn't whether to make this transition but how quickly they can execute it. The technology exists, and the market opportunity is clear. The only question is which firms will lead this transformation and which will be left behind.

Patrick Hannon VP, SaaS Commercialisation Fidelity Labs

AI Toolkit 2025 Roadshow / London

20TH MAY 2025

Hosted at the Pan Pacific Hotel, in the heart of the City of London, the London edition of the AI Toolkit Roadshow 2025 from The Wealth Mosaic (TWM) is the third event after editions in Zurich and New York. Focused on the role of Artificial Intelligence (AI) in the UK, and wider wealth management space, the event will run from 8:30 AM to 12:00 PM and mix short presentations, vendor demos and an industry panel discussion.

Don't miss the opportunity to hear several AI-focused technology demos and stay ahead in the industry on all things AI!

Interested in participating in the roadshow?

Contact us for more info and sponsorship opportunities. Register Now

Disclaimer

This event is free for wealth managers to attend, although registration is required, while there are a limited number of paid tickets available to technology vendors, consultants and others. Access to the event is limited and is available on a first-come, first-served basis. All registrations are subject to approval by the event organiser.

AI in ACTION

Showcasing the application of AI in wealth management HIGHLIGHTS

EVENT INSIGHTS

AI Toolkit Roadshow 24/25

'Live and Unplugged'

Highlights from discussions held at our AI Toolkit Roadshow events, hosted in Zürich in late 2024, and in New York in January 2025.

To coincide with the publication of each of our Toolkit Reports and to continue the debate on the theme each report addresses, we bring together industry participants –wealth managers, consultants, and vendors alike – in various locations at our Toolkit Roadshows. These are typically half day, in person events, where attendees discuss key themes relating to the topic in question, in this case: Artificial Intelligence.

This feature highlights some of the main points that emerged from the discussions sessions held in Zürich and New York, respectively.

AI's impact on the wealth management industry

AI is revolutionising wealth management by enhancing decision-making, improving efficiency, and personalising client experiences. AI-powered algorithms analyse vast amounts of financial data, market trends, and client profiles to provide actionable insights and optimise investment strategies.

Additionally, AI has the potential to streamline administrative tasks, such as compliance and reporting, allowing financial advisers to focus more on strategic planning and client relationships.

The adoption of AI in client engagement is increasingly shifting towards hybrid models that combine AI-driven tools with traditional personal interactions. This trend reflects a growing recognition of AI's potential to enhance relationship management without replacing the human touch. Over the next three years, 60% of relationship managers anticipate leveraging AI tools to improve client service, while 67% of firms plan to integrate digital solutions alongside face-to-face meetings. This dual approach ensures that AI supplements human expertise, offering a seamless blend of efficiency and personalisation in client interactions.

The adoption of AI in the banking sector is being driven in large part by heightened client expectations. To stay relevant, banks must leverage AI to create more seamless and more personalised customer experience, streamline operations, and attract top talent. This requires not only technological advancements but also new operating models to drive innovation in order to meet evolving consumer needs. However, the journey toward AI integration is still in its early stages for most banks, with many institutions remaining in the exploration phase. To unlock the full potential of AI, banks must define clear strategies and embrace cultural transformations, particularly to harness the benefits of Generative AI (GenAI).

Panel Summaries

AI – an industry perspective: commentary from Zürich

Participants at our Zürich event discussed how successful AI integration requires a deliberate and phased approach. Banks can mitigate risks by starting with low-stakes projects, learning from initial deployments, and scaling up gradually while maintaining strong governance and risk management frameworks. In order to be able to scale with AI, having strong data foundation, continuous training and enablement at all levels in the organisation as well as cross-functional collaboration remain critical. Leadership’s understanding of what path to success looks like and support along the journey are essential to fostering an environment that embraces innovation and transformation. By addressing these challenges and creating a culture that fosters innovation and agility, banks can position themselves to fully realise the advantages of AI technologies.

The conversation concluded by shifting to take a look at the future of wealth management, where AI is poised to play a transformative role. Discussions focused on the evolving responsibilities of relationship managers (RMs) in a landscape increasingly influenced by Gen-AI. While AI can enhance client interactions and streamline service delivery, the strong believe remained, namely that maintaining a human touch was key. A hybrid approach that combines AI technology with human expertise is seen as the ideal model for the future.

In closing, panellists reflected on the rapid evolution of AI technologies and their profound implications for the banking sector. Governance and cultural transformation emerged as recurring themes, with audience questions focusing on these critical areas. Panellists also discussed strategies for addressing the challenges of adopting new technologies while maximising their potential benefits.

AI – an industry perspective: commentary from New York

As in Zürich, we heard in New York that personalisation is at the forefront of AI’s potential application. Event participants discussed where firms should focus AI efforts. Initially, AI was used for summarisation, search and retrieval, and translation. Now, the focus has shifted toward demonstrating AI's return on investment (ROI).

Panellists discussed generational differences in AI adoption. Older clients tend to engage with the same information multiple times, valuing replayability and the ability to reference data later. Younger clients prioritise efficiency, preferring high-impact, three-minute summaries over lengthy interactions. The generational shift in content consumption underscores the importance of AI-driven solutions that cater to varied user preferences.

Other important topics considered included whether smaller firms have an advantage in AI adoption, being able to move more quickly, perhaps, or requiring smaller levels of initial investment to pilot AI initiatives?

A key theme throughout the discussion was the importance of data architecture. AI is not a magic solution – it requires structured, high-quality data to function effectively.

Again, questions arose about the role of human advisers. While AI can provide recommendations, the panel emphasised that financial advice remains a highly regulated, human-centred service.

Finally, the panel considered the long-terms viability of AI providers. Firms investing in AI are increasingly scrutinising the long-term viability of their AI vendors. Due diligence now includes evaluating whether vendors have sustainable business models and financial backing to ensure continued service.

In conclusion, it is clear that AI is fundamentally reshaping wealth management, from enhancing adviser efficiency to transforming client engagement. While challenges remain, the opportunities for AIdriven innovation are vast. Again, it was underlined that firms that strategically integrate AI while maintaining a human touch will be best positioned to thrive in the evolving landscape of financial services.

AI TOOLKIT SHOWCASES

Eleven individual Showcases relevant to the application of Artificial Intelligence (AI) in the wealth management industry in 2025.

Showcase #1 Adviser Efficiency

In today’s fast-paced financial environment, wealth managers and advisers are increasingly time-poor. This is where AI, is starting to show its value.

Contributed by aveni

Showcase #2 Future Foundation

To unlock AI's full potential, wealth managers must first address a foundational requirement: data.

Contributed by Point

Showcase #3 Generative AI

GenAI gives financial firms a powerful way to process and respond to unstructured and structured data, driving efficiencies and growth.

Contributed by InvestCloud

Showcase #4 AI-powered Video

Leverage personalised AIpowered videos to increase the value and frequency of client engagement, and acquire incremental AUM through new multigenerational clients.

Contributed by Storyline AI

Showcase #5 Portfolio Intelligence

AI is not changing the principles of sound wealth management –but it is redefining what is possible.

Contributed by Pebble

Showcase #6 Portfolio Management

Investment portfolio optimisation has long relied on historical data analysis to model uncertainty and guide decisions. These traditional methods are being disrupted by AI.

Contributed by Raise Partner

Showcase #7 AI Agents

Investment teams are rethinking their technology strategies to stay competitive and future-proof their processes. Traditional operating models are being replaced by new approaches that harness AI.

Contributed by Jacobi

Showcase #8 Intelligent Workflows

AI is unlocking growth opportunities through intelligent workflows.

Contributed by Zeplyn

Showcase #9 Future Proof

Today, thanks to major advancements in Big Data, chatbots and deep learning, AI is no longer speculative; it’s essential.

Contributed by Objectway

Showcase #10 GenAI Portfolio Solutions

Wealth managers operate in an increasingly complex landscape where clients expect tailored, datadriven investment insights. This is where Gen-AI portfolio advisory solutions come into play.

Contributed by GPTadvisor

Showcase #11 AI For The Next Gen

AI’s successful implementation hinges on robust cloud infrastructure.

Contributed by AWS

EFFICIENCY ADVISER SHOWCASE #1

AI - starting to show real, measurable business value

In today’s fast-paced financial environment, wealth managers and advisers are increasingly time-poor. This is really where technology, and in particular AI, is starting to show its value – helping those who are beginning to adopt it to recognise real gains.

Contributed

by

Financial advisers are often juggling multiple clients, each with unique needs and requirements. Having time to focus on those individuals is their most valuable commodity, and AI can be a game-changer in this context.

ABOUT AVENI

Aveni is an award-winning FinTech company, dedicated to enhancing human expertise and efficiency through advanced AI technology.

Adviser Efficiency

AI solutions to help advisers thrive and bring efficiency alive 1

By Joseph Twigg, CEO at Aveni

In today’s fast-paced financial environment, wealth managers and advisers are increasingly time-poor. This is really where technology, and in particular AI, is starting to show its value – helping those who are beginning to adopt it to recognise real operational gains, streamlining administrative processes, improving efficiency, and ultimately enabling a more personalised experience for clients in a few very specific areas.

The balance between client relationships, market analysis, portfolio management and increased compliance requirements all add to the increasing list of administrative requirements on today’s adviser –detracting from the most valuable part of their work – human interaction. Despite the roll out of online platforms, digital client portfolios and trackers, many of the administrative elements are still fairly rudimentary – with manual note taking, phone calls and checking back through different records and communications for each client.

This is really where technology, and in particular AI, is starting to show its value – helping those who are beginning to adopt it to recognise real operational gains, streamlining administrative processes, improving efficiency, and ultimately enabling a more personalised experience for clients in a few very specific areas.

AI is the ultimate note taker

Financial advisers are often juggling multiple clients, each with unique needs and requirements. Having time to focus on those individuals is their most valuable commodity, and AI can be a game-changer when it comes to optimising how that time is spent. For instance, AI-powered tools can automate the process of note-taking and data entry, significantly reducing the time advisers spend on administrative tasks. Natural Language Processing (NLP) tools, specifically designed for financial services, allow AI to convert voice or written notes into structured data, which is then automatically uploaded to a client’s file. This removes the need for advisers to manually transcribe notes or update client records, making it easier for them to keep track of important details in real time.

With AI-powered CRM systems and note-taking tools, advisers can automate much of this documentation. Tools like Aveni Assist, for instance, automatically capture and summarise meetings, turning conversations into structured notes and actionable items. AI systems also set reminders and generate action points, making it easy to stay organised without spending hours on manual entries.

A very effective second set of eyes and ears

One of the biggest worries for advisers in client meetings is the possibility of missing something crucial, whether it’s a vulnerability indicator, a new financial risk or a subtle clue about a client’s needs. This is also particularly important with increased regulation such as the FCA’s Consumer Duty, in the UK, where vulnerability identification, full assessment and proven actions are a crucial area of consideration.

For those who want to be fully present with clients while still catching every important detail, this can create a real challenge for wealth managers and advisers. There’s also the worry that AI might eventually replace human judgement in these scenarios — but what if it could simply act as a powerful support?

AI tools such as Aveni Detect can serve as a “second set of eyes” in client meetings, transcribing conversations in real time, flagging essential discussion points and identifying potential risks based on the conversation and the client’s profile. It acts as a ‘machine line of defence’ enabling AI-driven quality assurance automation, at all stages of the QA workflow. This way, advisers can focus entirely on building rapport, knowing that AI is picking up any red flags or actionable points they may otherwise miss.

AI’s ability to detect potential risks isn’t limited to immediate concerns, either. By analysing current market trends, economic factors and even client spending habits, AI can identify risks that may not yet be visible. This then allows advisers to proactively guide their clients’ financial plans with greater security.

Think about it. What if AI tracked every key point while you build trust and connection with your clients? With AI covering the details, advisers can relax, confident that no crucial insights or risks will slip through the cracks. This way, they can deliver a highly personal experience, knowing that technology is there to support – not replace – their role.

Trust in the human-machine

combination

One of the key concerns for clients is the security of their personal and financial information. Advisers must adhere to strict security and regulatory protocols to protect sensitive data, but AI can make this process more efficient.

AI systems can monitor account activity in real-time and flag any suspicious behaviour, providing an extra layer of security against potential fraud or cyberattacks. This is particularly critical for larger clients who may be targeted by sophisticated cybercriminals. AI's ability to detect anomalies and prevent breaches ensures that clients' data is kept secure, which builds trust and confidence in the adviser’s services.

However, there is scepticism in the marketplace about AI, and that is unsurprising, perhaps, with a technology that seems to promise such a paradigm shift in working models and life more broadly. And, as often is the case with new technologies, when issues arise these are often highly publicised, which while encouraging caution, can also bring trepidation. This is where it is crucial to consider the solutions that are being adopted. Transparency about data collection, an understanding of the right guard rails being deployed by not only the technology provider, but also in how it is used, is going to be essential and that must be top of mind when selecting the right AI solutions.

What if AI tracked every key point while you build trust and connection with your clients?

Peace of mind on compliance with a bespoke AI approach

AI can also play a crucial role in ensuring that advisers are compliant with regulatory standards and that their advice meets the highest quality assurance standards. Compliance can be an arduous task, and often is conducted manually listening to hours of recordings or reading pages and pages of notes and crossreferencing facts on multiple documents. Not only is this hugely time-consuming, but it also means that a far smaller percentage of calls or interactions typically are able to be assessed – exposing both advisers and their customers to an unnecessary level of risk.

AI tools can scan client files to ensure that all required documentation is present, flagging any missing or incomplete information. These systems can also monitor ongoing transactions and alert advisers to any potential compliance issues. By automating these tasks, rather than relying on manual checks means that a far larger proportion of advisers can focus more on providing valuable advice to their clients, knowing that compliance is being managed effectively in the background. With great speed around these preexecution checks also means that advisers can free up more advisory capacity, to bring in more clients and generate more revenue for their firms.

However, this is an area where bespoke models of AI, specifically trained in financial services, and equipped to better assess relevant compliance-based language can make a significant impact. A generic AI tool, whilst offering some efficiencies, does not offer as comprehensive a compliance assurance and advisers and firms should be cognisant of that from the beginning.

Enhancing the personal touch

Financial advisers know that clients expect quick answers. Yet, delivering those without sacrificing quality can feel like a balancing act. Though many clients ask similar questions, each situation requires an individual unique response. Just because advisers handle routine queries, it shouldn’t mean less time for clients needing in-depth support. A recent survey showed that 39% of advisers find managing client demands challenging, while 37% feel pressured to create a more differentiated client experience. AI can help with this challenge. By handling common questions quickly and accurately, AI frees advisers to focus on the unique, complex cases that need personalisation. Such tools can also pull up relevant client data and past interactions in seconds, making it easy to answer questions without endless back-andforth or research time.

The right type of AI tools also allows for highly personalised insights. With customised feedback and recommendations based on the client’s unique financial situation, goals, and preferences. The ability to tailor advice to each individual can make the service feel more relevant and personalised, which is key to building long-term relationships.

Reaching out to the next client generation

Financial advisers are increasingly finding that they must appeal to a broader mix of clients, including younger generations and those who may not have traditionally engaged with financial advisory services. AI presents a unique opportunity to tap into this market.

Adopting AI tools reflects a forward-thinking, modern approach to financial advisory and wealth management. This is an appealing factor for younger clients or those with investment aspirations who are looking for innovation in the services they receive. Clients expect a seamless, tech-driven experience, and AI can help financial advisers meet these expectations by providing a more efficient, secure, and modern service.

For example, AI-driven platforms can provide younger clients with tools for budgeting, portfolio tracking, and investment analysis, making it easier for them to take control of their finances. For generations who are used to running multiple aspects of their lives online financial tools, facilitated by AI, can create opportunities for financial advisers and wealth managers to open up to a more atypical client base, start to build relationships earlier and create deeper pipelines for their firms in the longer term.

AI will not take your job, but it can take out the strain

AI is revolutionising the financial advisory industry by reducing the administrative burden on advisers and enabling them to provide a more secure, and efficient service to their clients. It also offers a very personalised approach in the way it stores, tracks and highlights information. All of this brings the ultimate benefit of more time for humans to be, well, human and do what they do best – nurture and grow relationships. With the added benefit of attracting a broader client base and offering a more modern and innovative approach, AI is poised to transform the way financial advisers work and the services they provide.

But, don’t forget to think before you adopt AI. Plan properly, ask questions, ensure transparent and open discussions before you step in. AI really is there to bring fundamental benefits and reduce risk - not create more.

Joseph Twigg

AI is revolutionising the financial advisory industry by reducing the administrative burden on advisers, enabling them to provide a more secure, and efficient service to their clients.

Aveni Assist

SOLUTION SHOWCASE

Aveni is an award-winning FinTech company, dedicated to enhancing human expertise and efficiency through advanced AI technology. Specialising in Natural Language Processing (NLP) and AI automation, Aveni provides tailored solutions that empower financial advisers and wealth managers to achieve remarkable efficiency while maintaining the highest standards of service. Aveni's flagship products, Aveni Detect and Aveni Assist, are purpose-built to streamline workflows, automate administrative tasks, and improve quality assurance processes, ultimately achieving new levels of productivity for wealth and advice businesses.

At Aveni, we don’t just create AI tools; we innovate deep tech solutions that redefine what’s possible in financial services, driving efficiency, reducing risk, and enhancing value across the entire operating model. With a powerful combination of financial sector expertise and pioneering AI capabilities, we’ve spent years building specialised models which address the complex challenges of the tightly regulated environment of financial services. Our world-class team of scientists and engineers operate at the vanguard of Large Language Model development, setting new standards in accuracy, transparency, and reliability.

SOLUTION OVERVIEW

Aveni Assist is an AI-powered assistant built specifically for financial advisers. It sits with you on every client call extracting the relevant information needed to automate low value admin so you have more time to do what you do best: getting great financial outcomes for your clients.

FEATURES & BENEFITS

Automate low-value admin

Takes the relevant pieces of information from lengthy client calls and automatically completes time consuming admin. Aveni Assist will administer CRM, write client emails and draft content for suitability reports within minutes of finishing a call.

Aveni Meeting Assistant

Record meetings and easily upload them to Aveni Assist when it suits you using the Aveni Meeting Assistant app.

VC integrations

Integrated into all the most common VC platforms including Teams, Meet and Zoom. Quick to set-up with no major technical integration required.

Traceability

The outputs derived from our AI systems are completely traceable and compliant, ensuring reliability and accuracy for your advisers and clients.

Vertical AI

Extensive financial services experience put to use tuning models, ensuring financial terms, concepts and formats are output correctly.

USE CASES

Significant productivity inefficiencies in the advice industry

Aveni Assist enables unparalleled adviser/paraplanner efficiency, cutting post meeting admin, case checking and note taking from hours to minutes.

Closing the advice gap in an underserved market

39M adults in the UK fall in the advice gap representing a £185 billion adviser opportunity.

Aveni Assist can lower the cost to serve allowing advice firms to address a greater proportion of the underserved market resulting in increased revenue, as well as allowing for greater differentiation and personalisation for more targeted support and improved customer service.

Data quality and integrity as standard Firms have historically struggled to capture meaningful business and client data, or when they have its quality has compromised its utilisation.

Aveni Assist enables clear, searchable and traceable audit trail of client information that can be used across the advice value chain.

GET IN TOUCH

Joseph Twigg Chief Executive Officer

Aveni

joseph@aveni.ai

aveni.ai

WEBSITE hello@aveni.ai

EMAIL Edinburgh, United Kingdom HQ 2018 FOUNDED 51-100

CLIENTS

EMPLOYEES 1-10

CLIENT TYPES

EAMs, Bank Wealth Managers, Family Offices, Financial Advisers, Insurance-based, Trust & Fiduciary, Digital Wealth Platforms

CLIENT LOCATIONS

North America, Western Europe

FOUNDATION FUTURE SHOWCASE #2

To unlock AI's full potential, wealth managers must first address a foundational requirement: data.

Wealth managers should adopt a phased approach to AI integration, starting with data readiness. By ensuring that data is clean, structured, and accessible, firms can implement AI tools suited to immediate needs.

Contributed by

www. pointgroup .io

Managing data – specifically its governance, organisation, and orchestration – is essential for AI to function effectively.

ABOUT POINT

At Point, we empower investment managers to make data-driven decisions through our innovative IDI platform.

Future Foundation

Data first. AI ready: building a future-proof foundation for AI in wealth management 2

By Tom Williams, CEO at Point

The wealth management industry is entering a period of rapid evolution, driven largely by technological advancements that help businesses adapt to ongoing social, regulatory, and economic changes. Among these advancements, Artificial Intelligence (AI) stands out as a transformative force, with the potential to improve decision-making, boost efficiency, and deliver a more personalised experience to clients.

However, to unlock AI's full potential, wealth managers must first address a foundational requirement: data.

Managing data – specifically its governance, organisation, and orchestration – is essential for AI to function effectively. This article outlines key steps for wealth managers to prepare for AI-driven solutions, starting with establishing a solid data foundation, moving to AI-powered assistants that augment human expertise, and ultimately progressing to AI agents capable of handling complex tasks.

The importance of data in wealth management

Wealth management is an inherently data-intensive industry. To make well-informed decisions, wealth managers and financial advisers must draw from vast amounts of information, ranging from client portfolios and market trends to economic indicators and Environmental, Social, and Governance (ESG) data. As data sources continue to multiply and diversify, managing and unlocking the value trapped in this data becomes more challenging. The risk of analysis paralysis is real.

Historically, the wealth management sector has lagged behind other industries in addressing underlying data challenges. Over the past few years, the industry has made strides in technology adoption; however, true digital transformation requires more than advanced

technology. Data serves as the bedrock of effective decision-making, and without a strong data foundation, even the best AI tools can produce unreliable results. In this context, data governance, organisation, and orchestration are indispensable to build a scalable and successful AI strategy. Without these pillars, AI implementations may yield inaccurate predictions, limiting their value to wealth managers.

To understand why data is crucial, it helps to look at the distinct roles of data and technology as they serve distinct but complementary roles in building effective wealth management operating models. Whilst data is the foundational asset that informs decision-making, technology is the toolset that processes, analyses, and derives insights from that data. Before launching AI solutions, wealth managers must ensure that data is structured, clean, and accessible, setting the stage for AI to deliver accurate, actionable insights.

Data governance: the essential foundation

Data governance is the framework of policies, processes, and controls that ensure the quality, integrity, security, and accessibility of data. In the wealth management industry, data governance is particularly critical due to the stringent regulatory landscape, including compliance with GDPR, MiFID II, and other data protection standards. Without effective data governance, wealth management firms run the risk of data inaccuracies, breaches of client confidentiality, and regulatory non-compliance.

Implementing robust data governance allows wealth managers to establish consistent data practices that enhance transparency and traceability. Proper data governance ensures that data used for AI applications is accurate and up-to-date, minimising the risk of errors in AI-powered analysis and recommendations.

With well-organised data, wealth managers can analyse portfolios, assess risks, and identify opportunities with greater precision and speed.

Effective governance also helps organisations navigate the increasingly complex regulatory landscape. For instance, clients want assurance that their data is safe and used responsibly. When clients trust that their information is being handled securely and ethically, they are more likely to accept and even embrace AI-driven solutions.

Moreover, data governance fosters transparency by enabling managers to trace data's origins and transformations throughout its lifecycle. This transparency is crucial to making AI's role in decisionmaking understandable and explainable, which is essential for maintaining trust. When AI outputs can be explained, wealth managers and clients alike feel more confident in the technology’s role in investment decisions, viewing it as a tool that adds value to human expertise rather than a ‘black box’.

Data organisation and orchestration for AI applications

With governance in place, the next step in preparing for AI is organising and managing data. Wealth managers handle data from diverse sources, including financial markets, client reports, and third-party research. Aggregating, cleaning, and analysing this information in a cohesive manner is vital for making well-informed investment decisions. Without a structured framework, data can become fragmented and siloed, reducing the efficiency and effectiveness of AI applications.

Traditional portfolio management systems – storing much of the data an AI tool might want to exploit - rely on on-demand calculations. This introduces latency and inefficiencies, especially when using conversational AI like LLMs. In contrast, a precalculated data model that stores persistent analytics in a centralised data warehouse, refreshed daily, offers significant advantages for AI applications:

• Instantaneous insights

• Scalability for complex queries

• Consistency and accuracy

• Cost and resource efficiency

• Future-ready for advanced AI

Point’s Investment Data Intelligence platform is an example of a data architecture designed to provide these advantages. It aggregates complex investment data from multiple sources, cleans and organises it, and presents the information through easy-to-understand visualisations. With well-organised data, wealth managers can analyse portfolios, assess risks, and identify opportunities with greater precision and speed.

Further, the platform allows data sharing with thirdparty solutions, enabling users to create modular and customisable operating models that support scalable growth. Critically, from the perspective of AI the platform provides the pre-persisted, pre-calculated data model that supports effective AI exploitation.

AI Assistants: augmenting human expertise

Once data is organised and accessible, AI assistants can be introduced to augment human expertise. These AI-driven tools can handle a range of tasks, helping wealth managers extract insights and enhance their understanding of data by enabling interaction in natural language. This capability allows wealth managers to “talk” to their data, asking questions and receiving immediate insights in real time.

This is where AI’s value is being realised today. AI assistants are well-positioned to support analysis, working alongside human experts to navigate complex datasets. For example, Point’s AI assistant enables wealth managers to quickly generate actionable insights and respond to client questions on the spot. This instant, flexible access to data helps wealth managers make better decisions faster, improving both client engagement and decision-making.

Importantly, AI assistants are not intended to replace human advisers – they are designed to enhance human expertise. While AI can process large datasets at speeds beyond human capacity, it still relies on human judgment to interpret insights and translate them into actionable strategies. In this way, AI serves as a powerful tool in the wealth manager’s toolkit, empowering them to offer more personalised and data-driven services to clients.

AI Agents: the future of wealth management

Looking further ahead, AI agents represent the next frontier for wealth management. While AI assistants focus on specific tasks, such as generating insights, AI agents are capable of handling more complex, autonomous processes. These agents could manage entire portfolios, update CRM records, create tailored client reports, and even monitor regulatory compliance continuously.

However, deploying AI agents requires even more stringent data governance and organisation. As AI agents make more autonomous decisions, wealth managers must ensure that these systems align with both regulatory standards and ethical guidelines. Building trust is essential; clients and wealth managers alike must have confidence in AI agents’ capabilities, as even a minor error can have serious implications in a regulated environment. An AI agent with a 95% success rate may be acceptable in other fields, but in wealth management, even one mistake can endanger trust, underscoring the need for rigorous oversight.

Challenges and best practices for AI adoption

Adopting AI in wealth management brings certain challenges. Common barriers include data silos, legacy systems, and a shortage of in-house AI expertise. Additionally, some advisers may resist AI, concerned that it could change their roles. To overcome these challenges, wealth managers should adopt a phased approach to AI integration, starting with data readiness. By ensuring that data is clean, structured, and accessible, firms can implement AI tools suited to immediate needs, such as AI assistants for data analysis. Over time, as familiarity with AI grows, more sophisticated tools like AI agents can be introduced.

A successful AI strategy also requires fostering a culture of innovation and trust. Employees need to understand that AI is a tool that supports, not replaces, their expertise. While AI can streamline routine tasks and provide deeper insights, human judgment and creativity remain crucial for delivering personalised client service. By promoting an environment where AI complements human skills, firms can encourage wider adoption and support for AI solutions.

In summary

Wealth management is inherently datadriven, requiring robust data governance and organisation to enable compliance, transparency, and effective decision-making. By aggregating and structuring data from diverse sources, wealth managers can leverage platforms like Point’s Investment Data Intelligence solution to establish scalable, customisable operating models. This foundation fosters trust through transparent, explainable AI solutions that reveal data lineage and transformations.

With a strong data framework in place, wealth managers can utilise AI assistants to enhance decision-making by processing vast datasets and delivering actionable insights. Looking ahead, AI agents could autonomously manage tasks like portfolio optimisation and compliance, provided stringent data governance and ethical oversight are maintained. Addressing adoption barriers such as legacy infrastructure and cultural resistance through a phased approach –beginning with data optimisation and advancing to AI agents – will enable wealth managers to harness AI's transformative potential responsibly, improving operational efficiency, client outcomes, and industry leadership.

Tom Williams

A successful AI strategy requires fostering a culture of innovation and trust.

Point

SOLUTION SHOWCASE

Founded in 2017 by a team of industry veterans, Point has a proven track record of building data-driven Target Operating Models and enterprise-ready Investment Data Intelligence software for some of the most demanding clients in the sector.

The Point Investment Data Intelligence (IDI) platform builds on this legacy. It’s specifically designed to address the industry's core challenge: capturing, leveraging, and unlocking the value of proprietary investment data to create scalable, profitable businesses.

Our vision is a future where investment managers can instantly access the insights they need, anytime and anywhere, driving better client outcomes and accelerating business growth.

SOLUTION OVERVIEW

The future of investment management will be driven by AI, and preparation today will ensure leadership tomorrow. The Point Investment Data Intelligence (IDI) platform enables wealth and asset managers to fully harness their data through advanced architecture, sophisticated analytics, and a suite of AI tools.

Our platform integrates and steamlines investment data, preparing firms to leverage AI's potential by providing direct engagement with data via 3rd party AI applications - or own AI-powered Investment Data Assistant. The Point AI assistant is continually evolving and delivers instant answers, empowering relationship managers, portfolio managers, and the C-suite with data-driven insights.

Designed as the team’s Most Valuable Data Player, the Point AI assistant represents the first release of our evolving AI capability set trained on proprieratry investment data. As AI-driven agents emerge, Point will remain at the forefront, enabling investment managers to make informed decisions, enhance client engagement, and scale effortlessly.

FEATURES & BENEFITS

The Point IDI platform provides wealth and asset managers with tools to optimise their data through advanced data architecture, sophisticated analytics, and an expanding suite of AI tools.

The Point IDI platform assists clients in making informed investment decisions, engaging with their clients, and scaling their operations. It is designed to meet the specific needs of individual managers by leveraging a capability that aggregates, analyses, visualises, and shares complex investment data to overcome daily challenges faced by wealth and asset managers.

The Point platform includes an aggregation layer, a next-gen Independent Investment Book of Record (IIBOR), an advanced analytics engine, an extensible investment data warehouse and a set of untuitive UI dashboards and reporting features. It also includes a set of Opean Data Sharing capabilities allowing users to share data with 3rd party technology solutions.

USE CASES

Point provides wealth and asset managers with the data foundation they need to build data-first: AI-Ready businesses. Point:

Automates end-to-end total wealth reporting, producing timely, accurate, and fully branded PDF reports for investment and wealth managers, and family offices.

Delivers advanced multi-asset and multi-client analytics to provide insights into portfolio composition, performance attribution, and risk exposures, driving intelligence-led investment decision-making.

Ensures your team works with normalised, reconciled data across custodians and managers, covering all asset classes and ownership structures.

Provides accurate, reconciled data to 3rd party client portals, CRM systems, accounting platforms or portfolio management solutions (PMS) enabling firms to modernise their technology platforms without rebuilding core infrastructure.

Provides the data firms need to make the most of advances in AI.

DISCOVER

MORE GET IN TOUCH

COMPANY

Point Group (Point)

Tom Willliams Chief Executive Officer Point

tom.williams@pointgroup.io

pointgroup.io

EMAIL

WEBSITE contact@pointgroup.io

HQ 2017 FOUNDED 11-20

London, United Kingdom

CLIENTS

EMPLOYEES 1-10

CLIENT TYPE

EAMs, Bank Wealth Managers, Family Offices, Trust & Fiduciary, Digital Wealth Platforms, Asset Servicing Companies

CLIENT LOCATIONS

Middle East, North America, Western Europe

GENERATIVE SHOWCASE #3

AI

AI is transforming how we live and work, with its impact only beginning to unfold.

GenAI gives financial firms a powerful way to process and respond to unstructured and structured data, driving efficiencies and growth. GenAI marks a major shift, especially in boosting adviser productivity.

Contributed by

The productivity boost AI promises across industries, including financial services and wealth management, will be transformative – determining which firms thrive, and which struggle to survive.

ABOUT INVESTCLOUD

InvestCloud powers connected experiences through humaninspired, technology-forward platforms that enable wealth and asset managers, private banks, broker-dealers, and TAMPs to work smarter, enrich client relationships, and elevate financial outcomes.

Generative AI

Generative AI: redefining the adviser experience in wealth management 3

By Boris Rankov, VP Product Management & Strategy at InvestCloud

Setting the scene: the AI revolution in wealth management

AI is transforming how we live and work, with its impact only beginning to unfold. Technological advancements that seemed unimaginable a few years ago are now emerging at an accelerating pace. This momentum suggests we are on the brink of a revolution as impactful as the Cambrian Explosion was to biology and evolution. The productivity boost AI promises across industries, including financial services and wealth management, will be transformative – determining which firms thrive, and which struggle to survive.

For hundreds of millions of years before the Cambrian Explosion, life on Earth was mostly simple, multi-celled organisms. Around 540 million years ago, evolution accelerated, leading to the emergence of major animal and plant groups. Some scientists suggest this was driven by the advent of biological vision, sparking an evolutionary arms race.

Similarly, GenAI gives financial firms a powerful way to process and respond to unstructured and structured data, driving efficiencies and growth. When embedded into a secure, scalable digital platform, it gains access to a unified, permissioned data layer – enabling it to deliver highly relevant, context-aware outputs across the adviser journey. This integration transforms GenAI from a standalone tool into a strategic enabler, modernising adviser workflows, boosting productivity, and enhancing the end-client experience at scale.

We may be seeing a Cambrian Explosion of AI in wealth management, with GenAI revolutionising adviser tools and capabilities, much like its biological counterpart expanded life’s diversity.

The adviser experience: challenges and opportunities

Wealth management advisers face dual pressures: managing client relationships while navigating increasingly complex regulatory and financial landscapes. Much of their time is consumed by administrative tasks, compliance checks, and research – time that could be better spent building relationships and crafting tailored investment strategies.

GenAI-enabled digital wealth platforms address these challenges by automating and enhancing critical aspects of adviser workflows:

• Client communication

Large Language Models (LLMs) help advisers draft personalised emails, reports, and marketing content in minutes. Leveraging comprehensive datasets, these communications are both accurate and tailored.

• Data synthesis

GenAI analyses and summarises market trends, enabling advisers to stay ahead in volatile markets. Custom queries about client portfolios deliver insights tailored to individual needs.

• Regulatory compliance AI simplifies compliance by interpreting and checking regulations, significantly reducing the manual workload.

From data overload to actionable insights

A standout benefit of GenAI – when embedded within a secure, data-rich digital wealth platform – is its ability to transform firm-wide data into actionable insights. By tapping into integrated transaction histories, risk profiles, portfolio data, and client interactions, GenAI can power a range of high-impact use cases that enhance both adviser efficiency and client outcomes, including:

• Dynamic client profiles

Integrating data from transaction histories, risk profiles, and market behaviours, GenAI creates real-time client profiles, enhancing advisers' understanding of their clients’ needs.

• Scenario analysis

Simulating economic scenarios with GenAI allows advisors to provide clients with clear, data-backed recommendations during uncertainty.

• Hyper-personalisation

Tailored investment recommendations and accessible client reports enhance the advisory experience.

• Pre-defined workflows with agentic systems

Pre-configured workflows streamline routine tasks like generating portfolio summaries or answering client FAQs, freeing advisers to focus on complex client needs.

Practical applications and early success stories

Forward-thinking firms are already leveraging GenAI effectively:

• Virtual assistant integration

GenAI-powered assistants manage routine queries, allowing advisers to focus on strategic conversations. Personalised messages generated by these assistants ensure relevance and accuracy.

• Onboarding automation

GenAI simplifies and personalises onboarding, reducing drop-off rates and enhancing client satisfaction with tailored materials.

The future: challenges and considerations

While GenAI holds immense potential, implementation challenges must be addressed:

• Data privacy and security

Safeguarding client data during AI processes is paramount.

• Human oversight

Ensuring AI augments, rather than replaces, human advisers is critical. The focus must remain on relationship-building and value delivery.

• Retrieval-Augmented Generation (RAG) This approach enhances GenAI outputs by integrating relevant data before generating responses, improving accuracy and contextual relevance. In wealth management, RAG enhances client interactions by delivering precise, informed outputs.

Conclusion: embracing the GenAI-driven future

Tools like Digital AI Assistants are gamechangers, streamlining workflows and freeing advisers to concentrate on building relationships and crafting impactful strategies. But GenAI alone isn’t enough – it reaches its full potential when embedded into comprehensive platforms that support every aspect of the adviser journey. Platforms like InvestCloud’s Digital Wealth suite – with its integrated data warehouse, workflow automation, and forthcoming SMART capabilities – provide the foundation for scalable, secure, and humancentric AI experiences. The future belongs to those who act decisively – now is the moment to lead, innovate, and redefine what’s possible in wealth management.

Boris Rankov

InvestCloud

SOLUTION SHOWACSE

Founded in 2010, InvestCloud is a global leader in wealth technology, pioneering a smarter financial future through its scalable SaaS first Digital Wealth Platform. Transforming the wealth management industry, InvestCloud serves over 550 clients globally, managing US$6 trillion in assets, including wealth managers, asset managers, retail banks and private banking clients.

The company excels in delivering connected adviser-client experiences, large-scale personalisation, and cutting-edge solutions that enhance intelligence and foster collaboration. Supported by a powerful Digital Data Warehouse, its platform seamlessly scales across the entire wealth continuum, from mass afluent to high net worth and private banking clients.

In 2024, InvestCloud was recognised as a CNBC World’s Top Fintech Company, affirming its dedication to innovation and client success.

SOLUTION OVERVIEW

InvestCloud empowers wealth managers and their clients with solutions that elevate the client experience and drive operational efficiency, allowing advisers to focus on revenue-generating activities instead of administrative tasks. Its SaaS-native, and its modular design enables personalised user journeys, including all aspects of digital onboarding, intelligent client servicing, portfolio management, investment advisory, automated KYC/AML, and scalable content distribution.

InvestCloud’s robust Digital Data Warehouse integrates structured and unstructured data from diverse sources, delivering seamless experiences across devices. With access to over 900 data tables and 80,000+ fields, the platform provides a unified view of all client accounts and relationships, from one single place.

By combining cutting-edge technology with operational flexibility, InvestCloud equips wealth managers with the tools they need to serve clients effectively, improving outcomes while scaling across the complete wealth continuum.

FEATURES & BENEFITS

The InvestCloud Digital Wealth Platform provides clients and advisers a unified solution for managing the entire client lifecycle. It covers every stage, from prospecting tools and hybrid onboarding to intuitive client/adviser experiences, investment advisory, portfolio management, workflow-driven ongoing suitability reviews and client servicing.

Powered by SMART, our platform takes a data-driven, AI-enhanced approach to deliver actionable insights and automate adviser workflows. The Adviser Copilot simplifies communication, task tracking, and nextbest actions; Insights provide summarised portfolio commentary and data analysis in plain language; and Practice Analytics identifies trends and gaps in adviser books, proactively suggesting ways to grow and retain client relationships.

InvestCloud boosts efficiency with portfolio management tools for portfolio and proposal construction, regulatory-driven suitability checks, automated rebalancing, and order generation.

USE CASES

InvestCloud’s client lifecycle management tools allow advisers to effectively manage their book of business. For example, it simplifies lead generation, tracks pipeline progress, and enables frictionless collaboration on tasks to enhance client service.

Advisers can streamline the prospect journey with hybrid onboarding, including comprehensive fact-finding, risk profiling, online document sharing, and integrated ID&V and AI-powered KYC and AML checks, leading to efficient account opening. Additionally, client servicing tools like secure messaging, document management, and configurable proposals ensure enhanced productivity and maximum client engagement.

InvestCloud equiped one of the UK’s largest wealth managers with a one stop shop client lifecycle management platform. By consolidating multiple internal and third-party systems within its Digital Data Warehouse, InvestCloud provided a single source of truth for a scalable and secure platform. The solution includes centralised lead and pipeline tracking, custom workflows for task and activities management across teams, onboarding for various client types and service levels (with integrated risk profiling, smart KYC, and ID&V checks), and suitability reporting, for greater front-office productivity.

TOUCH

Boris Rankov

VP Product Management & Strategy

InvestCloud

brankov@investcloud.com

www.investcloud.com

WEBSITE brankov@investcloud.com

CLIENTS

CLIENT TYPE

EAMs, Bank Wealth Managers, Family Offices, Financial Advisers, Insurance-based, Digital Wealth Platforms, Asset Servicing Companies

CLIENT LOCATIONS

Asia, Middle East, North America, Western Europe

Editorial Programme

WealthTech Landscape Reports 2025

Our WealthTech Landscape Reports cover all key wealth management geographies. Each report provides the reader with a compelling mix of thought leadership, solution showcases and Solution Provider Directory highlights designed to provide our wealth management and vendor community with a modern and insightful knowledge resource for its technology and related business needs.

Coming soon! Europe WealthTech Landscape Report 2025

Q2 2025

With the rapid pace of change in financial services, understanding technology's impact on this sector is more crucial than ever. In this, our first Europe-focused Landscape Report, we feature a series of insightful articles that explore the trends, challenges, and innovations surrounding technology adoption in wealth management. Contributions come from a range of organisations, and cover many of the issues impacting the wealth management industry in Europe today (and tomorrow).

Interested in participating in the report? Contact us for more info and sponsorship opportunities.

Showcasing the application of technology in wealth management in the US

AI-POWERED VIDEO SHOWCASE #4

The future of personalised, scalable client-engagement is here

Leverage personalised AI-powered videos to increase the value and frequency of client engagement, and acquire incremental AUM through new multigenerational clients.

Contributed by

Combining AI-powered personalised content with an accessible, highly engaging medium creates a recipe for lasting engagement.

ABOUT STORYLINE

Storyline uses artificial intelligence to transform raw data into personalised, interactive content to engage and expand your audience.

AI-Powered Video

AI-powered video: the future of personalised, scalable client-engagement is here

By David Navama, CEO & Co-founder at Storyline AI

In an age of shrinking attention spans and information overload, wealth managers face a growing challenge: how to effectively engage clients with complex financial information. Artificial Intelligence (AI) is emerging as a game-changer, with powerful tools now available to transform the way financial advice is delivered and consumed. However, traditional communication formats – emails, PDFs, and static presentations – increasingly fall short of capturing clients' attention and delivering memorable insights. While AI-powered personalisation is a step in the right direction, we must also modernise the communication format itself to unlock the greatest opportunity for client retention and growth for years to come, and video will rapidly prove to be the preferred format, for both advisers and clients.

You can now leverage personalised AI-powered videos to increase the value and frequency of client engagement, and acquire incremental AUM through new multi-generational clients.

What can wealth managers learn from YouTube and TikTok?

As content consumption trends shift, platforms like YouTube and TikTok are setting the bar for engagement through video. YouTube remains the most popular platform in the U.S., with 83% of adults using it; similarly, TikTok's popularity has surged, with usage among adults rising to over one-third in recent years. It’s also worth noting that “Business and Finance” was one of the fastest growing content categories on TikTok between 2023-2024, jumping 73% year-over-year. Even Instagram, a platform previously known as the leading image-sharing platform, announced in 2021 that their focus had fully shifted to videos. These platforms exemplify how combining AI-powered personalised content with an accessible, highly engaging medium creates a recipe for lasting engagement. Wealth management firms can learn from these platforms’ success by adopting video as a core element of client communication, making complex financial topics both approachable and memorable.

Video has long demonstrated superior engagement, with Forrester reporting that individuals are 75% more likely to watch a video then read print. Research has also shown viewers can retain 950% more information from video than from text. With these advantages, video has a clear edge over traditional formats in capturing client interest. But more importantly, when combined with AI, video can deliver hyper-personalised financial insights that speak directly to individual clients, transforming complex data into actionable, easy-to-understand information.

Video has long demonstrated superior engagement, with Forrester reporting that individuals are 75% more likely to watch a video then read print.

The growing demand for personalised experiences

In recent years, personalisation has become an imperative in financial services, with clients expecting the same degree of customisation they experience in other areas of their lives. According to a recent Ernst & Young report, 34% of clients would increase their investments if provided with hyper-personalised experiences, while 49% would even pay more for services that reflect their unique needs. Yet, achieving this level of personalisation in wealth management has proven challenging for most firms.

Traditional methods of client communication, such as in-person meetings, emails, and printed reports, are time-consuming, inefficient, and lack the personalisation that today's clients demand. AI-powered video offers a unique solution to these challenges, enabling wealth managers to deliver complex financial information in a concise, personalised, and easy-tounderstand format, at any scale. By using client data – such as investment goals, risk appetite, and preferences – AI can craft personalised narratives in each video. This approach ensures that clients not only receive relevant insights but also feel that their unique financial journey is truly understood by their adviser.

Enhancing client engagement and retention

So how does this work in everyday client-facing scenarios? Imagine a client receiving a video summary of their portfolio’s quarterly performance, where AI highlights key investment insights. One of the most valuable aspects of a portfolio review video is that it prepares clients with key performance data ahead of scheduled meetings. This pre-engagement allows wealth managers to dedicate meeting time to strategy, building rapport, and addressing client questions, rather than simply presenting data. Their video can be customised to the client’s preferred level of detail, investment experience, and communication style. Such hyper-personalisation not only improves client understanding but also fosters trust and strengthens the adviser-client relationship.

Beyond performance reports, AI-powered videos are versatile tools that can deliver fund reviews, market insights, and even custom financial planning recommendations. Such tools empower clients to stay informed and engaged with relevant, timely content on the go. Let’s say you have a large portfolio exposure to Microsoft, and one-week prior to their quarterly earnings report you automatically receive a video summarising analyst estimates, your current exposure levels, and potential trade actions. All of these use-cases are available today, and offer wealth managers powerful opportunities to improve client satisfaction, increase loyalty, and ultimately drive greater business growth.

To stay competitive, you need to deliver high-impact content with the click of a play button.

The growth of digital engagement within wealth management

The wealth management industry is already experiencing a significant shift towards digital engagement. Research from Ernst & Young indicates that a staggering 57% of financial advisers are unsatisfied with their current data delivery tools. Over one-third of clients already express a desire for more personalised, curated experiences—and nearly half are willing to pay a premium for services tailored to their unique needs. These findings highlight the growing demand for innovative solutions like AIpowered videos that can bridge the gap between adviser capabilities and client expectations.

For firms ready to adopt this technology, here are some strategies to ensure the successful integration of AI video into their consumer engagement experience:

1.Data integration and personalisation:

To maximise the potential of AI-powered video, firms should either be using an established data platform, or have an organised and accessible data infrastructure. While there are many use-cases for AI-powered videos that do not require any client-level data (such as market recaps or fund reviews), many of the most powerful usecases for the technology leverage access to portfolio data.

2. Compliance and security:

Given the sensitive nature of financial information, it is crucial to work with AI video providers that prioritise compliance. Look for platforms that offer real-time monitoring, data security measures, and complianceready workflows to protect client information.

3. Client education and literacy:

Wealth managers can use AI-powered videos not only to update clients on their portfolios but also to educate them on broader financial concepts. Video content designed with interactive features – such as feedback prompts or call-to-action buttons – can drive higher engagement and deepen client understanding, which is key to building long-term relationships.

AI-powered videos represent just the beginning of a transformative journey toward highly personalised, engaging client communication. By adopting this technology, wealth management firms can build stronger relationships, boost client satisfaction, and enhance financial literacy. Embracing AI in client engagement strategies is no longer optional – it’s a path to sustained growth and relevance in an increasingly digital world.

With digital engagement becoming more prevalent, wealth management firms face growing demands for interactive, digital-first experiences. AI-powered video fits naturally into this shift, allowing advisers to meet clients where they already spend time – online and on their devices. The future of financial advice is personalised, interactive, and powered by AI.

David Navama

Storyline AI

SOLUTION SHOWCASE

Storyline’s audio and visual storytelling technology transforms raw data into personalised, AI-powered videos transforming the way organisations engage with customers.

Through its platform, wealth managers can deliver hyperpersonalised videos that speak to each client’s unique financial journey – an approach proven to drive engagement and satisfaction.

Storyline’s video content prepares clients with tailored, visually engaging performance updates, saving valuable meeting time. Its AI-powered storytelling and analytics features mean advisers can track real-time engagement, making it easier to optimise future interactions based on individual client preferences and needs.

Storyline redefines client engagement and positions wealth management firms for sustained growth.

SOLUTION OVERVIEW

Storyline’s platform enables financial firms to differentiate themselves with personalised, highimpact client communications.

Storyline’s solution delivers exceptional value in wealth management by combining AI-driven personalisation with interactive, real-time video communication.

Through its platform, wealth managers can deliver hyper-personalised videos that speak directly to each client’s unique financial journey - an approach proven to drive engagement and satisfaction.

Storyline’s video content prepares clients with tailored, visually engaging performance updates, saving valuable meeting time and allowing advisers to focus on strategy and planning. Moreover, its AI-powered storytelling and analytics features mean advisers can track real-time engagement, making it easier to optimise future interactions based on individual client preferences and needs.

Storyline meets a crucial demand by providing a scalable, compliant, and efficient solution. By enabling advisers to deliver sophisticated financial insights in a friendly, interactive format, Storyline redefines client engagement and positions wealth management firms for sustained growth.

FEATURES & BENEFITS

Storyline’s patent-pending, audio-visual storytelling technology allows financial firms to differentiate themselves with personalised, high-impact client communications.

• Data integration from multiple sources: Seamlessly connect data from various systems to create unified, personalised videos.

• Build and customise episodes: Tailor videos to specific client segments, enabling hyperpersonalised communication at scale.

• Theme branding and rich media: Customise themes, branding, and add dynamic media to reinforce your brand identity.

• Embed educational and promotional videos: Add videos that address client needs while promoting additional services.

• Measure real-time engagement: Use analytics to track client interaction and optimise future communications.

Storyline’s AI-powered storytelling helps firms improve communication, engagement, and reporting frequency, while driving growth through:

• Increased client engagement: Keep clients informed and connected with videos they enjoy and retain.

• Higher conversion rates for new prospects: Engage mass affluent prospects with scalable, personalised video experiences.

• Enhanced client retention: Foster stronger relationships by delivering consistent, meaningful interactions.

• Growth in assets under management: Support your growth strategy by deepening relationships and broadening reach.

• Improved adviser productivity: Automate routine tasks, allowing advisers to focus on high-value activities.

INTELLIGENCE PORTFOLIO SHOWCASE #5

AI is not changing the principles of sound wealth management –but it is redefining what is possible.

Clients want reassurance that their investments are being managed effectively. This requires not only a solid strategy but also a deep understanding of how evolving global events impact that strategy. Just as critical is the ability to communicate these insights with clarity and confidence.

Contributed by

Staying on top of the vast and ever-changing landscape of financial news, market trends, and economic developments is a daunting task.

ABOUT PEBBLE

Pebble turns global news, market data and events into actionable portfolio insights – accurately, intuitively, and in real-time.

Portfolio Intelligence

Portfolio intelligence – AI and the future of wealth management 5

By Justin Whitehead, Co-founder and CEO, Pebble Finance

In an increasingly complex world, clients want reassurance that their investments are being managed effectively. This requires not only a solid strategy but also a deep understanding of how evolving global events – such as geopolitical tensions, regulatory shifts, or technological disruptions – impact that strategy. Just as critical is the ability to communicate these insights with clarity and confidence.

The role of AI in wealth management

Staying on top of the vast and ever-changing landscape of financial news, market trends, and economic developments is a daunting task. No single adviser can continuously monitor and interpret every relevant event across multiple client portfolios in real time. The scale of information is simply too great. This is where technology is reshaping the industry.

While Artificial Intelligence (AI) will never replace the personal relationships and trust built through client interactions, it can provide essential support in managing the increasing complexity of investment decision-making. AI-powered portfolio intelligence platforms are emerging as a key tool for wealth managers, helping to bridge the gap between raw information and actionable insights.

With AI, advisers can:

• Respond to client concerns in real time When markets react to unexpected events, clients seek immediate answers. AI-driven insights allow advisers to connect market movements to portfolio impact instantly, reducing the need for lengthy analysis and reinforcing client confidence.

• Leverage firm-specific research more effectively AI enables wealth managers to integrate proprietary research with broader market data, crafting a cohesive investment perspective that aligns with their firm’s strategic approach.

• Enhance investment proposals By linking real-world events to client portfolios and target models, AI makes investment discussions more tangible and relatable, facilitating clearer client understanding and engagement.

The evolution of analytical techniques

The foundation of AI-driven wealth management lies in quantitative finance. For decades, institutional asset managers have relied on advanced analytical techniques to guide investment decisions. Attribution models, such as Brinson return attribution, have long been used to dissect performance drivers, while quantitative signals have helped anticipate market shifts. These methodologies, while powerful, were historically accessible only to sophisticated investors and large asset management firms due to their complexity.

Today, technological advancements are making these tools more accessible. Cloud computing and AI-driven analytics are transforming how wealth managers process information, allowing for sophisticated portfolio analysis, automated news synthesis, and rapid integration of market research – all in real time.

AI-powered risk management

Another crucial application of AI in wealth management is risk management. Traditional risk models rely on historical data and static assumptions, which can be insufficient in today’s dynamic markets. AI, however, can assess real-time data streams, market sentiment, and alternative datasets to provide more proactive risk assessments. By detecting emerging risks and offering predictive insights, AI helps advisers mitigate potential losses before they materialise.

Personalised investment strategies

AI is also driving greater personalisation in investment strategies. Traditional portfolio management often relies on standardised models that may not account for an individual investor’s unique financial goals, risk tolerance, and life circumstances. AI-powered advisory platforms analyse vast amounts of client data, from spending habits to social preferences, to create highly customised investment solutions. This level of personalisation strengthens client relationships and improves portfolio performance by aligning investments more closely with individual needs.

Automating routine tasks for greater efficiency

Beyond analytics, AI is streamlining routine tasks that consume significant time for wealth managers. Automating administrative processes such as compliance checks, data entry, and reporting allows advisers to focus on higher-value activities like strategic planning and client engagement. AI-driven automation improves operational efficiency, reduces costs, and minimises human error, contributing to a more effective wealth management practice.

The impact of AI on the industry

The application of AI in wealth management is not about replacing human expertise but enhancing it. It is not about automating rote tasks or deploying chatbots; rather, it is about augmenting advisers’ ability to provide deeper insights, faster responses, and a more comprehensive service to a broader client base.

• Democratisation of investment insights

Historically, high-level investment insights and sophisticated analytical tools were available primarily to institutional investors and high-networth individuals. AI is changing this landscape by making advanced financial intelligence accessible to a wider audience. Robo-advisers, for instance, leverage AI algorithms to offer personalised investment recommendations at a fraction of the cost of traditional financial advisory services. This democratisation of investment insights is empowering more people to make informed financial decisions and build wealth effectively.

• Ethical and regulatory considerations

As AI becomes more prevalent in wealth management, ethical and regulatory considerations must also be addressed. Ensuring transparency in AI-driven decision-making is crucial for maintaining client trust. Regulators are increasingly scrutinising AI applications in finance to prevent biased algorithms, ensure data privacy, and maintain fair market practices. Wealth managers must balance AI adoption with compliance to uphold ethical standards and foster long-term client confidence.

• The future of AI in wealth management

Looking ahead, AI will continue to shape the future of wealth management in profound ways. Advancements in Natural Language Processing (NLP) will enable more intuitive client-adviser interactions, making it easier to explain complex financial concepts in plain language. AI-powered predictive analytics will further refine market forecasting, helping advisers stay ahead of trends and proactively optimise investment strategies.

Moreover, the integration of AI with blockchain technology may enhance security and transparency in financial transactions. Smart contracts powered by AI could automate and enforce investment agreements with greater efficiency and accuracy. As AI technology evolves, wealth managers who embrace its potential while maintaining a human-centric approach will gain a competitive edge in the industry.

AI is not changing the principles of sound wealth management –but it is redefining what is possible. By enhancing analytical capabilities, personalising investment strategies, and improving operational efficiency, AI is empowering wealth managers to deliver superior service and better financial outcomes for clients. As the industry adapts to this technological revolution, those who leverage AI effectively while maintaining the human touch will set the new standard for excellence in wealth management.

Justin Whitehead

“We don’t foresee AI replacing human advisers wholesale, but we do think it can expand the range of investors who have access to affordable and professionally managed portfolios. Most investors are ignored or at a minimum underserved. If you have US$50,000 in your brokerage account, no one is helping you. AI can help wealth managers gain scale and efficiency, and make it efficient and thus cost effective for advisers to serve a much broader base of clients.” Barron's, 2023

Pebble Finance

SOLUTION SHOWCASE

Pebble turns global news, research, market data and events into actionable portfolio insights - accurately, intuitively, and immediately. We help advisers provide personalised service to their clients by explaining performance - what happened and why - for each individual portfolio.

The foundation of AI-driven wealth management lies in quantitative finance. Our team has deep roots in portfolio analytics, investing and determining how real world events affect asset prices.

Pebble's AI products are integrated directly within wealth management platforms and significantly reduce time spent on portfolio analysis.

SOLUTION OVERVIEW

Pebble brings portfolio and security-level performance to life by offering nuanced explanations grounded in truth and customised to be understood by your audience.

Easily understand performance trends, enriched with additional context from news and research seamlessly integrated into each narrative to answer questions like “why is my portfolio performing the way it is?”

A recent solution demonstration

At The Wealth Mosaic's AI Toolkit Roadshow 2025 New York Edition event in New York in January 2025, Pebble demonstrated how their AI technology is designed to easily integrate into existing any adviser workflow solution (third-party CRMs, reporting dashboards, etc.).

In the demonstration, Pebble’s AI extended the FactSet Advisor Dashboard to create “adviser cheat sheets” and custom newsletters by clicking on any client portfolio. Days of prep work wrapped up into seconds.

FEATURES & BENEFITS

Pebble provides portfolio intelligence at scale by packing days of manual labour… into a few seconds.

Our technology gathers market data, performs portfolio attribution, conducts quantitative analysis, analysing insights from 1,000+ articles/research reports and crafts a compelling narrative. During the process, our products verify facts for accuracy to ensure compliance and delivers a polished explanation.

Delivering personal attention to hundreds of clients shouldn’t be a challenge. Pebble helps advisers instantly answer what’s moving and why with each portfolio.

USE CASES

Respond to client concerns in real time

When markets react to unexpected events, clients seek immediate answers. Pebble AI-driven insights allow advisers to connect market movements to portfolio impact instantly, reducing the need for lengthy analysis and reinforcing client confidence.

Leverage firm-specific research more effectively

Pebble AI enables wealth managers to integrate proprietary research with broader market data, crafting a cohesive investment perspective that aligns with their firm’s unique strategic approach.

Pebble's solutions are integrated directly within adviser and investor-facing platforms leveraged across the wealth management industry.

DISCOVER MORE

GET IN TOUCH

COMPANY

Pebble Finance

Justin Whitehead

Co-founder and CEO

Pebble Finance contact@pebble.finance

pebble.finance

EMAIL

WEBSITE contact@pebble.finance

HQ 2021 FOUNDED 1-10

Cambridge, MA, United States

CLIENTS

EMPLOYEES 1-10

CLIENT TYPE

EAMs, Bank Wealth Managers, Family Offices, Financial Advisers, Trust & Fiduciary, Digital Wealth Platforms

CLIENT LOCATIONS

North America

SHOWCASE #6

MANAGEMENT PORTFOLIO

The future of investment portfolio optimisation

Investment portfolio optimisation has long relied on historical data analysis to model uncertainty and guide decisions. These traditional methods come with inherent limitations that introduce approximations and underperformance.

Contributed by

www.

With AI-enhanced portfolio management, the shortcomings of traditional portfolio management can be addressed to improve investment strategies.

ABOUT RAISE PARTNER

Raise Partner delivers digital solutions to the wealth and asset management industries, guiding investment decisions in an increasingly complex and digitalised environment.

Portfolio Management

The future of investment portfolio optimisation: leveraging AI for a comprehensive and adaptive approach in an ever-changing financial landscape

By Sophie Echenim, Founder and CEO, Raise Partner

Investment portfolio optimisation has long relied on historical data analysis to model uncertainty and guide decisions. These traditional methods are based on simplifying hypothesis hence come with inherent limitations that introduce approximations and underperformance.

With the advent of Artificial Intelligence (AI), new methodologies have emerged that enable a more dynamic and less constrained approach to portfolio management to improve decision making processes.

Raise Partner is a long-time specialist in portfolio analysis and optimisation, making cutting-edge optimisation models easy to use for decision makers in the asset and wealth management industries. Inspired by recent progress in deep learning and Large Language Models (LLMs), Raise Partner prides itself on working on next-generation optimisation techniques to enhance decision making in an everchanging financial landscape.

This article explores how Raise Partner’s new AI-driven model potentially surpasses traditional methods by leveraging both iterative learning and adaptive strategies, concluding with an overview of Raise Partner’s current optimisation models and their usage in the financial industry.

The limitations of traditional portfolio optimisation

Historically, investment portfolio optimisation has been heavily dependent on analysing past performance data with strong hypotheses on the distribution of returns.

Traditional approaches can be enhanced to some extent with robust optimisation techniques and non-gaussian models, but they come with inherent limitations:

• Static analysis

Traditional approaches assume that historical trends are reliable indicators of future performance. They rely on strong hypotheses on the returns such as stationarity which do not accurately model current market conditions.

• Parametric approach

Conventional models focus on a restricted set of financial parameters, overlooking some complex interactions and broader economic factors.

• Reactive approach

These models are often reactive rather than proactive, meaning strategies are adjusted only after significant market movements have occurred.

• Single-date optimisation

Traditional approaches ignore the dynamics of portfolio optimisation, leading to missed opportunities of enhancement.

These shortfalls limit the range of problems that can be modelled with traditional portfolio optimisation, leading to missed opportunities and unseen risk because of the required strong hypotheses.

With AI-enhanced portfolio management, these shortcomings can be addressed to improve investment strategies.

How Raise Partner transforms investment decision making with AI-driven optimisation

Raise Partner is working on next-generation models to revolutionise the approach to portfolio optimisation by integrating AI into investment decision-making. Raise Partner’s AI-powered solution operates in an offline, simulated environment where it is trained to continuously adapt. This capability ensures:

• Adaptive learning and dynamic fine-tuning

The AI model consistently recalibrates investment parameters to enhance multiple portfolio criteria.

• Broader insights beyond historical analysis

The model allows to make decisions based on real-time insights from a broader set of data rather than past trends alone.

• Adaptability to different market scenarios

Using LLMs and deep learning, the system learns to adapt dynamically to different economic and financial situations.

This innovative approach overcomes many limitations associated with traditional methods and allows for a more responsive investment strategy.

Raise Partner’s AI research is inspired by recent developments brought by LLMderived approaches in board games AI models such as Deep Blue and Alphago.

The optimisation model is trained to solve multiple-criteria problems without relying on strong hypotheses. This enables Raise Partner’s AI model to address multiple factors of the investment decision beyond the sole risk / reward financial driver, including ESG factors, for example.

Raise Partner’s model is designed to operate in a simulation-driven environment where the AI sets target criteria and executes actions accordingly. This process involves:

• Running multiple simulations to evaluate different investment strategies.

• Training the model to behave like the best trajectories.

By constantly testing and refining its approach, the AI ensures that investments align with the multi-criteria investment objectives.

The competitive edge of Raise’s AI model

Raise Partner’s AI-driven portfolio optimisation provides several competitive advantages over conventional investment strategies:

• Proactive decision-making

Our AI-driven model allows to make decisions based on real-time insights from a broader set of data rather than past trends alone.

• Continuous enhancement

The system refines its strategies dynamically, ensuring that portfolios remain optimised at all times.

• Scalability and flexibility

The model can be tailored to different investor profiles, risk tolerances, and multiple goals beyond the traditional risk/reward approach.

• Data-driven accuracy

By leveraging recent advances in LLMs and deep learning, the model avoids restrictive hypotheses hence delivers highly precise and well-informed investment recommendations.

Bringing trust and purpose to investment decisions with portfolio optimisation

Raise Partner is continuously enhancing its portfolio optimisation solutions, putting clients’ needs first, delivering industrialised solutions to support:

• Client-centric advisory and investment processes.

• Personalised investment proposals at scale.

• Strengthening regulatory investment constraints.

• Trade-offs between multiple criteria in the investment decision process.

• Transparency/clear communication and education to drive client engagement.

This year, Raise Partner is further upgrading its optimisation models to address growing challenges in the industry:

• Adding realised returns to the long list of optimisation criteria to help portfolio managers deal with accounting perspectives in addition to market risk and performance.

• Dealing with integer constraints (such as minimum holdings) with more efficient approaches.

• Extending instrument coverage to structured products.

• Mapping new dimensions of the client profile (risk, ESG, preferences) to the optimisation quantitative criteria to offer even more personalised advisory.

Raise Partner is also investing in intensive AI research to work on the next generation optimisation models that will overcome the current limitations of traditional approaches.

Conclusion

The integration of AI into portfolio optimisation is transforming the way investments are managed. Raise Partner is investing heavily on AI research to develop cutting-edge models that leverage deep learning and LLMs to overcome the limitations of traditional methods, ensuring continuous fine-tuning. By going beyond historical analysis and incorporating a broader range of parameters, Raise Partner’s model adapts to an everchanging environment.

As financial markets become increasingly complex and unpredictable, AI-driven investment strategies such as those developed by Raise Partner will play a crucial role in helping investors navigate uncertainty and achieve optimal outcomes aligned with their objectives (financial and beyond). Through constant learning and adaptation, Raise Partner is setting a new standard for comprehensive and adaptive portfolio management.

Sophie Echenim

Smart Risk

SOLUTION SHOWCASE

Raise Partner is a B2B WealthTech delivering cutting-edge digital solutions (Smart Risk) to support investment, advisory and distribution processes in the wealth and asset management Industries.

Our vision: bringing trust and purpose to investment decisions

Our mission: guiding investment decisions in an increasingly complex and digitalized environment

How we do it: using cutting-edge models and digital technology to leverage the human’s touch and expertise in the advisory process

Raise Partner equips portfolio managers, CIOs, investment advisers, private bankers, relationship managers to monitor their risks and build personalised investment proposals for their clients.

SOLUTION OVERVIEW

Digital transformation is not only about a great client experience. It is mostly about providing added-value services through digital channels to augment portfolio managers, advisers and relationship managers and help them better serve their clients. Smart Risk offers a user-friendly access to unique cutting edge mathematical models based on the latest API and cloud technology.

Smart Risk is a cloud-based modular solution designed to seamlessly integrate into the advisory journey. The Smart Risk suite consists of a set of modular APIs (Smart Risk APIs) and web apps (Smart Risk Decisions), as well as an underlying data connectivity platform to aggregate and consolidate multiple data sources.

Smart Risk is used by investment professionals in the asset and wealth management industry to support their risk monitoring, investment and advisory processes.

FEATURES & BENEFITS

Far from replacing the adviser, Smart Risk Decisions is a client-facing web application designed to bring more interactivity in the client/adviser relationship:

• Starting the advisory journey from the client’s wealth and expectations

• Helping advisers figure out the ‘next best action’ for each client depending on their profile, preferences and existing holdings

• Designing personalised portfolios with a global wealth approach

• Supporting a “4-hand” investment decision process: onboard the end-client with an interactive discussion

• Avoiding the black-box effect with a focus on transparency and explainability, which are the keys to a trustful adviser/client relationship

• Providing easy-to-use scenario simulations and analytics to understand the impact of investment decisions

Smart Risk Decisions helps advisers focus on their clients’ needs, build confidence through transparent and tailored investment proposal, hence develop new business by attracting and retaining clients.

Sophie Echenim President & CEO

sophie.echenim@raisepartner.com

Supporting the advisory process by enabling proactive, personalised and interactive investment proposals

Smart Risk Decisions was chosen by a leading European private bank to equip actors of the advisory value chain, from the CIO office to the relationship managers, to help them build personalised investment proposals to their clients with a high-end high-touch approach.

Equiping insurance portfolio managers with a digital solution to support their investment decision in a complex regulatory and accounting environment

Smart Risk Decisions for Insurance offers a userfriendly approach to run portfolio simulations and optimisations on a large number of instruments and constraints on SCR, accounting, etc.

Risk management at scale

Smart Risk APIs is used by several third-party vendors to complement their offering in terms of risk management and regulatory reporting, through our scalable SaaS solution offering risk analytics, stresstesting, regulatory risk calculation (for Solvency II, PRIIPS, AIFMD).

Artificial Intelligence WealthTech Market Map

An illustrative Market Map, highlighting the firms we know of in our ecosystem that have an AI offering and/or are incorporating AI into their offering/s.

Categories are based on the Business Needs from our online Solution Provider Directory.

SHOWCASE #7

AGENTS AI

Looking beyond the traditional approach to target operating model design.

Investment teams are rethinking their technology strategies to stay competitive and future-proof their processes. Traditional operating models are being replaced by new approaches that harness data, automation and AI.

Contributed by

Focus on foundations that underpin AI to futureproof investment teams.

ABOUT JACOBI

Jacobi streamlines multiasset investment processes with a unique, customisable cloud-based platform for portfolio design, analysis, and engagement.

The rise of AI agents and future-proofing technology for investment teams 7 AI Agents

By Tony Mackenzie, CEO & Co-founder, Jacobi

As Artificial Intelligence (AI) advances, investment teams are rethinking their technology strategies to stay competitive and future-proof their processes. Traditional operating models are being replaced by new approaches that harness data, automation and AI. This shift requires a fundamental redesign of the investment target operating model, focusing on flexibility, scalability, and clearer process delineation.

Looking beyond the traditional approach to target operating model design

Traditional operating models often centre on legacy processes and systems, such as PMS (Portfolio Management System), OMS (Order Management System), and IBOR (Investment Book of Record). While useful today, these platforms and ways of working are rigid. In fact, they are likely to limit a firm's ability to capitalise on emerging opportunities from AI.

Future operating models of investment technology should be less constrained and rely on three foundations:

1. Data and knowledge resources

2. Clear task definition

3. Leveraging automation through AI

This article expands on each of the above points in greater detail.

Crucially, the investment philosophy of the team should guide system design. Whether that focuses on a total portfolio approach, objective-based investing, factor-driven strategies, active return-seeking, or ESG considerations, the technology framework must enable and promote the philosophy - not restrict it.

Foundation 1

A comprehensive data strategy should also think of ‘knowledge’ as data

Most investment groups have made good progress on their data strategies. Data lakes and warehouses, security and product mastering, IBOR etc., are all important initiatives that will bear fruit in the future.

Looking further ahead, teams must widen what they consider ‘data’ and place equal emphasis on ‘knowledge resources’. This is akin to information used to train new employees in an investment organisation. For example, process documents, technical descriptions of investment models and tools, research management content and product collateral.

In an AI-driven world, knowledge resources provide crucial context, training and ways of working for an investment team. They must be organised, categorised and managed with a greater level of authority, plus be stored in a way that is accessible for AI tools to tap into.

No different to traditional data sets such as IBOR, better management of this information should be prioritised in any investment manager. It is particularly important for managers that wish to differentiate on the basis of their investment process in a competitive landscape.

Investment process tasks should be clearly defined, each triggerable via API

Investment processes are likely to become more complex as new data, models and methods become available. This makes the challenge of bringing together a coherent process and workflow increasingly difficult.

To manage this complexity, investment teams of the future will have neatly-defined tasks and processes, each systematised and triggerable via an Application Programming Interface (API). For example, tasks to optimise a portfolio, check portfolio positions for compliance, generate a risk analytic, submit a trade or produce a client report or review.

Every investment team does this today. But very few have such processes tightly delineated – and even fewer have them triggerable via APIs.

An API allows software applications to interact with one another. In the context of investment systems, this stretches beyond the simple exchange of data. Instead, they enable a variety of tasks to be performed programmatically that are otherwise performed by a user in a system.

The reality is most legacy platforms have significant catch-up to do on their APIs – i.e., investment teams need a much richer variety of API ‘endpoints’ for all the various actions and processes systems do. For instance, analytic calculations, operations procedures or retrieval of narrow bits of data.

A risk for many investment teams is they have bet big on closed-architecture platforms with limited API capabilities. Think terminal-based software with limited openness and that commercially penalises the extraction of data and outputs. That becomes extremely limiting in a world of immense AI potential – and certainly not future-proof. – and even fewer have them triggerable via APIs.

Foundation 2

Leverage AI agents to automate and chain processes together

While much AI discussion in the industry centres on siloed use cases, a bigger opportunity comes from chaining various processes and tasks together. The ability to infer the next step in a workflow and provide the necessary connectivity is what ‘AI agents’ enable.

Once data foundations are in place and tasks are clearly defined, investment teams will leverage ‘AI agents’ to connect and automate multi-step workflows. For example, chaining together a portfolio update process involving manager selection reviews, portfolio risk and factor sensitivity analysis, tactical allocation changes, compliance and investment policy adherence, trading, through to generating a client report.

Different AI agents may be used for different workflows. For example, agents specialised in compliance, investment research, trading and execution, or client portfolio management. In that way, AI agents become an extension of people’s roles and specialities, or may be more generalist.

While AI agents work horizontally to chain a series of actions, they operate in parallel with AI models working vertically. Indeed, the industry already has many vertical use cases at differing stages of development. Automated reporting, investment research, trading support, and data extraction are just some examples.

Many investment firms are also experimenting with different AI models and working through the complex information security, data and licensing implications. But importantly, the future will allow investment groups to plug and play different models for different purposes. Therefore, it’s important that emphasis is on the architecture to support multiple models, rather than betting big on a single approach.

To embrace this requires building agile teams looking into the future. That is, teams capable of managing AI-driven workflows while avoiding the pitfalls of shadow IT development. It also requires shining the spotlight on existing systems and any restrictions on ‘openness’. While many providers are investing in AI capabilities that exist natively within a product, that quickly becomes restrictive and erodes the control that an investment team has over their data and ability to unify a total process. Foundation 3

Focus on foundations that underpin AI to futureproof investment teams

To stay competitive, investment leaders should proactively prepare to integrate AI into their investment process. This requires equal focus on the foundations that underpin AI, rather than only the merits of different AI models and vertical use cases.

Leaders should be ruthless in the pursuit of organising data and widening that to include knowledge resources. This will provide crucial context and training for AI tools.

Operating designs should also favour open-architecture approaches, which should be considered as important as a system meeting its functional needs. After all, it takes only one task to be locked within a closed system to restrict AI agents from unifying a total process.

Tony Mackenzie

Leaders should be ruthless in the pursuit of organising data and widening that to include knowledge resources. This will provide crucial context and training for AI tools.

Jacobi Platform

SOLUTION SHOWCASE

At Jacobi, we’ve sat in our clients’ seats and managed multi-asset portfolios. We understand that technology needs to be built to fit each firm's unique investment processes, philosophy and data structures.

Jacobi has built a cloud-based technology that streamlines multiasset investment processes with a uniquely customisable platform for portfolio design, analysis and engagement.

Founded in 2014, Jacobi provides its technology to top-tier investors across the globe, including wealth managers, asset owners, asset managers and investment consultants. Jacobi is headquartered in San Francisco and has offices in London and Brisbane.

SOLUTION OVERVIEW

Jacobi’s cloud-based technology streamlines multi-asset investment processes with a unique, customisable platform for portfolio management, data analytics and engagement. White-labelled and highly flexible – users can tailor the platform by integrating their own code, models, data, analytics and applications. The Jacobi platform supports the many workflow priorities of the multi-asset investor. Bringing together the front-office to end-client workflows, Jacobi allows firms to efficiently manage portfolios against objectives.

Jacobi is built for client engagement, allowing firms to showcase their investment processes using interactive, white-labelled dashboards, apps and reports. Crisp visualisation features are combined with a powerful quantitative engine that allows for complex modelling to happen dynamically.

FEATURES & BENEFITS

Build portfolios with dynamic modeling and analytics

The Jacobi platform supports the many workflow priorities of the multi-asset investor. Bringing together the front office to end client workflows Jacobi provides an efficient workflow process to generate portfolios aligned to objectives

Integration of data, and expansion of workflows

Jacobi’s next generation private cloud infrastructure promotes open architecture and API connectivity. Integrate proprietary investment models, code and data structures that support your distinctive portfolio construction and analytics.

Improve client and stakeholder engagement

The Jacobi report designer provides an intuitive user interface that allows a user to construct a report with the click of a button. Data and analytics are dynamically integrated allowing for fast effective interaction and report generation.

Tony Mackenzie

CEO & Co-founder

Jacobi

info@jacobistrategies.com

www.jacobistrategies.com WEBSITE info@jacobistrategies.com

San Francisco. CA, United States

Asset managers

Who are looking for support with the build and distribution of customised processes and data at scale.

Wealth managers/advice firms

Who are looking to grow their businesses scalably but also require the implementation of efficient and client friendly processes.

Asset owners/pension funds

Who are after improved visibility and analytics over their pools of capital and better communications to clients around SAA and DAA decisions.

Investment consultants/OCIOs

Who want better means of showcasing their investment capabilities, better data connectivity and more meaningful analysis/narrative around risk.

INTELLIGENT WORKFLOWS SHOWCASE #8

Unlock growth opportunities through intelligent workflows

With more than 40% of their time consumed by administrative and backoffice tasks, wealth managers find themselves struggling to deliver on these demands while also growing their book of business. With the rise of intelligent workflows, all of this is changing.

Contributed by

Intelligent workflows, automated AI-processes that leverage data to optimise taxing or complex business activities, are revolutionising how wealth managers operate.

ABOUT ZEPLYN

Built by former Google engineers, Zeplyn is rebuilding the wealth management experience from an AI-native perspective.

Intelligent Workflows

Unlock growth opportunities through intelligent workflows

By Era Jain, Co-Founder and CEO of Zeplyn

It has never been more critical for wealth managers to invest their time where it matters most: deepening client relationships and delivering high-value guidance. As client expectations continue to increase, wealth managers are under enormous pressure to give their clients more seamless, personalised experiences. With more than 40% of their time consumed by administrative and back-office tasks, wealth managers find themselves struggling to deliver on these demands while also growing their book of business.

With the rise of intelligent workflows, all of this is changing.

Intelligent workflows, automated AI-processes that leverage data to optimise taxing or complex business activities, are revolutionising how wealth managers operate, freeing them from time-consuming manual tasks while equipping them with real-time business insights. They streamline operations and enhance the client experience by ensuring advisers can proactively focus on building trust, offering strategic advice, and identifying new and timely opportunities to serve their clients.

Firms cannot keep doing things the way they always have and expect to keep pace, let alone grow. Wealth managers who embrace intelligent workflows are making a strategic upgrade that enhances efficiency, improves client relationships, and strengthens their firm’s competitive edge.

Capturing the most relevant client details

Did you know that 60% of client data gathering happens during meetings? Historically, the burden of collecting that information rested on the wealth manager’s pen, leaving wide margin for error, missed details, and inconsistent record-keeping. It also prevented advisers from fully engaging with their clients during crucial relationship-building conversations.

AI has rendered manual note-taking obsolete. Intelligent workflow platforms can automatically capture structured meeting notes and action items from every client interaction – virtual or in-person. In doing so, wealth managers gain a comprehensive record of financial details, investment goals, life events, and personal preferences, ensuring no critical information is lost or misinterpreted.

Syncing notes and delegating tasks automatically

Even when an adviser takes notes by hand, less than 25% of meetings are properly documented and entered into the firm’s CRM, which is not hard to believe considering the time-consuming nature of manual note-taking and follow-up tasks. This disconnect leads to inefficiencies, missed followups, and unnecessary rework.

Intelligent workflows bridge this gap by automatically syncing client meeting notes and tasks to the firm’s CRM, immediately updating client records and equipping service teams with prioritised next steps. In addition to enhancing team collaboration, it can also promptly trigger additional follow-up workflows, cutting down wait time – for the team and the client – and ensuring a smoother, more efficient client experience.

Beyond streamlining daily tasks, intelligent workflows empower team leaders and firms with deep client and business insights, ones they have yet to have access to until now.

Reducing follow-up time with personalised emails

On average, wealth managers spend up to one and a half hours per client meeting handling followup tasks, including writing emails, summarising key points, updating CRM records, and assigning action items. More often than not, these follow-ups are delayed, diminishing their impact and reducing the momentum from the client interaction. AI can address all of this with ease.

Intelligent workflows can solve this issue by generating personalised, editable recap emails that advisers can send to clients within minutes instead of days. It can analyse meeting discussions, extract key details, and draft follow-up messages, saving valuable time while allowing wealth managers to maintain the human touch. This provides clients with timely, relevant communication that supports transparency and reinforces the value of the relationship.

Simplifying meeting prep

Meeting prep is one of the most time-intensive and expensive client engagement activities. The average adviser spends 239 minutes prepping for each client meeting, amounting to more than 75 hours per month on meeting prep alone!

Given that client meetings are among the most critical engagements, ensuring they are wellprepared is incredibly important. That is why relying on AI is key. Intelligent workflows can streamline this process by automatically generating comprehensive client recaps, meeting agendas, and key discussion topics. This gives wealth managers the ability to walk into every meeting fully informed with their client’s financial goals, action item statuses, life event details, and more – in less time and with a higher degree of accuracy and completeness. Not only that, but through intelligent workflows analysing client data, advisers can also be quickly equipped with multi-format content to deliver impactful, compliant recommendations tailored to the client’s needs. With this content, wealth managers are able to educate clients, deepen relationships, and deliver results in a timely manner and at scale.

Improving practice management with actionable insights

Beyond streamlining daily tasks, intelligent workflows empower team leaders and firms with deep client and business insights, ones they have yet to have access to until now.

Imagine if firms had visibility into client trends, firm productivity, and operational bottlenecks. They would be better positioned to refine their processes and improve client service outcomes. And that is what AI-powered workflows do. They allow firms to quickly identify patterns, optimise processes, and implement scalable, compliant solutions that enhance both efficiency and client satisfaction.

Moving into the future of wealth management

It is said that ‘Digital Darwinism’ is unkind to those who wait, and that is true for wealth management as well. AI is unlocking new growth opportunities, and those who stall will miss out on more than just the first-mover advantage. The future of wealth management belongs to those who embrace innovation, leveraging AI and other technologies to anticipate client needs and deliver personalised, high-impact experiences at scale.

If you’ve been thinking about taking the first or next step in your AI strategy, intelligent workflows are a great place to start. With low risk and high reward, they can enable you to maximise the use of client data – much of which you’re already collecting to meet compliance regulations.

Zeplyn

SOLUTION SHOWCASE

Built by former Google engineers, Zeplyn is rebuilding the wealth management experience from an AI-native perspective. Through its purpose-built workflow intelligence platform, firms are streamlining workflows and reducing manual work associated with client engagements by more than 90%. Advisors who use Zeplyn save 12+ hours a week on meeting prep, note-taking, drafting follow-up emails, creating and assigning tasks, updating the CRM while also fulfilling compliance requirements.

SOLUTION OVERVIEW

Zeplyn is rebuilding the wealth management experience from an AI-native perspective, freeing advisers to invest their time where they'll earn the highest return: building relationships and scaling their firm.

Zeplyn's intelligent workflow platform gives wealth managers greater insights and opportunities from every client conversation, unlocking a superior way for them to grow their advisory firm and serve their clients. Its flagship product, Zeplyn Meeting Assistant, transforms unstructured client conversions into highly accurate notes and uses the information within them to power additional actions throughout the adviser tech stack. By automating these administrative burdens while maintaining compliance requirements, Zeplyn saves financial advisers an estimated 10-12 hours per week.

FEATURES & BENEFITS

Purpose-built for wealth managers, Zeplyn streamlines adviser workflows, reducing time-consuming tasks from hours to minutes with unparalleled accuracy. It also surfaces client and advisory trends and insights, empowering firms to optimize practice management.

Zeplyn does this by using AI to capture real-time, structured meeting notes and action items during virtual or in-person client interactions. It then autodocuments key client financial details, investment goals, life events, personal preferences, and more into the CRM and powers additional workflows, such as auto-drafting personalised email recaps and equipping client service teams with prioritized next steps.

Zeplyn also streamlines how advisers prepare for meetings, gathering client information to automatically create client summaries and meeting agendas. In doing so, Zeplyn saves advisers 12+ hours a week on admin tasks, logs 100% of notes into the CRM, increases adviser face time by 50%, and empowers personalisation at scale—all while ensuring compliance and client data security.

DISCOVER MORE GET IN TOUCH

Era Jain

Co-Founder & CEO

Zeplyn era@zeplyn.ai

Wealth management firms often turn to Zeplyn in search of an AI-notetaker—and stay for the accuracy and value delivered through its intelligent workflows.

Sequoia Financial CTO had this to say: “We selected Zeplyn not just for their omni-channel note-taking capabilities but also for their long-term vision in supporting the over-all client meeting process—which is the most frequent and expensive activity of an RIA.“

A Zeplyn client said it best: “We had tried two other AI software solutions that were supposedly the best in the industry. There was an extreme amount of frustration because neither one of the AI applications could capture a real-life conversation with clients to identify meaningful and relevant details of the meetings. Then, we tried Zeplyn. I was amazed at how accurate and organized their notes were. My confidence in AI has been restored!”

CLIENTS

Family Offices, Financial Advisers, Insurance-based, RIAs, Brokers-Dealers

CLIENT LOCATIONS

CLIENT TYPE North America, Oceania, Western Europe

FUTURE PROOF SHOWCASE #9

A decade ago, AI’s role in investment advisory and portfolio management seemed like a far-off idea...

Today, thanks to major advancements in Big Data, chatbots and deep learning, AI is no longer speculative; it’s essential. And at the vanguard of this revolution, Generative AI (GenAI) hails as a key enabler of digital change, poised to redefine how financial firms operate.

Contributed by

Artificial Intelligence is no longer just a buzzword – it’s here, actively transforming wealth management as an essential driver of efficiency, innovation, and growth.

ABOUT OBJECTWAY

Objectway is an international wealth, banking and asset management software provider empowering clients to embrace their future challenges while providing great performance today.

From hype to reality: how AI is reshaping wealth management

By Miroslav Petrov, Product Director, Objectway

Artificial Intelligence (AI) is no longer just a buzzword –it’s here, actively transforming wealth management as an essential driver of efficiency, innovation, and growth.

Only a decade ago, AI’s role in investment advisory and portfolio management seemed like a far-off idea, but today, thanks to major advancements in Big Data, chatbots and deep learning, AI is no longer speculative; it’s essential. And at the vanguard of this revolution, Generative AI (GenAI) hails as a key enabler of digital change, poised to redefine how financial firms operate.

However, as with any new technology, the arrival of AI raises a fundamental question: is it the next grey rhino – a looming risk hiding in plain sight – or a (partially) unexploited wellspring of growth and opportunity?

The answer? It’s both. Early adopters have already unlocked the power of GenAI to enhance operational efficiency, deliver tailored financial products and advice, bolster market reach, facilitate smarter decision-making, and open doors to new value propositions. Yet, widespread adoption remains a hurdle, with firms navigating the complexities of security concerns, ethical dilemmas and the unknowns of long-term sustainability.

Why some firms are hesitant to dive in

Despite the surge of enthusiasm surrounding AI’s potential, many firms are, in fact, holding back. According to a 2024 Cisco global study, over 25% of organisations have banned outright the use of GenAI due to concerns regarding data security and privacy.

Additional key challenges include the risk of inaccuracies, inherent biases in AI algorithms, potential misuse of AI technologies, and issues related to the explainability of AI-driven recommendations. Ethical and workforce concerns add another layer of hesitation, with debates surrounding job displacement and the moral responsibility of financial institutions.

The opacity of AI’s decision-making – often referred to as the “black box” effect – makes it challenging for firms to explain financial outcomes to clients and investors. This perceived lack of transparency may raise compliance red flags, especially as regulators push for greater clarity in AI-driven processes. Cost considerations weigh heavily on decision-makers as well. AI integration requires significant upfront investment in technology and talent, leaving firms reluctant to commit without clear, measurable benefits.

While regulatory hurdles remain a concern, they’re not seen as insurmountable. The business conduct requirements for further AI deployment include implementing rigorous quality assurance processes and maintaining comprehensive records that document AI usage related to the provision of investment services. Costs are gradually coming down, making AI solutions more accessible to businesses of all sizes and helping firms stay competitive.

Where AI is making the biggest impact

GenAI is therefore already effectively transforming wealth management in fundamental ways. A recent Celent study shows that 60% of wealth managers across the globe are either actively using or exploring use cases with GenAI. The McKinsey Global Institute (MGI) projects that GenAI could generate economic impact ranging from US$200 billion to US$340 billion annually in the banking sector, equating to 4.7% of total industry revenues, primarily through enhanced productivity. As AI adoption continues to ramp up, progress in governance, ethical considerations, and legal frameworks is creating a more structured environment for its responsible and effective integration in financial services.

Driven by its intuitive usability and potential to reinvent workflows, GenAI is now influencing core functions such as product development, customer operations, risk assessment, compliance, marketing, and sales. Initially, projects were developed on a case-by-case basis, but now there’s a clear trend towards centrally managed GenAI systems that have access to live data and core applications, capable of consuming and generating AI-created content and recommendations.

The client experience is also getting a complete overhaul. From prospecting and onboarding to personalised financial advisory services, GenAI is seamlessly adapting to the evolving expectations of today’s clients, especially as technology usage continues to shift generationally, ensuring continuous innovation and user engagement.

In terms of operational efficiency, AI is eliminating the inefficiencies that have long plagued wealth managers: time-consuming administrative tasks, switching between multiple platforms, and limited client interactions. AI-driven solutions are streamlining workflows by consolidating internal data, analysing information, improving document processing efficiency, and automating routine data entry and reporting functions. These improvements translate into cost reductions, stronger client relationships, and enhanced business growth.

Risk and compliance are also being redefined. AI systems now proactively flag discrepancies and potential issues, enabling firms to stay ahead of complex regulatory requirements, while ensuring seamless compliance checks. This approach safeguards documentation accuracy while keeping costly penalties at bay.

Meanwhile, investment strategies are evolving, as AIpowered analytics uncover new market opportunities and optimise portfolio management, helping firms stay ahead of trends.

And last, but not least, GenAI’s functionalities – including content enhancement, translation, semantic analysis, and conceptual creation – are redefining client engagement strategies. These capabilities not only capture user attention and foster ongoing dialogue but also improve the accuracy and relevance of data. AI’s contextual awareness is further enhancing financial communications by summarising complex market trends and documentation and providing insights and support in an easily digestible manner. It acts as a collaborative partner, either augmenting workflows by enhancing human capabilities or automating tasks to improve efficiency – all while preserving the personal touch clients demand.

The path forward: balancing innovation and trust

The future of AI in wealth management extends far beyond just automation – the future is about intelligence, adaptability, and human-AI collaboration. AI is rapidly evolving into a multimodal force, integrating text, images, audio, video, and code generation to create richer interactions in financial services, allowing for more engaging client experiences. As it merges with API, cloud computing and Distributed Ledger Technology (DLT) – it will increasingly enable seamless integration and automation, allowing firms to achieve operational efficiency, build stronger client relationships, and futureproof their business models.

Firms now therefore face a critical choice: embrace AI and harness its potential, or risk falling behind in an industry where digital transformation is ever accelerating.

Looking ahead, AI’s integration into wealth management is progressively set to redefine completely both innovation and operational efficiency. It transforms knowledge into actionable intelligence, marking the beginning of a new era of digital collaboration.

As wealth management firms navigate this evolution, the balance between innovation, regulatory compliance and trust will be key to shaping the future of financial services and ensuring long-term success in an increasingly AI-driven world.

Miroslav Petrov

Objectway Platform

SOLUTION SHOWCASE

For over 30 years, Objectway has partnered with banks, asset managers and wealth managers to grow their business while improving the financial wellbeing of their clients.

As a global Top 100 FinTech provider (IDC FinTech Rankings), Objectway manages €1 trillion in assets and supports more than 100,000 investment professionals who manage €700 billion in AUM for more than 5 million investors.

With more than 250 clients across EMEA, North America and Canada, Objectway drives growth through innovation. Its 800+ professionals operate from nine regional hubs, delivering scalable, flexible solutions that adapt to global players and regional champions expanding across clients, geographies and the value chain.

SOLUTION OVERVIEW

The Objectway Platform provides a flexible, personalised solution design, leveraging a comprehensive suite of end-to-end digital and service capabilities seamlessly integrating front-to back-office operations into one as-a-service growth platform, including SaaS, BPaaS and EaaS, to deliver key benefits across the entire value chain.

The Objectway Platform is made of two distinct yet interoperable technology layers:

• Client engagement, adviser & investment management solutions: enhancing client engagement, front-office productivity, and optichannel interaction. We offer an online portal, mobile apps for digital banking, and investor self-service functionality. Relationship managers can efficiently handle discretionary portfolios, advisory services and execution-only investments, with robust reporting, suitability reviews, risk management and compliance monitoring.

• Wealth, asset & banking operations solutions: supporting securities management services, cash and payments, FX and treasury, credits and loans, along with STP (straight through processing) automation, regulatory and compliance reporting, including processing and execution of open and alternative funds.

USE CASES

Enhancing client retention with AI-powered financial advice:

Client defection remains a significant challenge for financial advisors, often driven by dissatisfaction with investment performance, service quality, collaboration, or costs. Addressing these concerns proactively is essential to maintaining strong client relationships. Objectway has tackled this issue by leveraging AI to predict which clients are at risk of leaving, enabling advisors to take timely action.

By analysing both bank-specific data—such as investment amounts, products offered portfolio performance over time, and transaction history— alongside publicly available data like market trends and consumer confidence, the AI system evaluates and compares key risk factors to identify at-risk clients. Transparency in AI-based decision-making is ensured through game-theory-based methods that explain why a client is flagged as high risk. Advisers receive prioritised client lists, including weighted risk factors, allowing for targeted, personalised interventions. By harnessing AI-driven insights, financial advisors can enhance service quality, deepen client relationships, and improve retention - turning predictive analytics into a strategic advantage.

COMPANY

Objectway

Miroslav Petrov

Product Director

Objectway

miroslav.petrov@objectway.com

Optimising back-office operations with AIpowered automation: Leveraging computer vision and intelligent document processing, data extraction from contracts, order forms and regulatory documentation is automated with high accuracy, enhancing back-office efficiency and freeing up valuable human recourses for more strategic tasks.

Building on this foundation, Objectway is exploring the use of GenAI and Large Language Models (LLMs) to extract key information from complex regulatory documents. Unlike more controversial AI applications, this use case aligns well with LLMs’ strengths— handling intricate language structures while ensuring compliance and accuracy.

This overall approach streamlines operations while improving regulatory adherence, transforming traditionally manual processes into intelligent, automated workflows.

www.objectway.com

WEBSITE marketing@objectway.com

EMAIL Milan, Italy

HQ 1990

FOUNDED

EMPLOYEES

501 - 1,000

CLIENTS

101 - 500

EAMs, Bank Wealth Managers, Family Offices, Advisers, Insurance-based, Trust & Fiduciary, Investment Platforms

CLIENT LOCATIONS

CLIENT TYPE Africa, Caribbean, Central America, Europe, Middle East, North America

GENAI PORTFOLIO SOLUTIONS SHOWCASE #10

Scaling personalised wealth management with GenAI

Wealth managers operate in an increasingly complex landscape where clients expect tailored, data-driven investment insights. This is where GenAI portfolio advisory solutions come into play, fundamentally changing how advisers operate and engage with clients.

Contributed by

gptadvisor .com

The transformative power of GenAI in wealth management lies in its ability to scale personalisation effectively.

ABOUT GPTADVISOR

GPTadvisor is an AI-native startup founded in 2023 with the mission of harnessing the power of generative AI to transform the wealth management industry.

GenAI Portfolio Solutions

GenAI portfolio solutions: how GPTadvisor generates high-quality, personalised insights for all of your clients

By Nacho Díaz de Agandoña, Managing Director and Founder, GPTadvisor

Wealth managers operate in an increasingly complex landscape where clients expect tailored, data-driven investment insights. Yet, traditional advisory processes often fall short of delivering truly personalised services at scale. In fact, under 15% of investors feel that their service is truly personalised (Accenture, 2024). This is where GenAI portfolio advisory solutions come into play, fundamentally changing how advisers operate and engage with clients.

Scaling personalised wealth management with GenAI

The transformative power of GenAI in wealth management lies in its ability to scale personalisation effectively. By leveraging AI, advisers can deliver tailored, high-quality financial insights to a broader client base, ensuring that each individual receives data-driven, relevant advice regardless of portfolio complexity. AI seamlessly integrates client data, market trends, and financial history, allowing wealth managers to engage with clients in a more strategic and personalised manner. The key advantages include:

• Enhancing adviser productivity: Advisers can dedicate more time to strategic decisionmaking and relationship-building instead of manual data processing.

• Providing personalised services at scale: AI ensures that every client receives high-quality insights, fostering stronger engagement and trust.

• Generating dynamic, client-centric reports: AI transforms raw financial data into comprehensible, investor-friendly reports that contextualise insights within unique financial goals and market conditions.

• Driving revenue growth: By automating portfolio analysis and reporting, firms can serve a larger client base without compromising service quality.

• Empowering proactive wealth management: AI anticipates market trends and client needs, enabling advisers to deliver proactive, forwardthinking financial strategies.

AI models, while powerful, can generate incorrect data or unreliable recommendations if not properly governed This is why leading firms must prioritise AI reliability to maintain trust and credibility with clients.

The challenge: trust and reliability in Gen-AI advisory

While GenAI presents an opportunity to transform wealth management by scaling personalisation, one critical challenge remains: ensuring reliability and accuracy. AI models, while powerful, can generate incorrect data or unreliable recommendations if not properly governed. This is why leading firms must prioritise AI reliability to maintain trust and credibility with clients.

Without a structured approach to data integrity and governance, GenAI can introduce risks such as data hallucinations, regulatory non-compliance, and flawed insights. In an industry where trust and precision are paramount, wealth managers must adopt AI solutions that combine efficiency with verifiable accuracy.

To address the key challenge of AI reliability, GPTadvisor incorporates the Trust Layer, a reliability framework that ensures full auditability and traceability of AI-driven insights. By detaching generative AI models from data management workflows, the Trust Layer guarantees that all calculations, metrics, and data outputs are accurate and verifiable. This eliminates the risk of AI hallucinations, allowing advisers to confidently use AI-generated content while maintaining full control over financial insights.

Key benefits include:

• Full data traceability: Every insight is backed by verifiable data sources, ensuring accuracy and transparency.

• Eliminating AI hallucinations: AI focuses on reasoning, explaining, and enhancing human understanding rather than generating unverified information.

• Regulatory compliance: AI outputs align with industry regulations, safeguarding firms against compliance risks.

• Enhanced adviser oversight: Advisers maintain full control, with the ability to audit and refine AI-generated insights before client engagement.

By combining GenAI’s efficiency with GPTadvisor’s Trust Layer, firms can scale personalised financial services while ensuring the highest levels of accuracy and client confidence.

Let AI do the heavy lifting

GPTadvisor operates as an AI-driven agentic tool that streamlines the entire portfolio advisory process. Advisers simply connect their portfolio data, and GPTadvisor takes over from there, handling all aspects of data analysis, insight generation, and content creation. Once the portfolio data is connected, advisers can configure and personalise how GPTadvisor generates insights and reports for their clients. They can set preferences for the depth of analysis, the tone of narratives, and specific market insights to be included. GPTadvisor then autonomously processes the data, synthesises market trends, and generates client-ready outputs with a high degree of personalisation.

GPTadvisor produces a comprehensive suite of personalised advisory outputs, including:

• In-depth portfolio analysis and comparisons: AI evaluates asset allocation, risk exposure, and performance trends to generate actionable insights in real-time.

• Market impact commentaries: Automated assessments of how current market conditions affect individual client portfolios, enabling more proactive and informed decision-making.

• Tailored executive summaries: AI distils complex financial data into clear, insightful summaries that advisers can use to enhance client conversations.

• Concise responses to common client concerns: AI anticipates key investor questions and provides detailed yet digestible explanations.

• Branded, client-ready PDF reports: Professionally designed documents that enhance engagement and improve communication with clients.

Advisers maintain complete control over how the AI operates, from setting the tone and style of reports to selecting the sources of information (e.g., market research, articles) that GPTadvisor considers. They also have the ability to refine and make final adjustments before using the AI-generated outputs in client engagements.

After AI-driven reports and insights are generated, advisers simply review and finalise the content before using these resources in their client engagements. This ensures that every interaction is backed by high-quality, data-driven insights while significantly reducing the time required for preparation. By letting AI do the heavy lifting, advisers can dedicate more time to delivering strategic, relationship-driven financial advice.

The future of AI in wealth management

The wealth management industry is undergoing a profound transformation, and GenAI is at the heart of this change. By leveraging GenAI solutions like GPTadvisor, advisers can enhance their efficiency, provide truly personalised insights, and scale their businesses with confidence. This technology is not just about automation; it’s about enabling wealth managers to deliver a level of service that was previously unattainable. As client expectations continue to evolve, firms that embrace Gen-AI will be the ones leading the way, offering smarter, faster, and more customised portfolio advice. The future of wealth management is here, and Gen-AI is making it more intelligent and client-focused than ever before.

For those looking to explore how AI-driven portfolio advisory can revolutionalise their wealth management practice, visit us on gptadvisor.com.

Nacho Díaz de Agandoña

GPTadvisor

SOLUTION SHOWCASE

GPTadvisor is a wealthtech company founded in 2023 that develops AI agents to streamline wealth management workflows. Designed for the industry, its agents possess deep expertise, understanding the complexities of financial services. Built for traceability, they ensure full auditability of information, sources, and processes. By processing a vast knowledge base—including market data, assets, and proprietary insights—they generate unique analyses and value-added content to refine investment strategies. Already serving over 20,000 advisers across Europe, the UK, and LATAM, GPTadvisor is shaping the future of wealth management with AI-driven solutions, empowering wealth managers to enhance productivity and client engagement.

SOLUTION OVERVIEW

In today’s fast-evolving wealth management landscape, delivering a superior client experience is more critical than ever. Advisers must navigate vast amounts of financial data, personalise interactions, and communicate complex investment insights clearly. All while maintaining efficiency and compliance. However, traditional processes often limit their ability to scale high-quality client engagement.

GPTadvisor addresses this challenge by offering a suite of generative AI agents designed to streamline workflows and enhance client interactions. These AI-powered agents automate key advisory tasks, transforming complex data into clear, actionable insights. They provide wealth managers with an intuitive, natural-language interface to generate personalised reports, compelling product explanations, and persuasive investment proposals effortlessly.

By leveraging accurate market data, portfolio insights, and proprietary knowledge, GPTadvisor ensures timely, proactive, and tailored communication. This enables advisers to focus on building stronger relationships, delivering value-added insights, and improving client trust and satisfaction. With intelligent automation, GPTadvisor redefines client engagement in wealth management.

FEATURES & BENEFITS

GPTadvisor’s suite of generative AI agents is designed to streamline wealth management workflows while enhancing client engagement. Built with reliability and traceability at their core, these agents ensure fully accurate financial information, enriched with AI-driven reasoning and adaptability to provide unique insights and high-value resources that keep advisers ahead in the industry.

Key agents include Financial Education Agents, which dynamise investor literacy; Product Information Agents, which simplify investment products; Portfolio Agents, which generate reports, comparisons, and analyses; and Sales Agents, which craft persuasive pitches and new investment proposals.

These agents equip advisers with valuable resources like executive summaries, market impact commentary, and research to stay informed. For client engagement, they provide personalised sales pitches, pre-sales presentations, branded PDFs, ensuring tailored engagements. They also generate investment proposals, product comparisons, and financial education content, while performing compliance checks, market alignment analysis, and portfolio monitoring. With reliable, auditready intelligence, GPTadvisor empowers advisers to deliver superior client experiences.

DISCOVER MORE GET IN TOUCH

COMPANY

GPTadvisor

Nacho Díaz de Argandoña

Managing Director and Founder

GPTadvisor

ndda@gptadvisor.com

GPTadvisor’s AI agents are designed to support advisers across the entire wealth management lifecycle, enhancing efficiency and client engagement at every stage. These agents empower advisers to excel in pre-sales, providing insightful financial education and product explanations; in sales, by crafting personalised pitches and investment proposals; in investment modeling, by delivering datadriven portfolio proposals; in reporting, with compelling summaries and comparisons; and in proactive communication, by generating timely market insights and responses to investor concerns.

Leading financial institutions leverage GPTadvisor’s Gen-AI Portfolio Solutions to enhance advisory services. Asset managers use AI-driven agents to create compelling model portfolio presentations, summaries, and resources for advisers and clients. Private banks improve adviser-investor communication, increasing personalization and engagement frequency. Retail and universal banks deploy AI agents to help clients better understand their portfolios and the impact of market events on their investment strategies, democratising wealth management.

www.gptadvisor.com

WEBSITE ndda@gptadvisor.com

EMAIL Madrid, Spain LOCATION 11-20

CLIENTS

EMPLOYEES 11-20

EAMs, Bank Wealth Managers, Family Offices, Financial Advisers, Insurance-based, Digital Wealth Platforms, Neobanks, Robo Advisors, Custodians, IFA platforms CLIENT TYPE

CLIENT LOCATIONS

Central America, Middle East, North America, South America, Western Europe

SHOWCASE #11

AI FOR THE NEXT GEN

AI’s successful implementation hinges on robust cloud infrastructure

Even as the proportion of wealth managers moving business-critical workloads to the cloud increased from 57% to 69% in the past year (according to Celent), only 16% of wealth managers in EMEA have reached full cloud maturity.

Contributed by

www.

In wealth management, AI - and generational shifts - are fundamentally transforming how services are delivered.

ABOUT AWS

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud, offering over 200 fully featured services from data centers globally.

AI For The Next Gen

AI for next-generation wealth management – new approaches for new customers 11

By Chris McDonald, Capital Markets Specialist, AWS

Navigating generational shifts in wealth management

As generations transform, so too does wealth management. With Millennials approaching their peak earning years, and Gen Z projected to become the richest generation in history, capital will increasingly be controlled by younger customers who crave more digital, hyper-personalised experiences from their advisers. To meet the needs and preferences of these customers – and to capture the coveted mass affluent market while they’re at it – wealth managers are looking to harness AI to proactively deliver great customer experiences, help advisers work more efficiently, and serve more customers than ever before.

AI’s successful implementation hinges on robust cloud infrastructure. Yet even as the proportion of wealth managers moving business-critical workloads to the cloud increased from 57% to 69% in the past year (Celent), only 16% of wealth managers in EMEA have reached full cloud maturity (PwC).

Against this backdrop, wealth management firms are considering what’s possible now with AI, a holistic approach to their AI and cloud journeys, and how they can align AI implementation with ongoing cloud migration.

Delivering more personalised investment advice

The release of consumer-facing Generative AI (GenAI) tools kicked off an era of excitement about GenAI, with early wealth management adopters using it for content search and summarisation. Since then, innovation has continued, with Retrieval Augmented Generation (RAG) helping make GenAI more cost-effective, accurate, trustworthy, and tailored to unique business needs. New offerings, tools, and mechanisms are being made available to promote the responsible use of AI.

Recent advances show this is just the beginning for wealth management. At AWS, new tests demonstrate that AI can potentially enable wealth management advisers to deliver portfolio recommendations that are 35% more personalised to their customers. Wealth managers can combine Amazon Bedrock (our service for building GenAI applications) and Amazon QuickSight (our business intelligence service) to process complex data from multiple sources, including the customer’s investment policy statement, the customer’s portfolio, recent earnings reports and call recordings, real-time market data, and more. This processing happens in near real time, and is backed by a deep set of cloud security tools. The output is an analysis of the portfolio’s performance, which the adviser can use to explain to the customer how macroeconomic factors, industry trends, security-specific events, and investment decisions contributed to performance. The adviser is also presented with an assessment of the portfolio’s current positioning. If portfolio weightings have shifted due to price changes, the adviser will receive rebalancing recommendations on how to bring it back in line with the customer’s risk tolerance, time horizon, and preferences.

By combining GenAI and business intelligence, this solution does the heavy lifting on portfolio analysis and market research, allowing advisers to dedicate more time to relationship building and understanding customer needs – and to growing assets under management by successfully serving more customers.

Improving customer onboarding with AI agents

One of the most exciting areas of AI for wealth managers is AI agents, which enable GenAI applications to break down user requests, gather relevant information, and efficiently complete tasks by seamlessly connecting with company systems, APIs, and data sources. Agentic AI has the potential to significantly improve many types of workflows within wealth management. For example, AI agents can be used to streamline and improve the customer onboarding process by simultaneously handling document gathering, contract signing, and compliance checks, all while providing updates and visibility for both the customer and the adviser. This saves advisers time, and can reduce the abandonment rate during the customer onboarding process by minimising friction and frustration. Beyond customer onboarding, AI Agents can execute adviser and customer workflows around goal setting, financial planning, investment management and advice, and ongoing communications.

Migrating to the cloud to harness AI’s potential

These exciting developments all run in the cloud. Cloud service providers like AWS deliver the necessary computational power, data storage, and scalability that AI applications demand. Cloud platforms also enable firms to process enormous amounts of structured and unstructured data, train complex AI models, and deploy AI solutions across their organisation.

This presents a practical challenge: with most wealth managers not yet completely migrated to the cloud, how can they begin implementing AI solutions while still developing their cloud capabilities? The answer lies in finding ways to align AI implementation with ongoing cloud migration:

Start with data

Data strategy is fundamental to this effort. Wealth managers need a well-thought-out data management and analytics plan not only to fully leverage their huge volumes of data, but also to create AI applications. The value of what wealth managers can derive from AI depends on the quality of their data, their ability to unify data from numerous internal and external sources, and their ability to seamlessly access it. Wealth managers can consider replicating or migrating on-premises data to the cloud, and then pulling data from other sources to the cloud as well. This cloudbased data foundation will serve as the backbone of AI readiness. It can take the form of a data lake, a data warehouse, or potentially a data mesh architecture, which unites disparate data sources and links them together through centrally managed data sharing and governance guidelines.

Focus on security

Security is “job zero” for maintaining customer trust. For advisers and customers to engage with AIpowered tools, they must be confident that sensitive personal and financial information is secure. Successful AI implementation requires robust data governance frameworks, clear privacy policies, and stringent security controls.

Take a phased approach

Firms can begin by identifying areas already migrated to the cloud, such as customer on-boarding or account management, as initial targets for AI enhancement. This approach allows organisations to gain experience with AI implementation while continuing their broader cloud transformation.

AWS provides many services to help businesses deploy AI, including Amazon Bedrock (for building GenAI applications), Amazon Nova foundation models, Amazon SageMaker AI (our machine learning model service), Amazon Q (a GenAI-powered assistant), and Amazon Connect (an AI-powered contact centre, which can be augmented with GenAI via Amazon Q). Each of these services is part of the world’s most secure, scalable, and resilient cloud.

Thriving as demographics shift

Today, AI is being applied not only for search and summarisation, but also for delivering insights, suggesting next best actions, and even taking action to execute complex workflows. For the wealth management industry, this represents more than just a technological shift. AI is fundamentally transforming how wealth management services are delivered and experienced, whether by helping advisers deliver tailored investment advice faster, or by improving the customer experience. Successful firms – wherever they are on their cloud journey – will be those that find ways to embrace AI, while prioritising data and security, in order to deliver hyper-personalised experiences to younger generations of customers, at scale.

Chris McDonald

Amazon Web Services

SOLUTION SHOWCASE

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud. Millions of customers - including the fastest-growing startups, largest enterprises, and leading government agencies - use AWS to be more agile, lower costs, and innovate faster.

SOLUTION OVERVIEW

AWS for financial services is a pioneer at the intersection of financial services and technology, enabling our customers to optimise operations and push the boundaries of innovation with the broadest set of services and partner solutions – all while maintaining security, compliance, and resilience at scale. Tens of thousands of financial services firms around the world, from the fastest-growing FinTechs to systemically important financial institutions, are redefining their future on AWS.

FEATURES & BENEFITS

Evolving customer expectations, breakthrough technologies like generative AI, new regulations, and disruptive business models have made cloud migration a strategic imperative for financial services. Financial services institutions turn to AWS to transform their mission-critical operations, create hyper-personalised customer experiences, explore new business models, and capture the value of data and generative AI – all while meeting ever-changing regulatory requirements.

Today's tech-savvy customers expect wealth managers to offer customised advice and frictionless experiences, including value-added services like financial goal planning and retirement advice. To meet these expectations and stay competitive, wealth management firms are turning to AWS for innovative digital solutions that help them reimagine the customer experience and provide enhanced, personalised services.

For wealth managers, AWS offers industry-specific experience and expertise; the most secure, scalable, and resilient cloud; the broadest set of services and capabilities; and a mature and broad community of trusted financial services partners.

aws.amazon.com

With AWS, wealth managers can build resilient wealth management platforms. AWS services and GenAI enable workflows across the entire wealth management value chain to attract, engage, and retain customers. With AWS, wealth managers can deliver hyper-personalised prospecting, lead generation, marketing, onboarding, and engagement experiences to their customers; empower advisers with next best actions; enhance portfolio construction and management; and strengthen middle and backoffice functions.

linkedin.com/showcase/aws-forfinancial-services/posts/

The Wealth Mosaic is a curated online marketplace directory of solution providers and solutions relevant to the business needs of the global wealth management community.

Our online directory includes 3,000+ technology and related Solution Provider profiles and well over 6,950+ categorised solutions from across this community. This resource is supported by an extensive library of knowledge resources including New & PR, video and video interviews, podcasts & webinars, white papers & thought leadership, solution information and more.

2 Marketplaces

39 Business needs

3,000+ Business profiles

6,950+ Solution profiles

6,000+ Knowledge resources

ABOUT THE WEALTH MOSAIC

TWM - built for a dynamic wealth management marketplace

The digital marketplace for wealth management

The Wealth Mosaic (TWM) is an increasingly well-known and highly-regarded knowledge resource, closing the gap between the evolving business needs of wealth management businesses across the world and the growing marketplace of technology and related solution providers selling into the market.

The Wealth Mosaic is a UK-headquartered online solution provider directory and knowledge resource, focused specifically on the wealth management industry. Built around a curated and constantly growing and evolving directory of solution providers to the wealth management sector across the world, our business is founded on five core principles that make us different from other offerings in the market:

• Wealth management-focused

• Directory-first

• Research-led

• Online-first

• Accessible

Behind this report, the engine room of our business in delivering all of the above is our website. This is available to any user 24/7, 365-days a year. As of April 2025, our website hosts over 3,000+ solution provider profiles and hosts over 6,750+ solution profiles from these businesses. Each of these solutions is tagged to at least one of the 39 headline Business needs categories across our first two live marketplaces (Technology and Data, and Consulting, Research and

Support Services). These Business needs categories create the first level of filtering around our Solution Provider Directory.

Alongside our core directory focus, we continue to add and further develop the content, knowledge resources, and tools within our platform to support the user in their discovery, learning and engagement process.

For Wealth Managers

For wealth managers, the buy side of our marketplace, TWM is designed to enable discovery of key solutions, solution providers and knowledge resources by specific business needs.

For Solution Providers

For solution providers and vendors, the sell side of our marketplace, TWM exists to support the positioning, exposure and business development needs of these firms in a more complex and demanding market.

Our offering pivots around the following six core components which can be used individually or pieced together to support your needs.

As part of our goal of creating a deep knowledge resource for the wealth management sector, alongside the maintenance and development of our SPD, we offer the market six core ways of working with TWM:

• Membership

• Content

• Reports

• Campaign

• Events

• Research & Insights

Offering a supporting fuel to help drive the engine that is the SPD, each of these service pillars also features standalone service offerings available to both wealth managers and solution providers to support their specific business needs whether that be positioning, exposure, insight, learning, networking or more.

If you are interested in discovering more about our offering, projects and plan for 2025, please don't hesitate to get in touch. You can access more detail on how you can work with us in 2025 in our Media Kit below.

Stephen Wall Founder stephen@thewealthmosaic.com

Mungo Hamlet Director, Head of Marketing & Operations mungo@thewealthmosaic.com

Marc Bussell Head of

Content

marc@thewealthmosaic.com