From data to value: AI and analytics in wealth management

RESEARCH PAPER

From data to value:

AI and analytics in wealth management Driving competitive advantage in wealth management through AI-powered analytics and actionable data insights.

Contents

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Executive summary

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Introduction: the shift from data consumption to value creation

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How firms use data today: the current landscape

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Enablers of the shift to value creation

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Measuring ROI: beyond cost savings

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Challenges and risks

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The road ahead: strategic priorities for leaders

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Conclusion: building the wealth firm of the future

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About the research

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Executive summary

W

ealth management is at a pivotal juncture. Clients today expect not just performance, but seamless digital tools, deeply personalised advice, and an experience aligned with the best of technology-driven firms. At the same time, wealth managers face margin pressure, intensifying regulatory demands, and disruption from FinTechs, neobanks and platform players. In this climate, simply providing data dashboards or portfolio statements is no longer enough.

Embedding advanced analytics, tools powered by Artificial Intelligence (AI), and real-time data have moved from being nice-to-have to being core to enhanced engagement, retention, and achieving measurable Return on Investment (ROI)1.

Firms that harness data not just for reporting but for insight-driven action will set themselves apart.

This paper explores: • • • • • •

The current state of data usage in the industry How leading firms are shifting from data consumption to value creation The key enablers driving this transformation Ways to measure ROI beyond cost reduction The challenges and risks that need to be managed Strategic priorities for wealth firms aiming to lead rather than follow

Along the way we reference LSEG’s wealth management data analytics frameworks and materials and complement these with other industry data.

In this report, ROI is defined as including strategic value, resilience, scalability, and innovation, not only financial return.

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AI and analytics in wealth management | Executive summary

Introduction:

The shift from data consumption to value creation H

istorically, the wealth management industry has been data-rich but insight-poor. Firms have accumulated vast volumes of market data, fund information, client profiles, transactional histories and regulatory-reporting inputs. Yet the translation of that data into actionable insight that drives behaviour or business outcomes, often remains incomplete.

Many organisations operate in a mode of information delivery rather than outcome enablement. They deploy dashboards of performance, risk, holdings, and compliance reports: all useful, but still under-leveraged.

The challenge is that static reports (for example, monthly portfolio breakdowns or performance metrics) provide visibility, but they don’t always empower advisers or clients to act with speed and context.

The opportunity now is clear: AI and Machine Learning (ML) techniques allow firms to transform previously passive data into predictive, personalised and actionable insights. For example: identifying a client likely to churn based on behavioural cues; recommending a portfolio rebalance tailored to lifetime goals rather than just benchmarks; or surfacing market-sentiment signals from news and social media that human advisers would struggle to monitor at scale.

The key question becomes: How can firms turn technology spend and data infrastructure into measurable ROI and genuinely impactful client outcomes? In other words: not just ‘how much data can we hold or display?’, but ‘what value are we bringing or extracting from it?’.

In wealth management we are shifting from simply providing data to enabling decisions.

AI gives advisers the ability to anticipate client needs before they ask.

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How firms use data today: the current landscape I

n many wealth management firms, the predominant approach to data is centred on reporting, dashboards, and compliance. Advisers and back office teams rely on business-intelligence tools that aggregate holdings, performance, risk metrics, or regulatory-reporting outputs. The mindset has been ‘let’s collect and display the data we have’.

such as retail, e-commerce, consumer finance, or other popular content streaming platforms including Spotify and Netflix, amongst others. According to LSEG’s research, 68 percent of investors expect their digital experiences with wealth management firms to match those of leading technology companies.

Firms may maintain separate systems for client onboarding, portfolio management, reporting, CRM, compliance and market-data feeds. These systems often run in siloes, requiring manual reconciliation or cross-system integration.

A broader LSEG insight finds that 44 percent of wealth managers say relationship-management is the highestpriority area where AI can have an impact. It could free up adviser time for more client interactions, enable personalised advice for timely decisions, and improve transparency in portfolio sharing.

Pain points

Adviser perspective

This status quo brings significant pain points: legacy infrastructure that is rigid and costly to maintain; fragmented systems and data flows that don’t provide holistic views of clients and portfolios; limited integration between front, middle and back office systems; and lack of real-time or near-real-time capabilities, meaning insights may lag events.

For advisers, time is often the constraint. They need tools that save time, reduce administrative burden and allow them to enrich client conversations. Rather than spending hours preparing spreadsheets or performance reviews, advisers increasingly expect ‘one-pane-ofglass’ dashboards that highlight actionable items — for example, an alert for emerging risk in client’s holdings, or a suggestion to explore an alternative historical return scenario. These allow them to focus on valueadded engagement rather than swimming through pools of data to reach a relevant recommendation or course of action.

Further, data quality issues (duplicate client records, incorrect risk-profile data, missing holdings) undermine trust in analytics. Without trust, advisers are unsurprisingly reluctant to rely on automated tools.

Client perspective From the client’s point of view, expectations have soared. Clients today expect seamless digital engagement — apps, dashboards, interactive tools — and personalised experiences that mirror other sectors,

Our goal is to give the adviser a ‘superpower’ — not replace them, but allow them to deliver high-impact, personal advice at scale.

AI and analytics in wealth management | Research paper

Enablers of the shift to value creation To move from data consumption to value creation, several enablers must be in place. These span technology, process, people, and ecosystem.

Real-time data integration: from siloed systems to single source of truth A foundational step is creating a ‘single source of truth’ for client, portfolio, market, and risk data. This means eliminating fragmented systems, integrating front, middle and back offices, ensuring data timeliness and consistency.

Having a trusted data backbone also supports advanced analytics and AI. When an AI model is fed stale, inconsistent, or poorly governed data, it can produce unreliable outputs and misleading insights — increasing the risk of poor advice, erroneous actions, and ultimately undermining adviser trust and client outcomes.

Practical AI use case A wealth firm builds a real-time data ingestion pipeline that integrates client transactions, market movements, news sentiment feeds, and adviser-activity logs into a central platform. A machine learning model continuously analyses these inputs — not only to detect early signs of client disengagement (such as reduced portal logins, shifts in risk profile, or large outflows) — but also to interpret how market developments and sentiment may be influencing each client relative to their investment objectives.

When potential risks or opportunities are detected, the system alerts the adviser and generates personalised talking points aligned with the client’s financial plan and current market context. Equipped with this insight, the adviser can re-engage the client in a timely, relevant conversation that builds trust and drives meaningful action — improving both client retention and adviser productivity.

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Real-time or near-real time data access is key — it empowers advisers and systems to respond quickly to market movements or client signals.

The real value from AI comes when it is embedded into the adviser workflow — the adviser remains the decisionmaker but is now far better informed, far more proactive.

AI and ML: examples of personalisation, risk detection, portfolio optimisation.

Once the data foundation is established, firms can deploy AI and ML to unlock greater value. Examples include:

Personalisation

Risk detection

Portfolio optimisation

Using client-behavioural, demographic and transaction data to recommend tailored products, content or services.

Identifying early warning signs of client churn, portfolio drift, overconcentration, or behavioural risks (such as increased risk-taking in volatile markets) — especially when these factors deviate from a client’s investment policy statement, financial plan, or long-term retirement goals.

Leveraging ML models to optimise allocations across multi-asset portfolios, identifying inefficiencies or correlations that traditional methods may miss.

Use-case An AI-driven ‘next-best-action’ engine identifies that a younger mass-affluent client has shown interest in thematic ETFs (detected via clicks and holdings) and prompts the adviser to offer an ESG-thematic investment vehicle — increasing cross-sell and engagement potential.

Use-case A risk-analytics model monitors portfolio holdings and detects that a high-net-worth (HNW) client has more than 60 percent exposure to a single sector and has recently reduced trading activity. The system alerts the adviser with a ‘rebalance suggestion’ and a client-communications script.

This proactive approach reduces concentration risk and strengthens the client relationship.

Use-case Using alternative data (news sentiment, social media signals) from LSEG’s MarketPsych Analytics, a wealth manager builds a model that adjusts multiasset portfolios for forward-looking sentiment shocks. Clients who adopt the model see a 2–3 percent improvement in returns versus traditional benchmarks.

LSEG’s ‘Mastering wealth management: The quantitative modelling advantage’ describes how LSEG’s StarMine models combine quantitative research, AI, and sentiment analysis to drive alpha and manage risk.

AI and analytics in wealth management | Research paper

Digital platforms: onboarding, CRM, mobile-first tools that empower clients A further enabler is the digital platform layer — comprising mobile apps, adviser portals, onboarding flows, CRM systems, and client portals. These platforms allow data and analytics outputs to be surfaced in a way that engages clients and advisers.

Use-case A client onboarding process uses AI-driven identity verification, automated know-your-customer (KYC) screening (via vendor APIs), a risk-profiling chatbot, and immediate customised investment-journey design. The system automatically segments the client, proposes onboarding content, and pre-populates portfolio suggestions. Time-to-onboarding drops significantly, conversion improves, and adviser time is freed up to focus on more value-adding activities.

LSEG’s Client-Centric Experiences offering highlights the LSEG Market Answers AI-chatbot and widget capabilities, enabling wealth firms to deliver personalised digital portals quickly.

Cloud adoption: scalability, resilience, agility Modern analytics and AI demand scalable compute, flexible storage, and agile deployment. Cloud adoption is increasingly a prerequisite.

No firm builds everything internally. The shift to value creation often requires a broader ecosystem: third-party FinTechs, data vendors, analytics-platform providers, and open APIs. Wealth managers must choose vendors that provide not only data but analytics, insight engines and developer-friendly platforms.

Use-case

A wealth manager migrates its portfolio-analysis and adviser-dashboard stack into a cloud environment, enabling real-time data refresh, elastic compute for AI-models during market stress, and API-first access.

The firm is then able to generate client-onboarding modules in significantly less time than before.

A wealth management firm integrates LSEG’s wealth-dataAPIs, a FinTech analytics engine (for behavioural signals), and a CRM platform. The result: advisers receive triggered ‘next-best-action’ prompts in the CRM based on real-time client and market signals, boosting cross-sell by 15 percent within six months.

By migrating from on-premise, monolithic systems to cloud-native or hybrid architectures, firms gain resilience, speed to market, and cost efficiency.

According to industry research, 81 percent of asset & wealth management organisations are exploring strategic partnerships or ecosystem builds to enhance tech capabilities.

Use-case

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Ecosystem partnerships: APIs, FinTech collaborations, vendor selection

Measuring ROI: beyond cost savings Often, firms embark on analytics or AI programmes with an expectation of cost savings (such as fewer manual processes, lower headcount, or reduced error).

While cost efficiency is important, it should not be the only metric. Firms that win are those that define value in a broader set of business-outcome metrics.

How leading firms define value from AI investments Leading wealth managers adopt metrics such as: •

Client retention and loyalty: leveraging analytics to identify at-risk clients and pre-empt churn.

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Client engagement: measuring the frequency, relevance, and quality of client interactions (through digital and adviser channels).

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Cross-sell/upsell rates: insights that identify next-best-product or service tailored to client lifestage, risk-profile, or behavioural signals.

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Time-to-market: how quickly a new digital client experience, product, or insight-engine is deployed.

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Adviser productivity: measuring time saved on adviser preparation, proposal generation, client segmentation, and insights provisioning.

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Net Promoter Score (NPS) or other clientsatisfaction metrics: improved experience leading to referrals, lifetime client value, and reduced attrition.

For example, LSEG research has shown that 62 percent of wealth management firms believe AI will significantly transform their operations; 68 percent of investors expect digital experiences to match the leading technology.

68% of investors expect digital experiences to match the leading technology.

AI and analytics in wealth management | Research paper

Example metrics in practice Time saved Wealth firms deploying AI-powered proposal engines and workflow automation tools have reported significant reductions in adviser preparation time — in some cases cutting administrative effort by up to 30 – 40 percent, according to industry analyses (e.g. Deloitte, 2024; Capgemini World Wealth Report, 2024).

Adviser productivity Firms that embed data-driven analytics into clientreview workflows typically see notable gains in adviser capacity — enabling more frequent and higher-quality client interactions each quarter (EY NextWave Wealth Management, 2023).

Engagement Personalised digital experiences, such as tailored insights and portfolio updates, have been shown to increase client portal engagement and digital adoption, improving satisfaction and deepening relationships (LSEG Insights: Four impactful approaches to boosting client engagement in wealth management, 2024).

Client retention Predictive analytics that identify clients at churn risk allow advisers to intervene earlier, with firms reporting material improvements in client retention rates following targeted re-engagement campaigns (Accenture Wealth Management Trends, 2024).

Cross-sell “Next-best-action” and “next-best-product” analytics have driven measurable uplifts in share of wallet and product penetration across client segments, as noted in McKinsey’s State of AI in Wealth and Asset Management (2023).

Client satisfaction Wealth managers adopting AI-enabled personalisation and streamlined adviser workflows often see meaningful gains in Net Promoter Score (NPS) and client advocacy (Capgemini World Wealth Report, 2024).

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These kinds of outcomefocused metrics help shift the conversation from operational efficiency (“we saved X in costs”) to client-centric impact — demonstrating tangible improvements in engagement, retention, advice quality, and lifetime client value.

Challenges and risks While the opportunity is substantial, firms must navigate several challenges and risks as they seek to implement AI in various areas of their operations: Legacy systems, integration pain, data quality issues

Cultural resistance: advisers and clients wary of machine-driven insights

Many wealth management firms have grown through acquisition, hold decades of legacy systems, and are burdened by disparate technology stacks. Integrating these systems to achieve real-time, high-quality data is non-trivial. Poor data quality, or inconsistent data, undermine analytics investment and adviser trust.

Even with the best technology, adoption can falter if advisers mistrust analytics or feel threatened by automation. Furthermore, clients may mistrust ‘machine-only’ advice. LSEG research has found that while investors are open to AI, the adviser’s role remains critical: 45 percent of current adviser-users and 51 percent of non-users believe the adviser’s value lies in providing trusted investment advice over the next three years.

Shortage of AI/data-science talent Building, deploying, and maintaining advanced analytics and AI models requires specialist skills — data scientists, ML engineers, data engineers, and model-governance experts. Many firms struggle to recruit and retain this talent, or to embed it effectively in the business (versus a central ‘tech’ team disconnected from business needs).

Regulatory scrutiny and the importance of explainable AI The regulatory environment for wealth management is increasingly demanding: data governance, model risk, fair treatment of clients, and transparency are all under scrutiny. AI/ML models must be explainable, auditable, and compliant. For example, LSEG has published on how it is building data-trust through governance and lineage.

Therefore, the human-plus-machine model (rather than machine-only) is essential.

AI and analytics in wealth management | Research paper

The road ahead: strategic priorities for leaders For wealth management firms determined to lead the shift from data to value, the following priorities are essential.

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Start with the ‘why’: AI must solve specific business problems

Reskill the workforce: advisers fluent in data, not threatened by it

Rather than launching AI initiatives because ‘everyone is doing it’, firms should identify specific business problems (e.g., adviser time spent on proposals, client churn in certain segments, or poor client-portal engagement) and define measurable outcomes. This purpose-first approach ensures technology becomes an enabler, not a distraction.

Advisers and back office staff must be upskilled to work with data and insights. Rather than viewing analytics as a threat, advisers should see it as a productivity multiplier and relationship-enhancer.

Training programmes, user-friendly tools, and change management are key to this shift.

Invest in data discipline: clean, centralised, and accessible

Adopt modular, flexible architecture: avoid monolithic re-platforming

Before advanced analytics can deliver, the data foundation must be solid, built on data that is clean, consistent, and accessible. Building a ‘data catalogue’, tracking lineage, ensuring governance, and establishing a centralised data lake or warehouse, are essential. For example, LSEG’s data-trust programme emphasises data lineage as foundational to trusted analytics.

Many firms fall into the trap of large-scale ‘ripand-replace’ projects. Instead, a modular, API-first architecture facilitates agile deployments, incremental change, and faster time-to-value. It also allows firms to plug in best-of-breed data/analytics vendors (for example LSEG’s wealth-data and widget capabilities) rather than building everything in-house.

Balance human and machine: AI empowers, but advisers remain critical

Cybersecurity and trust: safeguarding sensitive wealth data is non-negotiable

Wealth management is a human-centric business. AI should enhance the adviser-client relationship, not replace it. The hybrid advisory model — where AI tools support advisers in generating insights, and the adviser adds personal judgement, empathy and client-specific nuance — is emerging as the industry norm.

The sensitivity of wealth management client data means that cybersecurity, data-privacy controls, and operational resilience must underpin any analytics or AI deployment. Failure in these domains undermines client trust, regulatory compliance, and the foundation upon which data-driven value is built.

Conclusion:

Building the wealth firm of the future for leaders

T

he winners in wealth management over the next decade will be those firms that treat technology not as an overhead, but as a growth engine.

They will shift from dashboards to decision-making, from passive reporting to proactive engagement. They will harness real-time data, analytics, and AI to empower advisers, personalise client experiences, and drive measurable business outcomes.

With the industry moving fast, 62 percent of wealth firms see AI as a key growth driver, while rising client expectations make technology maturity a critical differentiator.

Now is the time for decisive action.

Wealth management firms must prioritise the ‘why’, invest in data foundations, balance human expertise with machine intelligence, reskill their workforce, adopt flexible architectures, and embed trust and security across their platforms.

In short: the journey from data to value is underway — but the firms that lead it will be those who act with clarity, speed and purpose. The future belongs to those who not only keep pace but define what ‘digital-first, client-centric wealth management’ will look like.

Define your problem, clean your data, deploy a pilot AI usecase, measure outcomes, and scale. The time to act is now.

AI and analytics in wealth management | Conclusion

About the research WealthTech Insight Series This research is part of The Wealth Mosaic’s WealthTech Insight Series (WTIS), an ongoing and developing research process, mixing online surveys and interviews, and focused exclusively on technology in the wealth management sector across the world.

Rather than a one-off research process, the WTIS will seek to build an ongoing program of research among wealth managers of different types across the world on a broad range of technology and related topics, building up an aggregated knowledge base of both qualitative views and perspectives as well as quantitative data points.

Discover the latest research papers: Playbook for Technology Spend in Wealth Management Drawn from interviews with senior executives from across the UK wealth management sector, this paper looks at the past, present, and future of technology spend and transformation in the industry.

Read now >

The European Wealth Playbook for Growth This paper has been developed to showcase useful insights for wealth managers seeking to navigate the evolving financial landscape in Europe and capitalise on emerging opportunities to drive growth in an everevolving industry.

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We are pleased to present this research paper created in partnership with LSEG on how AI, advanced analytics, and real-time data are transforming wealth management.

This paper adds to our growing, aggregated knowledge base, blending qualitative perspectives with quantitative data to track technology adoption and impact over time.

For our readers, we hope the findings in this paper are insightful as we continue building an evidencebased view of the industry’s technology evolution.

LSEG Data & Analytics is one of the world’s largest providers of financial markets data and infrastructure. With over 40,000 customers and 400,000 end users across approximately 190 markets, we are an essential partner to the global financial community and redefining the future of data in financial services. We enable customers to draw crucial insights through data, feeds, analytics, AI and workflow solutions.

With our unique insights seamlessly integrated into your workflow, you can identify opportunity and seize competitive advantage.

Find out more at Wealth Data Solutions

The Wealth Mosaic is a UK-headquartered online solution provider directory and knowledge resource, focused specifically on the wealth management industry.

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

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

Find out more at www.thewealthmosaic.com

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AI and analytics in wealth management | About

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