GLOBAL BUSINESS
Why Automate Regulation and Compliance Automation is all the rage, but why is this happening and what are the benefits? In short, money – cost savings and greater efficiency are both imperatives and key drivers. Indeed, the UK Chancellor Philip Hammond put it well in a speech to the second International FinTech Conference in London in March 2019. His speech covered many areas of financial innovation, but perhaps the most significant aspect from a compliance perspective, was his announcement that the FCA and Bank of England are moving towards automating regulatory compliance. The intended benefits of this automation will likely be reduced costs for financial services firms, as well as the removal of a key barrier for FinTechs as they enter financial services markets. Indeed, the proposed automation is part of the government’s new FinTech Sector Strategy, which seeks to retain the UK’s position as ‘the global capital of FinTech’ well beyond Brexit. The UK has reason to be proud of its innovation. After all, FinTech contributes nearly $7 billion to the UK economy each year and London is home to 17 of the top 50 international FinTech firms. Indeed, in 2017, investment in UK FinTech more than doubled. For a country that launched the industrial revolution, evidently innovation and entrepreneurship is far from over. Let us now translate some of these concepts into normal language by exploring their definitions.
Data Science & Technologies In transforming regulation, the core technologies are: Data Facilities: online facilities of regulatory data collected by national government agencies, and often open source for public access and analysis (e.g. www.data.gov, https://data.gov.uk);
Artificial intelligence (AI): systems able to perform tasks normally requiring human intelligence;
Internet of Things (IoT): is the internetworking of ‘smart’ physical devices, vehicles, buildings, etc. that enable these objects to collect and exchange data;
Behavioral/Predictive Analytics: the analysis of large and varied data sets to uncover hidden patterns, unknown correlations, customer preferences etc. to help make informed decisions;
Chatbots: systems for interacting with regulated companies, registrants and the general public using natural language and speech;
Blockchain Technologies: technology underpinning digital currency, that secures, validates and processes transactional data.
Big Data: is the process of examining very large data sets to uncover hidden patterns, unknown correlations etc.; data sets that are so complex that traditional data processing application software is inadequate to deal with them;
Let us now illustrate these vital technologies in more detail
Big Data Regulators collect huge volumes of data (increasingly open sourced) and thus has major opportunities for socalled Big data (analytics). In general, Big data provides the opportunity of examining large and varied data sets to uncover hidden patterns, unknown correlations, customer preferences etc. Big data encompasses a mix of structured, semi-structured and unstructured data, gathered formally through interactions with citizens, social media content, text from citizens’ emails and survey responses, phone call data and records, data captured by sensors connected to the internet of things and so on. The notion of ‘Big data’ is both increasing in volume, variety of data being generated by organisations and the velocity at which that data is being created and updated; referred to as the 3Vs of Big data.
Artificial Intelligence Technologies AI technologies power intelligent personal assistants, such as Apple Siri, Amazon Alexa, and ‘Robo’ advisors, and autonomous vehicles. AI provides computers with the ability to make decisions and learn without explicit programming. There are three main branches:
Behavioural and Predictive Analytics
Closely related to Big data is behavioural and predictive analytics that focuses on providing insight into the actions of people. Behavioural analytics centres on understanding how consumers act and why, enabling predictions about how they are likely to act in the future, predictive analytics is Machine Learning: a type of AI program the practice of extracting information with the ability to learn without explicit from historical and real-time data sets programming, and can change when to determine patterns and predict exposed to new data; future outcomes and trends. Predictive analytics ‘forecasts’ what might happen Natural Language Understanding: in the future with an acceptable level the application of computational techniques to the analysis and synthesis of reliability, and includes what-if scenarios and risk assessment. of natural language and speech; and Sentiment Analysis: the process of computationally identifying and categorizing opinions expressed in a piece of text.
Blockchain Technologies Perhaps the most popular and much coined term in FinTech, the core blockchain technologies are: Distributed Ledger (DLT): a decentralized database where transactions are kept in a shared, replicated, synchronized, distributed bookkeeping record, which is secured by cryptographic sealing. The key attributes are: resilience, integrity, transparency, and unchangeable or mostly ‘immutable’. Smart Contracts: (possibly) computer programs that codify transactions and contracts which in turn ‘legally’ manage the records in a distributed ledger.
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