The Bank of England has published its report “Machine Learning in UK Financial Services”. The report sets out its findings, following a survey of around a hundred regulated firms in the UK. It highlights the growing use of machine learning, especially in insurance, and the challenges of explainability, legacy systems, the skills gap and regulatory uncertainty.
Adoption and use of machine learning in financial services
The number of UK financial services firms using or developing machine learning (ML) applications is increasing, and this trend is set to continue across a greater range of business areas within financial services. The largest expected increase in use, in absolute terms, is in the insurance sector, followed by banking.
The ML applications being used in recent years are more advanced, and more embedded, in the day-to-day operations of financial services firms. Customer engagement and risk management are the business areas with the most ML applications, while the business areas with the fewest ML applications are investment banking and treasury.
Digging into this further:
- In insurance, ML is typically used for claims management, product pricing and capital reserve modelling.
- In banking, ML is being used for anti-money laundering functions, credit risk and regulatory capital modelling.
- In investment management, ML is being used for order routing and execution, and generating trading signals.
Governance and risk management
Good governance is essential for the safe and responsible adoption of ML in financial services. The Bank of England’s report reveals that the majority of UK financial services firms are using existing data governance, risk management and operational risk management frameworks to identify, manage and mitigate the risks arising from the use of ML.
Benefits of machine learning
The most commonly identified benefits are enhanced data and analytics capabilities, increased operational efficiency, and improved detection of fraud and money laundering. The benefits are expected to increase over the next three years, as ML technologies become even more widely used, which may also improve the personalisation of financial products and services, as well as overall customer engagement.
Risks of machine learning
There are trade-offs with the use of ML and the technology can pose risks. The top risks identified to consumers relate to data bias and representativeness, while the top risks to firms are the lack of explainability and interpretability of ML applications.
Constraints on machine learning
UK financial services firms are also contending with practical constraints on ML adoption and deployment. The greatest constraints are associated with legacy systems and the associated technology infrastructure at firms. These are followed by a lack of sufficient skills, and a lack of clarity with existing financial services laws and regulation in the UK.
Your chance to influence regulation
The Bank of England, the Prudential Regulation Authority and the Financial Conduct Authority have recently issued a joint discussion paper (DP5/22) on the use of artificial intelligence (AI) and ML in financial services. The discussion paper explores how policy can best support further safe and responsible AI and ML adoption and whether additional clarification of the existing regulatory framework may be helpful. Please see our earlier blog for more details.
We have experts on the evolving regulation landscape in financial services, transport, healthcare and AI per se. There are clear emerging trends and opportunities for the the UK to differentiate itself, including strategic investment in projects and education, and striking legal and regulatory balances between innovation, risk, privacy and IP. Given the potential social impacts of AI, especially in financial services, the UK Government needs to hear from all sections of society to find suitable consensus and compromise. In the meantime, companies need to consider their data governance and ethics to future-proof their services against regulatory developments.
