Bank of England and FCA disclose Machine Learning use in UK financial services

By Puja Sharma

The Bank of England and Financial Conduct Authority conducted a second survey into the state of machine learning in UK financial services.

Over the past few years the use of machine learning (ML) has continued to increase in the United Kingdom (UK) financial services sector. As with other technologies, ML can bring a range of benefits to consumers, firms, markets, and the wider economy. Many firms are already realising these benefits and deploying ML applications across various business lines, services and products. However, ML can also raise novel challenges (such as ethical issues) and amplify risks to consumers, the safety and soundness of firms, and even potentially financial stability. That is why it is important regulatory authorities monitor the state of ML deployment and ensure they understand the different use cases, maturity of applications, benefits, and risks.

In 2019, the Bank of England (Bank) and Financial Conduct Authority (FCA) conducted a joint survey to gain an understanding of the use of ML in the UK financial services sector. One of the key findings was the need for further dialogue between the public and private sector to ensure the safe and responsible adoption of ML. The Bank and FCA established the Artificial Intelligence Public-Private Forum (AIPPF) in 2020, which explored various barriers to adoption and challenges related to the use of artificial intelligence (AI)/ML, as well as ways to address such barriers and mitigate risks.

This survey builds on the 2019 survey, the AIPPF final report, and the wider domestic and international discussion about the use of ML in financial services (in which the Bank and FCA have been active participants). In publishing the findings, the Bank and FCA demonstrate their commitment to monitoring the state of ML deployment, improve their collective understanding, and support the safe and responsible adoption of ML technology in UK financial services.

The number of ML applications used in UK financial services continues to increase. Overall, 72% of firms that responded to the survey reported using or developing ML applications. This compares to 67% of respondents to the 2019 survey, although it is worth noting the sample size and composition was different to the 2022 survey. Similar to 2019, respondents from the banking and insurance sectors have the highest number of ML applications.

From the survey responses, 79% of ML applications are in deployment. In particular, 65% of applications are already deployed across a considerable share of business areas, with a further 14% of ML applications reported to be critical to the business area. Although the survey question was somewhat different in 2019, a significantly higher proportion of applications were in pre-deployment stages then, 44% in 2019 versus 10% in 2022. This suggests the survey respondents’ ML applications are more advanced and increasingly embedded in day-to-day operations.

 Overall, 80% of ML applications are in deployment or critical stages

Banks, insurance, and FMIs, payments and other firms broadly have a similar split between the different stages of deployment. Non-bank lenders have the highest percentage of ML applications (42%) that are critical to business areas with just 3% of applications in pre-deployment. At the other end of the scale, respondents from the investment and capital markets sector have the largest number of ML applications in the pilot or small share of business stage and no critical applications.

• The number of UK financial services firms that use machine learning (ML) continues to increase. Overall, 72% of firms that responded to the survey reported using or developing ML applications. These applications are becoming increasingly widespread across more business areas.
• This trend looks set to continue and firms expect the overall median number of ML applications to increase by 3.5 times over the next three years. The largest expected increase in absolute terms is in the insurance sector, followed by banking.
• ML applications are now more advanced and increasingly embedded in day-to-day operations. 79% of ML applications are in the latter stages of development, ie either deployed across a considerable share of business areas and/or critical to some business areas.
• Financial services firms are thinking about ML strategically. The majority of respondents that use ML (79%) have a strategy for the development, deployment, monitoring and use of the technology.
• Firms use existing governance frameworks to address the use of ML. 80% of respondents that use ML say their applications have data governance frameworks in place, with model risk management and operational risk frameworks also commonplace (67%).
• Firms consider that ML presents a range of benefits. Currently the most commonly identified benefits are enhanced data and analytics capabilities, increased operational efficiency, and improved detection of fraud and money laundering.

Image courtesy: ClaudeAI.uk