Executive summary
- 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.
- Respondents do not see ML, as currently used, as high risk. The top risks identified for consumers relate to data bias and representativeness, while the top risks for firms are considered to be the lack of explainability and interpretability of ML applications.
- The greatest constraint to ML adoption and deployment is legacy systems. The difficulty integrating ML into business processes is the next highest ranked constraint.
- Almost half of firms who responded to the survey said there are Prudential Regulation Authority and/or Financial Conduct Authority regulations that constrain ML deployment. A quarter of firms (25%) said this is due to a lack of clarity within existing regulation.
1: Introduction
1.1: Context and objectives
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.
This joint Bank-FCA report is the result of the analysis of the responses to the 2022 survey. This includes, in relation to the firms that responded to the survey:
- a quantitative overview of the use of ML;
- the ML implementation strategies of firms;
- the share of ML applications developed in-house or by third-party providers;
- approaches to the governance of ML;
- respondents’ views on the benefits of ML;
- respondents’ views on the risks of ML;
- perspectives on constraints to development and deployment of ML; and
- a snapshot of the use of different methods, data, safeguards performance metrics, validation techniques and perceived levels of complexity of ML.
The report closes with a selection of ML case studies, describing a sample of typical use cases, including:
- Insurance pricing and underwriting.
- Credit underwriting.
- Marketing.
- Fraud prevention and anti-money laundering (AML).
1.2: Methodology
In total, 168 firms received the questionnaire and 71 submitted responses (42% overall response rate). The Bank surveyed 48 dual-regulated firms, 17 firms applying for Prudential Regulation Authority (PRA) authorisation as a deposit-taker, and eight Bank-regulated financial market infrastructures (FMIs), and received 51 (70%) responses. The FCA surveyed 95 FCA-regulated firms and received 20 (21%) responses.
The Bank selected firms with the aim of surveying each type of FMI and PRA-regulated firm and covering a significant share of those firms. It also included several firms that are small in terms of their market share but were considered to be advanced in the use of ML and therefore of interest for horizon-scanning purposes. In addition, the sample included a number of FCA-regulated small-sized firms, who were undergoing the PRA authorisation process for deposit-taking permissions.
The FCA sent the survey to a representative list of firms from the following sectors: credit referencing agencies, crowdfunders, custody services, exchanges, fund management, alternatives, lifetime mortgage providers, multilateral trading facilities, non-bank lenders, principal trading firms, wealth manager and stock brokers, wholesale brokers, credit brokers, debt purchasers, debt administrators, consumer credit providers, motor finance providers, retail finance providers, payment services, and e-money issuers. It also included firms who responded to the 2019 survey.
Overall, the combined sample is skewed towards larger firms with no responses received from smaller fintech firms or start-ups. While firms may be more likely to respond to the survey if they are already using or developing ML, the sample can be seen to provide a broad representation of firms by types of activity, size, and areas of ML applications. However, the sample and survey findings should not be seen as representative for all types of firms or the entire UK financial services industry.
The results presented in this report are anonymised and aggregated with the respondents grouped into the sectors listed in Table A.
Table A: Sector classification used in the survey and report
Sector | Type of firms included |
|---|---|
Banking | Building societies, international banks, retail banks, UK deposit-takers |
Insurance | General insurers, health insurers, life insurers, personal and commercial lines insurers |
Non-bank lending | Credit brokers, consumer credit lender, non-bank lenders |
Investment and capital markets | Alternatives, asset managers, fund managers, wealth managers and stockbrokers, wholesale brokers |
Financial market infrastructures (FMIs), payments and other | Credit reference agencies, e-money issuers, exchanges, financial market infrastructures, multilateral trading facilities |
Footnotes
- Sources: Bank of England and Financial Conduct Authority.
All charts in this report are based on data received from respondents from this survey. When designing the survey, the Bank and the FCA considered the Legislative and Regulatory Reform Act 2006 principle that regulatory activities should be carried out in a way which is transparent and proportionate.
Box A: Definitions of ML, application, algorithm and model
ML is a methodology whereby computer programmes build a model to fit a set of data that can be utilised to make predictions, recommendations, or decisions without being explicitly programmed to do so, instead learning from sample data or experience. There are many different approaches to the implementation of ML, which include techniques such as supervised learning, unsupervised learning, and reinforcement learning. For the purpose of this survey, this excludes simple linear regression – which we define as any regression techniques that does not employ subset selection methods, shrinkage methods or dimension reduction methods.
‘ML application’ refers to an entire system, including data collection, feature engineering, model engineering, and deployment. It also includes the underlying IT infrastructure (eg virtualisation, data storage, and integrated development environment). An ML application could include multiple models and algorithms. Respondents were asked to classify ML applications separately if they fulfil different business purposes or if their set up/components differ significantly.
The term ‘algorithm’ means a set of mathematical instructions or rules that, especially if given to a computer, will help to calculate an answer to a problem. Whereas the term ‘model’ means a quantitative method, system, or approach that applies statistical, economic, financial or mathematical theories, techniques, and assumptions to process input data into output. The definition of a model includes input data that are quantitative and/or qualitative in nature or expert judgement-based, and output that are quantitative or qualitative. In ML, an algorithm is a procedure that is run on data to create a model.
2: Machine learning adoption and use
2.1: Financial services firms use an increasing number of ML applications
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.
Chart 1: 72% of firms that responded already use or are developing ML
Respondents expect this trend to continue, with the overall median number of ML applications expected to increase by over 3.5 times over the next three years. This increase is in line with the trend reported in the 2019 survey. The largest expected increase is in the insurance sector, with the median number of applications per firm expected to increase by 163%.
Chart 2: Median number of ML applications expected to increase by over 3.5x
2.2: Deployment stage
ML applications pass through a number of development and deployment stages. The survey asked firms to report the number of applications they have at each of the five key stages: (i) proof-of-concept or experimental, (ii) front-line research and development and/or used for benchmarking existing models, (iii) pilot and/or used in small share of business area, (iv) deployed across considerable share of business area, and (v) critical to business area.
From the survey responses, 79% of ML applications are in deployment (Chart 3). 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.
Chart 3: 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 (Chart 4). 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.
Chart 4: Non-bank lenders have the highest percentage of ML applications in deployment
2.3: Range of applications across sectors and business areas
In terms of the range of ML use cases (Chart 5), firms are developing or using ML across most business areas. As with the 2019 survey, ‘customer engagement’ and ‘risk management’ continue to be the areas with the most applications and account for 28% and 23% of all reported applications respectively. The ‘miscellaneous’ category, which included business areas like human resources and legal departments, had the third highest percentage of ML applications (18%). The business areas with the fewest ML applications are ‘investment banking’ (0.9%) and ‘treasury’ (0.4%), with the latter also being the business area with the fewest ML applications in the 2019 survey.