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Machine learning in UK financial servicesOctober 2019 Machine learning in UK financial services October 2019 2 ContentsExecutive summary 3 1 Introduction 5 1.1 Context and objectives 5 1.2 Methodology 6 2 The state of machine learning adoption 8 2.1 Machine learning is already being used live by the majority of respondents 8 2.2 In many cases firms deployment of machine learning has passed the initial 9 development phase 2.3 Respondents identify a broad range of use cases 9 3 Strategies, governance and third-party providers 12 3.1 The majority of respondents have a dedicated machine learning strategy 12 3.2 The majority of users apply their existing model risk management framework to 13 machine learning 3.3 Only a small share of machine learning applications are implemented by 13 third-party providers 4 Firms perception of benefits, risks and constraints 16 4.1 Respondents already see benefits from machine learning and expect these to increase 16 4.2 Firms recognise model validation and governance need to keep pace with 16 machine learning developments 4.3 Constraints to deployment of machine learning are mostly internal to firms 18 4.4 Regulation is not seen as an unjustified barrier 19 5 How machine learning works 21 5.1 Machine learning applications consist of a pipeline of processes 21 5.2 Data acquisition and feature engineering are evolving with the advent 21 of machine learning 5.3 Model engineering and performance evaluation decide which models are deployed 23 5.4 Model validation is key to ensuring machine learning models work as intended 25 5.5 Complexity can increase due to deployment of machine learning 26 5.6 Firms use a range of safeguards to address risks 27 6 Conclusion 28 6.1 Context 28 6.2 What we have learnt 28 6.3 Questions for authorities 29 6.4 Next steps 29 7 Appendix case studies 30 7.1 Purpose and background 30 7.2 Methodology 30 7.3 Anti-money laundering and countering the financing of terrorism 30 7.4 Customer engagement 31 7.5 Sales and trading 31 7.6 Insurance pricing 32 7.7 Insurance claims management 33 7.8 Asset management 34Acknowledgements 36 Machine learning in UK financial services October 2019 3 Executive summary Machine learning (ML) is the development of models for prediction and pattern recognition from data, with limited human intervention. In the financial services industry, the application of ML methods has the potential to improve outcomes for both businesses and consumers. (1)In recent years, improved software and hardware as well as increasing volumes of data have accelerated the pace of ML development. The UK financial sector is beginning to take advantage of this. The promise of ML is to make financial services and markets more efficient, accessible and tailored to consumer needs. (2)At the same time, existing risks may be amplified if governance and controls do not keep pace with technological developments. But the risks presented by ML may be different in each of the contexts it is deployed in. (3)More broadly, ML also raises profound questions around the use of data, complexity of techniques and the automation of processes, systems and decision-making. (4)The Bank of England (BoE) and Financial Conduct Authority (FCA) have a keen interest in the way that ML is being deployed by financial institutions. That is why we conducted a joint survey in 2019 to better understand the current use of ML in UK financial services. The survey was sent to almost 300 firms, including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms, with a total of 106 responses received. The survey asked about the nature of deployment of ML, the business areas where it is used and the maturity of applications. (5)It also collected information on the technical characteristics of specific ML use cases. Those included how the models were tested and validated, the safeguards built into the software, the types of data and methods used, as well as considerations around benefits, risks, complexity and governance. Although the survey findings cannot be considered to be statistically representative of the entire UK financial system, they do provide interesting insights. The key findings of our survey are: ML is increasingly being used in UK financial services. Two thirds of respondents report they already use it in some form. The median firm uses live ML applications in two business areas and this is expected to more than double within the next three years. In many cases, ML development has passed the initial development phase, and is entering more mature stages of deployment. One third of ML applications are used for a considerable share of activities in a specific business area. Deployment is most advanced in the banking and insurance sectors. From front-office to back-office, ML is now used across a range of business areas. ML is most commonly used in anti-money laundering (AML) and fraud detection as well as in customer-facing applications (eg customer services and marketing). Some firms also use ML in areas such as credit risk management, trade pricing and execution, as well as general insurance pricing and underwriting. (1) Carney, M (2018), AI and the Global Economy. (2) Carney, M (2018), AI and the Global Economy. (3) fca.uk/news/speeches/future-regulation-ai-consumer-good. (4) Proudman, J (2019), Managing machines: the governance of artificial intelligence. (5) In this report the term application means the integrated whole of a ML application, including data collection, feature engineering, model engineering and deployment. It also includes the underlying IT infrastructure (eg data storage, integrated development environment). A ML application could include multiple models and ML algorithms. ML applications should be seen as separate if they fulfil different business purposes or if their set up / components differ significantly. Machine learning in UK financial services October 2019 4 Regulation is not seen as an unjustified barrier but some firms stress the need for additional guidance on how to interpret current regulation. Firms do not think regulation is an unjustified barrier to ML deployment. The biggest reported constraints are internal to firms, such as legacy IT systems and data limitations. However, firms stressed that additional guidance around how to interpret current regulation could serve as an enabler for ML deployment. Firms thought that ML does not necessarily create new risks, but could be an amplifier of existing ones. Such risks, for instance ML applications not working as intended, may occur if model validation and governance frameworks do not keep pace with technological developments. Firms validate ML applications before and after deployment. The most common validation methods are outcome-focused monitoring and testing against benchmarks. However, many firms note that ML validation frameworks still need to evolve in line with the nature, scale and complexity of ML applications. Firms use a variety of safeguards to manage the risks associated with ML. The most common safeguards are alert systems and so-called human-in-the-loop mechanisms. These can be useful for flagging if the model does not work as intended (eg in the case of model drift, which can occur as ML applications are continuously updated and make decisions that are outside their original parameters). Firms mostly design and develop ML applications in-house. However, they sometimes rely on third-party providers for the underlying platforms and infrastructure, such as cloud computing. The majority of users apply their existing model risk management framework to ML applications. But many highlight that these frameworks might have to evolve in line with increasing maturity and sophistication of ML techniques. This was also highlighted in the BoEs response to the Future of Finance report. (6)In order to foster further conversation around ML innovation, the BoE and the FCA have announced plans to establish a public- private group to explore some of the questions and technical areas covered in this report. (6) Bank of England (2019), The Future of Finance our response. Machine learning in UK financial services October 2019 5 1 Introduction 1.1 Context and objectives The UK economy is increasingly powered by big data, platform business models, advanced analytics, smartphone technology and peer-to-peer networks. (7)At the same time, innovation in the financial sector is dramatically changing the markets we regulate (8)but also the way in which we regulate. (9)(10)As an industry, financial services are (and will always be) very data-reliant. Hence, this new data-driven economy goes hand in hand with fundamental changes to the structure and nature of the financial system supporting it. (11)And ML is a principal driver contributing to this new finance. (12)ML has wide-ranging applications in financial services and, when combined with increasing computational power, has the ability to analyse large data sets, detect patterns and solve problems at speed. The use of ML has the potential to generate analytical insights, support new products and services, and reduce market frictions and inefficiencies. (13)If this potential is achieved, consumers could benefit from more tailored, lower cost products and firms could become more responsive, learner and effective. It is important that regulatory authorities understand ML; including the current state of deployment, maturity of applications, use cases, benefits and risks. This was the motivation behind the BoE and FCA joint survey, which was carried out during the first half of 2019. The objective was to gain an understanding of the use of ML in the UK financial sector. The results, together with ongoing dialogue with the industry and other authorities, both domestically and internationally, will help identify where there are policy questions that need to be answered in the future, in order to support the safe and productive deployment of ML within the financial sector. This joint BoE-FCA report is the result of the analysis of the responses to the survey and presents: a quantitative overview of the use of ML across the respondent firms; the ML implementation strategies of firms that responded to the survey; approaches to the governance of ML; the share of applications developed by third-party providers; respondents views on the benefits of ML; perceptions of risks and ethical considerations; 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. (7) Carney, M (2019), A platform for innovation remarks. (8) fca.uk/news/speeches/innovation-hub-innovation-culture. (9) fca.uk/news/speeches/financial-conduct-regulation-restless-world. (10) Chakraborty, C and Joseph, A (2017), Machine learning at central banks, Bank of England Staff Working Paper No. 674. Turrell et al (2018), Using online job vacancies to understand the UK labour market from the bottom-up, Bank of England Staff Working Paper No. 742. Proudman, J (2018), Cyborg supervision the application of advanced analytics in prudential supervision. (11) See Mnohoghitnei, I, Scorer, S, Shingala, K and Thew, O, Embracing the promise of fintech, Bank of England Quarterly Bulletin, 2019 Q1. (12) Carney, M (2018), AI and the Global Economy. (13) fsb/wp-content/uploads/P011117.pdf. Machine learning in UK financial services October 2019 6 The report closes with a non-exhaustive selection of case studies, describing a sample of typical use cases, including: Anti-money laundering and countering the financing of terrorism Customer engagement Sales and trading Insurance pricing Insurance claims management Asset management 1.2 Methodology When designing the survey, the BoE and 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 1 What is the difference between artificial intelligence and machine learning? Artificial intelligence (AI) is the theory and development of computer systems able to perform tasks which previously required human intelligence. (1)AI is a broad field, of which ML is a sub-category. ML is a methodology whereby computer programmes fit a model or recognise patterns from data, without being explicitly programmed and with limited or no human intervention. This contrasts with so-called rules-based algorithms where the human programmer explicitly decides what decisions are being taken under which states of the world (Figure A). Many ML algorithms constitute an incremental (rather than fundamental) change in statistical methods. They introduce more flexibility in statistical modelling. For instance, many ML models are not constrained by the linear relationships often imposed in traditional economic and financial analysis. However, over the last decade, computing power and the amount of data processed has grown exponentially. This has allowed ML models to become an order of magnitude larger and more complex than more traditionally used techniques. As a result, ML models can often make better predictions than traditional models or find patterns in large amounts of data from increasingly diverse sources. (1) fsb/2017/11/artificial-intelligence-and-machine-learning-in-financial-service/. Machine learning Human sets optimisation criteria Optimising programme Data + Rules-based algorithms Human explicitly programs rules If A B C and D Then X Y Z If ? ? ? Then ? ? ? Programme comes up with rules Figure A Machine learning algorithms make decisions without being explicitly programmed Machine learning in UK financial services October 2019 7 In total, 287 firms received the questionnaire and 106 submitted responses. The BoE surveyed 58 dual-regulated firms (14)and received 47 (81%) responses. (15)The FCA surveyed 229 FCA-regulated firms and received 63 (28%) responses. The BoE selected firms with the aim of surveying each type of BoE and Prudential Regulation Authority (PRA)-regulated firm. This sample was determined to cover a significant share of BoE and PRA 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. The FCA sample was built according to the following criteria. Sample selection reflected the need to represent firms that, due to their size and the number of customers, have the potential to affect the highest number of consumers, or are more likely to be anti
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