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The impact of Covid on machine learning and data science in UK banking

Bank of England | By David Bholat, Mohammed Gharbawi and Oliver Thew| Dec 2020

Covid a call for digital transformation - The impact of Covid on machine learning and data science in UK banking

  • We conducted a survey of banks to understand the impact of the Covid-19 (Covid) pandemic on their use of machine learning (ML) and data science (DS).
  • The use of ML and DS by banks has remained broadly stable since the start of the pandemic, with the number of applications remaining the same or increasing.
  • Half of surveyed banks expected an increase in the importance of ML and DS for future operations as a result of Covid. However, only a third of banks said there was an increase in the number of planned or existing ML or DS projects.
  • We will continue to monitor these developments closely, and to support the safe adoption of ML and DS in financial services.

Overview

This article reports findings from a survey conducted in August 2020 of banks headquartered or operating in the UK. The survey sought to understand how Covid has affected the adoption and use of ML and DS. Half of the banks surveyed reported an increase in the importance of ML and DS as a result of the pandemic. None of the banks believe that Covid will reduce the importance of ML and DS for them.

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The fact that investment and interest in ML and DS has remained broadly stable, and has actually increased for some banks, is significant given wider market volatility and the economic downturn, which might be expected to constrain budgets for investment in innovation. This may reflect the nature of the shock, which has affected financial services less than other sectors so far. We will continue to monitor how these technologies are used, including their benefits and risks, and explore how we can best support their safe adoption.

Benefits for households, banks and the economy

ML and DS have wide-ranging applications in financial services, which can bring benefits to consumers, businesses and the economy. For example, many banks use ML and DS for anti-money laundering (AML) processes. In many instances, this has reduced the rate of false positives in money laundering detection with one large UK bank lowering its false positives by 70%. For consumers, this helps reduce the number of erroneously blocked or delayed payments. For banks, this frees up scarce resources and speeds up internal processes. For the economy as a whole, this can help banks and authorities more precisely identify illicit financial activity.

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ML and DS also have the potential to provide more inclusive and tailored products to consumers. For example, ML is already being used by banks and fintech companies around the world to analyse newer data sources (such as social media data) to provide risk assessments of individuals with limited credit histories, which might help underserved or unbanked customers access financial services. Some UK fintechs and banks are using new data sources for consumer and business risk assessments. This trend looks set to continue with one credit rating agency announcing plans to offer UK banks access to a broader range of transactional data for consumer credit scores, including money earned and spent, council tax payments, savings and investments, and subscription payments.

The impact of Covid on ML and DS in UK banking

To help us understand the impact of Covid on ML and DS in the UK banking sector, we conducted a further survey of Prudential Regulation Authority (PRA) regulated banks in August 2020. The survey focused on banks’ perception of ML and DS, as well as the resourcing for current and planned ML and DS projects (see annex for the survey questions).

post covid research on ML and UK banks - The impact of Covid on machine learning and data science in UK banking

Around 40% of respondents reported an increase in the importance of ML and DS for future operations, and a further 10% of banks reported a large increase. None of the banks reported a decrease in the importance of ML and DS. This is a striking finding that runs counter to the suggestion of some commentators that a new ‘AI winter’ might be setting in as a result of reduced investment budgets due to the economic impact of Covid or because pre-pandemic models may no longer be relevant. With the latter, this may be the case since the vast amount of training data required for ML does not account for recent societal and economic changes.

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