We conducted a survey of UK banks in August 2020 to understand the impact of the Covid pandemic on their use of machine learning (ML) and data science (DS). It is important that we understand how these technologies are being used by the firms we supervise and regulate, their impact on the wider economy and the potential policy implications. Ultimately, we aim to support the safe and productive deployment of ML and DS across the financial sector.
Half of the banks surveyed reported an increase in the importance of ML and DS as a result of the pandemic and none of the banks believe that Covid will reduce the importance of ML and DS for them. However, only a third of banks said there was an increase in the number of planned or existing ML or DS projects.
ML and DS are important because they 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 processes. And some banks use the technologies to help provide financial services to underserved or unbanked customers. Our survey found that around 35% of banks reported a ‘positive’ impact from Covid on the ML and DS technologies that support remote working among employees.
However, existing risks may be amplified or new risks may emerge from the use of ML and DS in financial services. For example, ML models may perform poorly when applied to a situation they have not encountered before in the training data. This is particularly relevant in the context of the Covid pandemic when the underlying data may have changed (data drift) or the statistical properties of the data may have changed (concept drift).
In our survey, around 35% of banks reported a negative impact on ML model performance as a result of Covid (Chart A). This is likely because the pandemic has created a major downturn that could not have been forecasted on the basis of economic data alone or historical predictors.
We will continue to monitor these developments closely, along with other regulators like the Financial Conduct Authority, and take necessary steps to support the safe adoption of ML and DS in financial services. As Covid has resulted in changes in model performance, more continuous monitoring and validation is required to mitigate this risk, compared to static validation and testing methods.
Chart A: Covid had a negative impact on the performance of banks’ machine learning models
Issues (opportunities and risks) that existing machine learning and data science applications have encountered as a result of Covid
Footnotes
- Source: Bank of England (2020), ‘ML, DS and Covid survey’.
- Notes:
- Chart A indicates where ML and DS had ‘positive’ or negative effects as a result of Covid. However, the numbers do not tell us the extent of these effects, beyond small or large, nor indeed how they may impact banks’ business models or financial performance. More research is needed to gauge how material the affected ML/DS models are to banks’ overall performance, operations and risk profile, and hence the overall impact of the crisis.
- The survey consists of 32 submissions in total, with 17 from UK banks, nine from foreign banks with operations in the UK, and six from insurers. The sample of insurers was too small to be judged representative of the sector and the results are not included in this article. Note that, although the survey only covers 26 banks, the assets of those banks account for around 88% of all UK bank assets.
This post has been prepared with the help of Oliver Thew, Mohammed Gharbawi and David Bholat.
For more detail on the impact of Covid on machine learning and data science in UK banking, read our 2020 Q4 Quarterly Bulletin article.
Share your thoughts with us at BankOverground@bankofengland.co.uk.
