BoE has set out results of a survey on the impact of COVID-19 events on the use of machine learning and data science. The survey, which was conducted among 17 UK banks and 9 foreign banks with UK operations, aimed to understand how these technologies are being used by the supervised and regulated firms, assess the potential policy implications of these findings, and support the safe and productive deployment of machine learning and data science across the financial sector. BoE states that it will continue to consider whether new policy initiatives may help firms to realize the benefits and more effectively manage the risks of artificial intelligence, machine learning, and data science. One of the survey findings is that COVID-19 events had a "positive" impact on outsourcing and the use of third-party providers by large banks.
In general, half of the surveyed banks reported an increase in the importance of machine learning and data science as a result of the pandemic, with none of the banks believing that COVID-19 crisis will reduce the importance of machine learning and data science for them. Nearly 40% of the survey respondents reported an increase in the importance of machine learning and data science for future operations and a further 10% of the banks reported a large increase. None of the banks reported a decrease in the importance of machine learning and data science. About a third of the banks said there was an increase in the number of ongoing machine learning and data science applications. Yet only 16% of the banks reported an increase in funding and/or resourcing for existing applications and a similar number reported a decrease. Similarly, nearly 35% of banks reported an increase in the number of planned applications but only 23% of the banks reported an increase in funding and/or resourcing for planned applications and 11.5% of banks reported a decrease. Meanwhile, about 35% of the banks reported a negative impact on machine learning model performance. This is likely because the pandemic has created major movements in macroeconomic variables, such as rising unemployment and mortgage forbearance, which required machine learning (as well as traditional) models to be recalibrated. The pandemic has created a major downturn that could not have been forecasted on the basis of economic data alone or historical predictors.
Additionally, notable differences exist between small and large UK banks with respect to their use of third-party vendor products and services. Smaller banks reported a "positive" impact (for example, in terms of performance, impact, use) of COVID-19 on all categories of data science and machine learning, with data collection, and model testing and validation being the areas with the largest "positive’ impact. Large UK banks reported a "positive" impact on use of outsourced platforms and infrastructure. These findings are in line with market intelligence that smaller banks are looking to increase their use of off-the-shelf machine learning products. This stands to reason, given the generally more substantial in-house data and analytical capabilities of large banks. Alongside the usual risks associated with outsourcing, the use of machine learning and data science can pose additional risks and challenges. For example, outsourced machine learning models may be more difficult to interpret because detailed knowledge in terms of how they were developed resides outside the bank. This can make it more difficult for banks to understand how the model works and to monitor performance, which could result in unexpected or unexplained performance and risks materializing. If multiple banks use the same third-party provider and machine learning model, this could also potentially lead to an increase in herding, concentration, and even the possibility of systemic risks where methodologies are common.
Keywords: Europe, UK, Banking, COVID-19, Machine Learning, Artificial Intelligence, Regtech, Big Data, Outsourcing Arrangements, Third-Party Arrangements, BoE
Leading economist; commercial real estate; performance forecasting, econometric infrastructure; data modeling; credit risk modeling; portfolio assessment; custom commercial real estate analysis; thought leader.
BIS published a paper that provides an overview on the use of big data and machine learning in the central bank community.
APRA finalized the reporting standard ARS 115.0 on capital adequacy with respect to the standardized measurement approach to operational risk for authorized deposit-taking institutions in Australia.
ECB published a guide that outlines the principles and methods for calculating the penalties for regulatory breaches of prudential requirements by banks.
MAS and The Association of Banks in Singapore (ABS) jointly issued a paper that sets out good practices for the management of operational and other risks stemming from new work arrangements adopted by financial institutions amid the COVID-19 pandemic.
ACPR announced that a new data collection application, called DLPP (Datalake for Prudential), for collecting banking and insurance prudential data will go into production on April 12, 2021.
BCB announced that the Financial Stability Committee decided to maintain the countercyclical capital buffer (CCyB) for Brazil at 0%, at least until the end of 2021.
EIOPA has launched a European-wide comparative study on non-life underwriting risk in internal models, also kicking-off of the data collection phase.
SRB published an overview of the resolution tools available in the Banking Union and their impact on a bank’s ability to maintain continuity of access to financial market infrastructure services in resolution.
EBA is consulting on the implementing technical standards for Pillar 3 disclosures on environmental, social, and governance (ESG) risks, as set out in requirements under Article 449a of the Capital Requirements Regulation (CRR).
ESAs Issue Advice on KPIs on Sustainability for Nonfinancial Reporting