BoE Survey Shows Positive COVID Impact on Outsourced Banking Services

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.

Related Links

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• Detailed Survey Results

Keywords: Europe, UK, Banking, COVID-19, Machine Learning, Artificial Intelligence, Regtech, Big Data, Outsourcing Arrangements, Third-Party Arrangements, BoE