Bank of England Report: Machine learning in UK financial services (pdf) Nov 08 2019 16:17 languageMoneyScience
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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. 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. 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. More broadly, ML also raises profound questions around the use of data, complexity of techniques and the automation of processes, systems and decision-making.
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. 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.