The Roles of Big Data and Machine Learning in Bank Supervision

Abstract

Big data and advanced algorithms using machine learning (ML) have changed the way financial products and services are produced, delivered, and consumed, and they have a potential to change the entire financial landscapes. Along with providing customers with better, faster, and cheaper services, big data and ML are being used by financial firms to improve the effectiveness and reduce the costs of regulatory compliance. These new powerful analytical tools come with the new types of risks, including third-party vendor risk, model risk, and cyber security risk. While the supervisory process will adapt over time to the new financial landscapes, bank supervisors will continue to expect users of these complex tools to follow sound principles in managing their risks. There will be no one-size-fits-all compliance solution across the various AI tools, and, in most cases, the initial compliance questions should be based on pre-existing frameworks that were intentionally crafted to be technologically neutral. Financial supervisors’ interest goes beyond ensuring compliance with existing regulations — to understand how these tools are changing the financial system for better and worse.

Keywords: Big Data, Fintech, Machine Learning, Artificial Intelligence, ML and AI, P2P Lending, Third Party Vendor, Model Risk Management, Banking Supervision

JEL Classification: G21, G28, G18, L21