From Burden to Advantage: Leveraging AI/ML for Regulatory Reporting in US Banking

Abstract

Machine Learning (ML) revolutionizes prediction processes, making them more cost-effective and precise. As the volume and diversity of financial data continue to grow, ML becomes increasingly valuable. One significant implication for regulators is the banking sector's growing reliance on ML methods for decision-making, which inherently lack full understanding by their creators. Consequently, regulators across all levels will increasingly encounter ML models that are challenging to fully grasp.Regulatory scrutiny is affected as supervisors must assess model risk. ML models incorporate numerous and intricate features, requiring examiners to comprehend their implications for transparency and associated operational risks. Moreover, utilizing historical data to train models may raise concerns related to fair lending practices. Already, some banks and FinTech firms employ ML across various banking services, including fraud detection, risk management, and pricing.Policy formulation may also feel the impact through two main channels: operational risk and market behavior. ML directly influences model risk, a subset of operational risk. Banks, bound by model risk management regulatory guidance established in April 2011, may find certain aspects of this guidance challenging to apply to ML tools due to their opaque nature. Furthermore, ML could potentially alter market behavior for certain liquid assets.