A Review of Algorithmic Accountability and Model Risk Management Approaches in Financial Services
Abstract
The adoption of machine learning and other algorithmic methods across credit underwriting, fraud detection, algorithmic trading, and customer-facing advice has outpaced the regulatory and governance structures originally built for simpler quantitative models. This paper reviews two overlapping but historically separate strands of oversight in financial services: model risk management , the supervisory discipline built around frameworks such as SR 11-7, and algorithmic accountability, the newer body of law and practice concerned with fairness, transparency, and explainability of automated decisions. Drawing on U.S., U.K., and E.U. regulatory sources, supervisory surveys, and the applied machine learning literature, the paper traces how frameworks such as the NIST AI Risk Management Framework, the U.K. Prudential Regulation Authority's SS1/23, and the CFPB's adverse-action guidance have each tried to extend accountability into algorithmic systems, and where their assumptions still strain against adaptive, opaque, and vendor-supplied models. A case study of Zillow's 2021 iBuying failure illustrates that model risk failures need not involve discrimination to be financially destructive, broadening the practical scope of what accountability regimes should capture. The review concludes that durable governance depends on integrating fairness and explainability testing directly into the model lifecycle rather than treating them as a parallel compliance exercise, and it identifies gaps, including inconsistent fairness metrics and fragmented cross-border rules, that remain open as of 2023.
References
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