Model Risk Management and Validation Frameworks for Machine Learning Models in Banking:

Author(s)	Theophilus Asiedu Nketiah, Clement Abugri, Joshua Pallapati
Country	United States
Abstract	The infusion of machine learning (ML) into banking has revolutionized credit decisioning, fraud detection, and risk management, but the black-box nature of ML models poses unique challenges for traditional model risk management (MRM). This study provides an overview of recent developments (2020-2025) in MRM frameworks for ML models in banking, including regulatory changes, validation methods, explainability approaches, and upcoming governance issues. We critically examine peer-reviewed papers, regulatory guidelines, and industry reports to determine trends, challenges, and knowledge deficits in the validation and risk management of ML models. There are substantial discrepancies among traditional MRM frameworks, and ML models need prompting model interpretability, bias identification, continuous monitoring, and third-party model governance. Regulatory agencies are currently updating their guidelines, but uniform validation protocols remain in a gap. Explainable AI methods are promising but face scalability limitations. Novel ML model risk management will necessitate a fundamental rethink of validation frameworks that also include dynamic monitoring, fairness testing, strong governance structures, and interdisciplinary knowledge.
Field	Computer Applications
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-19
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.66703

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