A Regulatory Governance Framework for AI-Driven Financial Fraud Detection in U.S. Banking: Integrating OCC, SR 11-7, CFPB, and FinCEN Compliance Requirements for Model Development, Validation, and Monitoring Lifecycles

Researchers present the RGF-AFFD, an integrated governance framework for AI-driven fraud detection in U.S. banking that unifies compliance requirements from four regulatory bodies (OCC, SR 11-7, CFPB, FinCEN). The framework includes a Regulatory Digital Twin meta-model that benchmarks six AI architectures, with an LSTM+XGBoost ensemble achieving 0.9289 ROC-AUC, and establishes continuous monitoring protocols to satisfy fragmented regulatory requirements simultaneously.

U.S. financial institutions deploying artificial intelligence for fraud detection operate within a fragmented regulatory landscape where four separate governance frameworks—OCC Bulletin 2011-12, SR 11-7, CFPB circular guidance, and FinCEN BSA/SAR requirements—impose overlapping yet disconnected compliance obligations. This paper addresses a critical gap by proposing the first unified governance architecture that maps model development, validation, and monitoring lifecycles directly to all four regulatory frameworks. The research moves beyond theoretical compliance mapping by empirically validating the framework using 590,540 transactions from the IEEE-CIS dataset and 284,807 from the ULB benchmark. Six machine learning architectures underwent rigorous testing including temporal drift analysis and fairness assessments, with the LSTM+XGBoost ensemble demonstrating superior fraud detection performance at 0.9289 ROC-AUC and a 6:1 benefit-cost ratio. Notably, XGBoost showed significantly better temporal stability than LSTM, a critical finding for production environments where model degradation poses regulatory risk. The Regulatory Digital Twin meta-model translates technical performance metrics into four regulator-specific health scores and a composite Regulatory Fitness Index, enabling banks to demonstrate continuous compliance. For the banking industry, this framework reduces compliance fragmentation costs while improving fraud detection reliability. For regulators, it provides a transparent audit trail connecting model performance to regulatory requirements. The paper's policy recommendations suggest regulatory harmonization opportunities, potentially reducing the compliance burden on institutions. Community banks particularly benefit from the implementation vignette provided, offering a roadmap for resource-constrained institutions navigating complex AI governance.

• →LSTM+XGBoost ensemble achieved 0.9289 ROC-AUC with 6:1 benefit-cost ratio on fraud detection benchmarks
• →RGF-AFFD is the first framework simultaneously addressing OCC, SR 11-7, CFPB, and FinCEN compliance requirements
• →XGBoost demonstrated superior temporal stability (delta-AUC -0.0017) versus LSTM (-0.0626), critical for production models
• →Regulatory Digital Twin meta-model translates technical metrics into regulator-specific health scores for continuous monitoring
• →Framework includes community bank implementation vignette and four evidence-based policy recommendations for regulatory harmonization