A Regulatory Governance Framework for AI-Driven Financial Fraud Detection in US banking integrating OCC, SR 11-7, CFPB and FinCEN compliance requirements for model development, validation and monitoring lifecycles

US financial institutions deploying AI-based fraud detection face a fragmented compliance landscape spanning four regulatory frameworks – OCC Bulletin 2011-12, SR 11-7, the CFPB AI circular and FinCEN BSA/SAR requirements – with no integrated governance life cycle connecting these requirements to model development, validation and monitoring practice. This paper presents the Regulatory Governance Framework for AI-Driven Financial Fraud Detection (RGF-AFFD), a three-tier governance architecture empirically anchored in a multi-study empirical program and corroborated by independent published evidence. Using the IEEE-CIS Fraud Detection dataset (590,540 transactions; data collected 2017–2018, Kaggle competition 2019) and the ULB Credit Card Fraud benchmark (284,807 transactions; September 2013 collection), we benchmark six architectures including an LSTM+XGBoost ensemble and conduct ablation, temporal drift, SHAP interpretability and BISG fairness analyses. The LSTM+XGBoost ensemble achieves ROC-AUC of 0.9289 (F1: 0.6360) with a benefit–cost ratio of 6:1. XGBoost demonstrates the strongest temporal stability (ΔAUC = −0.0017 versus −0.0626 for LSTM), while network/account-linkage features are the primary causal fraud signal (ΔAUC = −0.0294 on removal). The RGF-AFFD is the first integrated deployment blueprint – to the authors’ knowledge – to simultaneously satisfy OCC, SR 11-7, CFPB and FinCEN requirements, supported by a community bank implementation vignette and four evidence-based policy recommendations.