*Result*: The Evolution of Bank Regulatory Modeling: Integrating Artificial Intelligence with Traditional Statistical Frameworks.

*The integration of artificial intelligence and machine learning techniques into bank regulatory modeling represents a fundamental shift in how financial institutions approach risk assessment, stress testing, and capital planning. This article examines the evolving landscape where traditional statistical methods such as ordinary least squares regression, logistic regression, and time series models—long valued for their transparency and regulatory acceptance—are being enhanced and complemented by advanced AI techniques, including gradient boosting algorithms, neural networks, and ensemble methods. While machine learning approaches demonstrate superior predictive performance in capturing nonlinear relationships, interaction effects, and regime-dependent behavior that conventional models often miss, their adoption faces significant regulatory hurdles centered on interpretability, auditability, and model governance requirements. The article explores how financial institutions are navigating this tension through hybrid modeling architectures that strategically combine AI capabilities for feature engineering and pattern recognition with interpretable final-stage models that satisfy regulatory transparency demands. Explainable AI frameworks, particularly SHAP and LIME methodologies, are emerging as critical bridges between predictive accuracy and regulatory acceptability, enabling institutions to decompose complex model predictions into understandable feature contributions. The article documents empirical evidence from credit risk modeling, systemic crisis prediction, and stress testing applications, revealing that two-stage hybrid approaches and challenger model frameworks offer pragmatic pathways for integrating machine learning innovations while maintaining compliance with stringent supervisory standards imposed by regulatory authorities, including the Federal Reserve, European Central Bank, and Prudential Regulation Authority. [ABSTRACT FROM AUTHOR]*