The goal of this research is to examine AI-based risk governance models in banking, with a focus on how AI may enhance risk assessment, mitigation, and detection procedures. This study demonstrates how AI-driven governance may improve decision making accuracy, reduce human error, and raise regulatory compliance. Among the challenges that institutions must overcome are worries about data privacy, model interpretability, and ethical considerations. This article evaluates AI’s risk management capabilities using case studies of international organizations. It emphasizes the necessity of a standardized framework for transparent and accountable AI risk management. The topic of early threat identification is explored in this study, along with the role automation and real time data analytics play. The study also examines the impact of AI on operational resilience and cost-effectiveness. It outlines how to strike a balance between prudent risk management and technological innovation.