This study investigates the implementation of Artificial Intelligence (AI) and Machine Learning (ML) in banking risk management, focusing on credit risk, fraud detection, and regulatory compliance. Using secondary data sources, industry reports, and a GAP Analysis framework, findings reveal that most banking institutions are not fully utilizing AI for core risk functions. Even early adopters apply AI to less critical operations, missing high-impact areas like predictive credit scoring and real-time fraud prevention. The report provides recommendations for Chief Risk Officers on data infrastructure, AI governance, and workforce development.
Risk Management, Artificial Intelligence, Machine Learning, Fraud Detection, Banking Sector, Data Governance, Digital Transformation.