Artificial Intelligence is increasingly central to predictive and behavioral analytics, especially in risk management and compliance within financial institutions. By analyzing large volumes of financial and non-financial data, AI improves risk identification, measurement, and mitigation. It enables early warning and learning systems in lending, governance, cybersecurity, AML, and fraud prevention, often predicting issues before they materialize. AI in risk management relies on techniques such as machine learning, natural language processing, segmentation analytics, evolutionary programming, cognitive RPA, and visual analytics, with ML, NLP, and segmentation most widely used. Applications span credit scoring, portfolio optimization, behavioral modelling, enterprise risk management, and financial crime detection. AI tools enhance customer segmentation, creditworthiness assessment using alternative data, stress testing, fraud detection, alert prioritization, and case investigation through pattern recognition and graph analytics. Overall, AI significantly increases accuracy, speed, and effectiveness of risk and compliance processes while reducing manual effort and operational inefficiencies.