This paper examines deeper and profound implications of artificial intelligence on risk management practices for the banking and financial services industry. As AI technology continues to penetrate nearly every facet of finance, more banks embrace AI algorithms, including decision trees, random forests, and neural networks, with some related machine learning techniques aimed at improving their risk assessment processes and decision-making processes and mitigation strategies. In this context, the research study orients itself towards the application of AI in risk management and proceeds further to evaluate the efficacy and accuracy of AI models against conventional methods, using statistical tests and confidence intervals in order to justify such differences. Ethical considerations, such as algorithmic bias and data privacy, will be also discussed in depth, including strategies for mitigating such risks. The study of the present article outlines the transformative capacity of AI in banking risk management and its implications for the industry through an inclusive review of current literature and case studies, particularly in cybersecurity and risk assessment.