The purpose of this study is to explore a novel Artificial Intelligence (AI)-driven framework for financial risk assessment using a granular approach to assess risk. The framework uses machine learning models in conjunction with granular analysis to minimize inadequate correlation instability of the financial risk models and to identify and quantify financial risks at different levels of abstraction. The method enhances risk prediction accuracy by analyzing financial datasets, capturing elusive patterns and interactions over traditional approaches. Based on a tailored approach, the proposed framework can be applied to a variety of risk models associated credit risk management applications. By combining AI and granular decision-making, financial institutions maximize risk mitigation strategies, adjust dynamically to evolving market conditions, and promote financial stability. As part of this research, the focus is on improving the decision-making skills of chief executive officers (CEOs), Chief Risk Officers (CROs) and chief financial officers (CFOs) in the banking industry in order to face emerging challenges with greater certainty and to be able to visualize the transformed results at granular levels to help identify specific strengths and weaknesses. This paper presents a real-life case study of credit risk management in the banking industry to illustrate the proposed model framework.