Credit risk management is an integral aspect of financial stability of financial institutions. The determinants of credit default behavior help in identifying types of credit risk based upon customer’s habits or actions such as purchasing behavior, spending patterns, social interactions, and moral hazard. The awareness on such behavioral factors helps in getting a complete picture in analyzing the credit worthiness of any individual. The onset of artificial intelligence (AI) and machine learning (ML) has remodeled the methodologies of credit risk assessment. Training and developing learning models to predict the borrowers’ behavior evolved along with AI and ML, developing advanced mechanisms for managing credit default risks. Using risk modeling to determine behavioral scoring enables the banks to acquire creditworthy retail borrowers, alongside its inherent demerits of the models. This study is focused on the employment of AI and ML techniques along with qualitative determinants in managing credit risk, drawing attention to the advantages and disadvantages and applications and implications for the financial service providers. The paper examines contemporary literature on modern methods of credit risk management and sheds light on the technological accuracy of credit risk calculations and automated decision-making processes alongside methods to alleviate biases. The study also outlines the sequential phases of the conventional models along with the regulatory and ethical considerations of AI-ML-driven credit risk assessment.