This study explores the development and application of credit risk models in modern banking systems. Credit risk—the possibility of a borrower failing to meet contractual obligations— remains a central concern for financial institutions. With the advancement of data analytics and computational techniques, banks have increasingly adopted sophisticated models to assess, measure, and manage credit risk. This paper examines traditional models such as credit scoring and structural models, as well as contemporary approaches including machine learning and artificial intelligence-based models. It further evaluates their effectiveness in improving risk prediction, regulatory compliance, and decision-making processes. The study finds that while modern models offer enhanced predictive accuracy and efficiency, challenges such as data quality, model interpretability, and regulatory constraints persist. The research highlights the importance of integrating advanced technologies with robust risk management frameworks to strengthen financial stability.