Research on Bank Credit Risk and Default Customer Identification Integrating Machine Learning Techniques

This paper aims to explore the application of machine learning technology in bank credit risk management and default customer identification. By outlining the fundamental theoretical framework of credit risk, the concepts, classifications, and key influencing factors are clarified. The analysis focuses on the application of machine learning in credit risk identification, covering its suitability, core algorithms, and practical advantages and limitations. Addressing the specific needs of default customer identification, the paper further examines how machine learning enhances the accuracy of identifying default customers and proposes various model optimization strategies. Through in-depth analysis of relevant theories and technologies, this paper seeks to provide financial institutions with effective technical support and theoretical foundations for managing credit risks.