Application of Data Mining Algorithms in Bank Credit Risk Assessment: Review and Prospect

Against the backdrop of intensified competition in the financial market and increased demand for risk management and control, the wave of digital transformation is profoundly reshaping the landscape of the financial industry. The accumulation of massive amounts of data has brought new opportunities and challenges for financial institutions to innovate business models and enhance risk management capabilities. This paper focuses on the application of data mining algorithms in bank credit risk assessment. Through literature review and case analysis, it explores the application status, effects, challenges faced, and looks ahead to future development trends. The findings domenstrate that data mining algorithms can significantly improve the accuracy of credit risk assessment and decision-making efficiency, but there are also issues such as data quality and algorithm interpretability. Future studies should focus onalgorithm integration, enhanced interpretability, and combination with emerging technologies will be the development directions. Banks should actively respond by improving data management and technology application strategies.