Enhancing Bank Credit Risk Management Using the C5.0 Decision Tree Algorithm

The study explores the application of the C5.0 decision tree algorithm to improve bank credit risk management. Banks can enhance their credit risk management practices by transforming risk identification from subjective judgment to objective analysis, risk measurement from qualitative to quantitative, and risk control from static to dynamic. Using Center for Machine Learning and Intelligent Systems data, we constructed a C5.0 decision tree model to predict high-risk bank loans. The model's performance was evaluated through various metrics, including a confusion matrix, revealing an error rate of 14.9%. The study demonstrates that decision tree models can significantly enhance the accuracy and efficiency of bank credit risk assessments by leveraging key features such as checking and savings balances.