Bank Credit Risk Early Warning Model Based on Machine Learning Decision Trees
DOI:
https://doi.org/10.5281/zenodo.11627011ARK:
https://n2t.net/ark:/40704/JETBM.v1n3a04PURL:
https://purl.archive.org/suas/JETBM.v1n3a04References:
26Keywords:
Credit Risk Management, Decision Tree Model, Loan Default Prediction, Machine Learning In FinanceAbstract
The study explores the application of the C5.0 decision tree algorithm to improve bank credit risk management. By transforming risk identification from subjective judgment to objective analysis, risk measurement from qualitative to quantitative, and risk control from static to dynamic, banks can enhance their credit risk management practices. Using data from the Center for Machine Learning and Intelligent Systems, 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, by leveraging key features such as checking and savings balances, can significantly enhance the accuracy and efficiency of bank credit risk assessments.
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