Credit risk analysis based on machine learning methods

Zenghui Jiang; Xinghao Wang

doi:10.1117/12.2646350

23 August 2022 Credit risk analysis based on machine learning methods

Zenghui Jiang, Xinghao Wang

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 1233013 (2022) https://doi.org/10.1117/12.2646350
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

Credit risk management, which is the basis of the credit application, is the most perfect embodiment in the bank credit application and asset supervision. The ultimate purpose of credit risk management is to ensure that credit fund is of safety, profitability and fluidity. At present, it is extremely important for commercial banks to set up an early bank risk warning system. Therefore, this paper considers the use of six kind of machine learning methods to model credit risk management problems. We treat the problem as a classification problem and use the data from LendingClub company. The six methods are the neural network, random forest, XGBoost, LGB, CatBoost, and Logistic Regression. The result of the experiment shows that the accuracy of the six methods all exceed 88%. Furthermore, the neural network gets the best AUC score, which can better meet the demand of commercial banks’ credit risk management.

Citation Download Citation

Zenghui Jiang and Xinghao Wang "Credit risk analysis based on machine learning methods", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 1233013 (23 August 2022); https://doi.org/10.1117/12.2646350

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