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Research on Credit Risk of enterprises based on Logistic Regression and Boost

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DOI: 10.38007/Proceedings.0001190


Ye Yang

Corresponding Author

Ye Yang


In recent years, the number and amount of defaulted debentures in the bond market keep rising, causing annual economic loss more than 100 billion yuan and this trend is growing. The bond market is only a small part of the huge economic market. Because of the contagion effect, the credit risk of the entire market cannot be ignored. As a financing institution, one of the risks that banks faced is credit risk. The main method currently used by banks to measure credit risk is internal ratings based approach, which is a method with a large degree of freedom. AS the global trend tighter regulation deepens, because of the drawbacks of excessive freedom of internal ratings based approach, Basel III restricts it.Therefore, looking for a new method to measure enterprises’ credit risk has become a consideration for financial institutions. This paper tries to find a new method to measure credit risk by using machine learning method.


Credit Risk; Logistic Regression; XGBoost