Abstract：
This paper takes 325 listed companies of Chinese A-shares and their 2011 and 2012 financial data as testing samples, it employs the tests of normality, significance and correlation to choose effective credit risk identification indexes, and respectively uses the Bayes discriminant method, Logistic regression model and BP neural network model to distinguish credit risk, and then compares their accuracy, predictability and stability. The results show that the degrees of accuracy for identifying credit risks are gradually increasing by the above three models. However, there is still the probability that the company whose credit situation is unhealthy can be identified as healthy one. The important financial indicators identified by Bayes discriminant method and Logistic regression model can effectively explain the company's credit status, while BP neural network model is unable to interpret the recognition results. The results provide an important reference for selecting and using proper technology of credit risk identification in commercial banks.
LIU Xiang-dong, WANG Wei-qing.A Comparative Study on Three Models for Credit Risk Identification in Chinese Commercial Banks[J] Economic Survey, 2015,V32(6): 132-137
2015 
