An Empirical Study On Credit Risk Evaluation Of Chinese Commercial Bank

As the circulation system of the global economic runs, as a populous country, China has become one of the increasingly active in the international financial and economic system. In recent years, China’s economic strength, comprehensive national power is emerging in the international market. China’s financial market has attracted more and more attention from foreign Banks. Foreign Banks begin to pour into China, not only intensify the competition situation of domestic banking industry, also increase the Chinese banking credit risk of complications. The nature of the business of commercial bank determines the importance of China’s commercial bank credit risk. In order to realize long-term stable development, Domestic commercial Banks must establish scientific and perfect risk prevention system. Commercial credit risk not only determines the commercial bank asset quality up or down, the frequency of the liquidity crisis, but also is a threat to the entire financial industry and even the global economic operation system. Sofinding out a suitable method for our country commercial bank credit risk evaluation has important practical significance.In order to explore the construction of the credit risk evaluation model of China’s commercial bank, and provide effective theoretical basis for the current situation of the development of the commercial bank of China, putting the corporate credit as the breakthrough point of the study, this paper builds a reasonable and effective evaluation index system of credit risk of commercial bank, and establishes the China commercial bank credit risk evaluation of binomial Logistic regression and B-P neural network model.The article selects the six aspects of financial data including profitability, operation ability, debt paying ability, development ability, capital structure, enterprise scale and so on of the not ST companies listed in Shanghai in 2014 and ST companies. Usingmethods of K-S normality test, paired sample T test, nonparametric statistical analysis, multicollinearity of variable selection, finally the paper establishes the binomial Logistic regression model and B-P neural network model, and by comparingthe accuracy of the prediction model of two kinds of comparison, finally choose the applicable model for the development of commercial banking credit risk evaluation, which is suit for the need of China’s current situation.