Credit Risk Assessment Of SMES According To The Theory Of Supply Chain Finance

In recent years, the number of Small and medium enterprises (SMEs) in China has increased rapidly. They play an significant role in promoting economic growth, maintaining social stability, creating jobs. However, SMEs are facing a lot of difficulties, especially the financing problem, which has become the biggest obstacle to their survival and development. SMEs’ status of credit facilities doesn’t match their role in the economic development. They face a lot of credit gap. We should focus on how to effectively ease the financing difficulties of SMEs, especially credit financing difficulties. The main methods to solve the financing problems of Small and medium enterprises are: constructing financing system and financial support system, adopting macroeconomic policies, etc. Taking the current development status of China’s financial market into account, more time will be needed through these pathways.Therefore, this article proposes to solve financing problems of SMEs by using supply chain. Supply chain finance breaks the traditional limitations of a single enterprise, it stands overall height, not only solve the long-standing financing problem, but also extend the services of financial institutions. This approach can help ease the financing difficulties of SMEs. How to help banks and other financial institutions to make a reasonable credit risk assessment based on supply chain financing is necessary. The existing methods have some flaws. The binary logistic regression model is not required to meet the assumptions of normal distribution and equal variance between the variables. And the model is easy to explain. Therefore, this paper choose logistic regression model for credit risk. First, a credit risk evaluation index system of supply chain financing is designed, this system includes 23 indicators. Then select 51 SMEs that had received loans from bank. Among these companies,46 had repaid bank loans,5 companies had non-performing loans. According to the survey of 51 SMEs, this research analyzes the corporate data by using PASW Statistics 18. Extract eight principal components through principal component analysis, which includes supply chain operation, solvency, assets under finance, characteristics of pledge, corporate profitability, business operation, enterprise development potential and the initial capital level. Then perform logistic regression analysis through forward stepwise selection, select variables retained in the model.P values SMEs’probability of default. Contrast supply chain financing model and general financing model, supply chain financing model have higher prediction accuracy rate and goodness of fit. Finally, issues can be further explored is proposed on the basis of this research.