Evaluation On The Credit Risk Of SME Under The Model Of Supply Chain Finance

Small and medium-sized enterprises are very important for the Chinese economy,and they play an irreplaceable role in promoting economic growth,alleviating the pressure of employment,and optimizing economic structure.However,SMEs have the characteristics of low collateralized fixed assets and low credit rating,so it is difficult for SMEs to obtain loans from banks.Not only that,the problem of financing has always affected the development of SMEs in China.In this context,the business of supply chain finance has emerged.The launch of the supply chain finance business has ushered in the dawn of solving the financing difficulties for SMEs.In the supply chain finance business,credit assessment conducted by commercial banks not only evaluate SMEs themselves but also focuse on the whole Supply Chain.The credit ratings of SMEs can be improved by having good creditworthiness of core enterprises,and as a consequence it increases the chance of obtaining loans for SMEs.Therefore,the supply chain finance business can alleviate the financing difficulties of SMEs.However,the credit risk of SMEs is a major risk faced by commercial banks in the development of supply chain finance business,but China has not yet established a comprehensive credit risk assessment system for supply chain finance to help banks effectively avoid credit risks when dealing with supply chain finance business.Based on these concerns,this paper evaluates the credit risk of SMEs conducted by commercial banks from the perspective of supply chain finance.Firstly,according to the relevant theory and the characteristics of supply chain finance,the paper analyze the factors that affect the credit risk of SMEs under the supply chain finance model,and then a credit risk assessment index system is built based on supply chain finance.secondly,comparing the advantages and disadvantages of the existing credit risk assessment methods,and selecting a support vector machine(SVM)as the credit risk assessment model;during the process of model solving,the complex quadratic programming problem is converted into solving a linear equation system by introducing a quadratic loss function,and as a result least squares support vector machine model(LSSVM)is constructed;The particle swarm optimization algorithm(PSO)is used to optimize the parameters of the least squares support vector machine model,and the PSO-LSSVM model is also established as an evaluation model for the credit risk of SMEs under the supply chain finance model.Finally,for both the supply chain financial index system and the traditional index system,the PSO-LSSVM and Logit models are used to compare the empirical results of the SME credit risk under the supply chain finance model.The comparison results show that the assessment results under the supply chain financial index system are better than those under the traditional index system.In addition,it also shows that the PSO-LSSVM model has higher prediction accuracy for supply chain financial credit risk than the Logit model.Therefore,the PSO-LSSVM model constructed in this paper has important theoretical value and practical significance,and it can provide scientific and rational decision-making advice for commercial banks when assessing the credit risk of SMEs under the model of supply chain finance.