Research On Credit Evaluation Of Small-medium Enterprises With Commercial Banks

Commercial Banks is the biggest source of direct financing in our country. CommercialBanks make great contribution to modern society, but the social transition rely on the credit ofbank, which leads to China’s commercial Banks with more stress, higher risk, so that it cancause the systemic financial risk of banks. There are many risks in Commercial Banks, but thecredit risk is the biggest one, mainly refers to the customer credit default, especially the faulttolerance of small-medium enterprises is less than the large-scale enterprise, and its credit riskproblem is more serious. On the one hand, it can cause huge losses, and weak the Banks’profitability; On the other hand, it also can increase burden of the bad assets, even reduce thecredit conditions of banks, and trigger a bank failure. Therefore, the credit risk managementof the small-medium enterprises is the in the key and the most challenging job for commercialbanks. For the credit risk management of the small-medium enterprises, the most effectiveway is understanding the credit conditions of enterprises, and rating credit, then usingeffective credit strategy based on the credit level.First, this paper reviews research on credit evaluation of the small-medium enterprises,and according to the characteristics of credit evaluation in small-medium enterprises, weconstruct a credit evaluation index system of the small-medium enterprises completely, whichreflects the credit conditions more comprehensively; Then, we use the principal componentanalysis method to processing the index data, and extracting some comprehensive principalcomponents to replace the original index system, then simplifies the data structure andreduces data correlation for the input data of the subsequent model; On the basis of usingLSSVM to evaluate the credit of small-medium enterprises, through this model to solvecomplexity and nonlinear problem of the data, so that we can find the most optimumregression function. Through empirical analysis shows that, the credit evaluation performancebased on principal component analysis and LSSVM is better than the BP neural network.This paper by using principal component analysis and LSSVM model to evaluate thecredit of the small-medium enterprises, not only established a scientific credit evaluationsystem of small-medium enterprise for commercial Banks, but also it have more accuratequantitative and analysis for the credit conditions of the small-medium enterprises, which canprovide the theoretical and tool for decision making. SO it can strengthen the credit risksupervision for commercial banks, and guard against and dissolve the credit risk, thenimprove the quality of the assets of the bank, which has a great theoretical significance and practical value for c ommercial banks.