Enterprise Credit Identification And Risk Management

Small and medium-sized enterprises' financing difficulty is a prominent problem in China's economic growth.Financing is difficult because an ISME is not as big as a large company with current assets,property mortgage and enterprise guarantee.As a result,the existing SME trust recognition mainly depends on the field research and tracking investigation of commercial banks.The audit process is complicated and time-consuming,moreover,there are human subjective factors.It is a very meaningful study to make use of the large data platform of government credit information,to identify the trust of small and medium-sized enterprises and to establish the management of credit risk.In this paper,a big data platform based on the identification of small and medium-sized enterprises credit trust,different from the previous credit analysis is mainly based on the company's financial statements information,but the use of government departments to master together,all the relevant business information,analysis of the cycle can be from the financial information of the annual report quarterly to monthly data so as to shorten.A more comprehensive,more timely reflect the development status of the enterprise.The BP neural network algorithm and the nonlinear decision tree are constructed to identify the possible conditions and factors of the credit,and the importance judgement of the possible conditions is obtained.From the perspective of model accuracy and robustness,the empirical results show that the nonlinear decision tree has good prediction effect,and the result of random forest also shows that nonlinear decision tree has strong robustness.At the same time,based on the non parametric method,we use the benchmark model to establish the corporate credit recognition model,and compare the accuracy of the credit recognition model before and after the establishment of the benchmark model.Finally,this article based on the prospect of the formation of a small and medium-sized enterprise credit risk early warning,making accurate credit service to every enterprise.