The Research of Third-party B2B Supply Chain Finance Credit Risk Identification & Evaluation System

Bao-sen WANG, Zi-jin WANG, Rui WEI

Abstract


With the development of Internet technology, many banks in China launched the “Internet+” supply chain financial services. The optimizing of Supply chain financial structure not only solves the problem that small and medium-sized enterprises financing of convenience and high threshold of loan, but also improves the loan efficiency of the supply chain financial services which achieves a new level and the degree of the participants of the supply chain collaboration. Due to Third-party B2B cooperation mode of supply chain financial services emerging in endlessly, Banks take advantage of the cumulative credit data from Third-party B2B e-commerce platform to issue credit loan and dynamically monitor the credit conditions of small and medium-sized enterprises. Which provide a new solution to long-term information asymmetry problem of supply chain finance service. But the credit risk identification and evaluation change a lot in new service mode. According to analyzing the supply chain finance credit risk changes as result of the evolution from supply chain financial to third-party B2B supply chain finance, this paper contract and analyze the traditional and modern credit risk assessment model. On the basis of above contraction and following the principle of credit risk evaluation index system, this paper build the index system of electronic warehouse financing mode and select the grey evaluation model as the third-party B2B supply chain finance business credit risk assessment model. Multi-level grey credit risk assessment method based on Theil disequilibrium index is used to make comprehensive evaluation on enterprise credit. It can get the evaluation value of the enterprise to judge the enterprise credit rating. This method provide more detailed evaluation method of the third-party B2B supply chain finance business for the banks.

Keywords


“International+” supply chain finance, Credit risk evaluation, Grey evaluation model


DOI
10.12783/dtcse/mmsta2017/19680

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