Research on Risk Management of Internet Supply Chain Financial Service Platform：Evidence from China - Yu, Zongchao (2020)
The technological innovation of Internet Data has spawned numbers of new Internet financial companies, and brought many new Internet financial products derived from traditional financial services. Internet-based supply chain financial products have become popular in recent years, and Internet financial companies have launched their own supply chain financial products. The measurement and prevention of Internet supply chain financial risks also need to keep pace with the times according to their characteristics. This dissertation chooses China, whose Internet supply chain market has developed rapidly in recent years, as the research background, and takes the Dual-chain financial service platform of Ant Financial as a case to analyze, mainly from two aspects of operational risk and credit risk. In the case analysis part, this dissertation describes the business structure and operation mode of the blockchain technology independently developed by the Dual-chain financial service platform. Relying on the data services and technology provided by Alibaba, a traditional e-commerce platform, Ant Financial has established a financial platform to provide core enterprises in the supply chain and upstream and downstream SMEs with a full chain of financial services. As it has the advantage of online marketing traffic, Ant Financial dual-chain supply chain financial service platform can quickly meet the financing needs of corporate customers and provide wealth management services; at the same time, it uses the mature blockchain technology derived from Ant Technology. To establish a scientific data risk control model to provide guarantee for the security of its financial services. In the empirical analysis part, based on the risk management of the traditional supply chain financial platform, this dissertation establishes a credit risk evaluation system that adapts to the characteristics of the Internet supply chain platform in accordance with scientific and effective principles. The empirical research object of this paper selects financing companies and core companies in the automotive supply chain, and standardizes their data, uses R language software to compile programs, and optimizes the credit risk evaluation model based on the SVM model. In the study, the classification effects of various kernel functions were compared, and the penalty coefficient C and gamma value of the SVM model based on the radial basis function (RBF) kernel function were adjusted by heuristics to optimize the data prediction accuracy of the model. The adjusted SVM model has an accuracy of 92.75% in judging whether to issue loans to financing companies in the supply chain. The classification effect is stable, proving that SVM is an effective method for credit risk evaluation.