刘成墉,董天宇,孟令星 :《The Prevention of Financial Legal Risks of B2B E-commerce Supply Chain》，刊发于《Wireless Communications and Mobile Computing》2022年1月。
摘要：
B2B supply chain finance is a new type of financial model, which is created to help companies raise funds and promote the production, operation, and development of companies in the supply chain. Based on the B2B e-commerce platform, it is used for online transactions and transactions between companies and companies. Information, integrating logistics, business flow, information flow, and capital flow for data analysis and processing. When people enjoy convenient and fast e-shopping, they must not only choose products carefully but also understand and be familiar with the relevant laws and regulations of shopping on the Internet. Avoiding potential legal risks is a key factor. The purpose of this article is to analyze the financial risks of the B2B e-commerce supply chain, according to the current Internet development trend, study the legal risks of the B2B e-commerce supply chain in the development, put forward corresponding recommendations, and build a relevant system to reduce risks. Combining some current legal issues faced by e-commerce, this article first analyzes the generation and operation mechanism of credit risk under the B2B platform online supply chain financial business model; then, based on the supply chain financial risk, a relevant system is constructed to reduce risks. This article first analyzes the generation and operation mechanism of credit risk under the online supply chain financial business model of the B2B platform; then, based on the supply chain financial risks, construct a system that can prevent and control the risks generated under this financial business model risk evaluation index system; finally, the KMV model and case are analyzed to verify whether this risk evaluation research is effective for supply chain financial risks. The experimental results show that through the KMV model, comparing the two sets of data, the default distance of most default groups is smaller than that of the normal group. It can be seen that the greater the default distance, the smaller the credit risk. When the default point coefficient is 0.85, use the KMV model which is most obvious when judging the company’s overall probability of default.
