Credit Risk Evluation Of Supply Chain Finance Based On Sem And Logistic Regression

In the 2020,under the impact of the COVID-19,the prospect of the world economic environment was unclear.Looking back at China,although the international economic situation is severe,it still presents a stable and positive trend,which cannot be separated from the support of many emerging industries,among which new energy vehicle industry is a typical representative.The new energy vehicle industry involves many industries,and when conducting research on this industry,it is not only necessary to study related automobile manufacturing enterprises,but also to analyze all related enterprises from the perspective of the financing attributes of supply chain finance.However,unlike traditional financing methods,supply chain finance increases the participation of relevant credit lenders,leading to the emergence of new credit risks and seriously affecting the development of credit business in the new energy vehicle industry.Based on this,it is crucial to accurately measure and evaluate the credit risk of supply chain finance,avoid new credit risks when financial institutions conduct supply chain finance business,and how relevant enterprises avoid credit risks.In previous studies,scholars have paid more attention to the mode of supply chain finance and the research on the influencing factors of credit risk,neglecting the issue of how these influencing factors affect credit risk.In response to this issue,this article selects financial data from enterprises related to the new energy vehicle industry from the first quarter of 2015 to the third quarter of 2022.These financial indicators are extracted through Deep Belief Network(DBN)and factor analysis(FA),and a supply chain finance credit risk measurement model is established by using Structural Equation Model(SEM).We uses logistic regression method to predict and provide recommendations on the credit risk of supply chain finance in the new energy enterprise industry.This method can not only measure credit risk under supply chain finance,but also reflect the causal relationship between financial indicators of enterprises.The main content of the paper includes the following three parts:We have constructed a credit risk evaluation index system for supply chain finance.The financial data of enterprises related to the new energy vehicle industry has the characteristics of large dimensions and high complexity,making it difficult to conduct credit risk assessment research on them.Based on this,using the DBN method,deep level features are extracted from the original financial data,combined with FA to further extract the deep level features.By utilizing the high interpretability of FA,the factors that have the greatest impact on credit risk are identified from these feature indicators,and a credit risk evaluation index system is constructed.We have constructed a credit risk measurement model for supply chain finance.Based on the supply chain finance credit risk evaluation index system,credit risk measurement models for core enterprises and financing enterprises were constructed using SEM.Considering that the explicit variable corresponding to supply chain finance is credit risk,the two risk measurement models were fused to form the entire supply chain finance credit risk measurement model.Combined with the standardized path map of supply chain finance credit risk developed by SEM,the impact relationships between various indicators can be obtained.We have predicted the credit risk of supply chain finance and proposed prevention suggestions for the credit risk issue of supply chain finance.We predicted the credit risk of supply chain finance in the new energy vehicle industry through multiple classification logistic regression and tested the predictive effect of the indicators.Propose credit risk prevention suggestions for relevant enterprises based on the four financing models of supply chain finance.