Evaluation of financial credit risk on pharmaceutical supply chain based on support vector machine

This paper selects the financial data of 80 listed pharmaceutical small and medium-sized enterprises (SMEs) to build a credit risk evaluation system, and compares the risk evaluation effect of Logistic model and support vector machine (SVM) model on the basis of factor analysis. The results show that the overall prediction accuracy of SVM model is 3.7% higher than that of Logistic model, and the first type of error rate is lower at 12.3%, which indicates the superiority and effectiveness of SVM model applied to the credit risk evaluation of the pharmaceutical industry under the supply chain finance (SCF) mode.