Keywords
supply chain finance
business operation management
support vector machine
How to Cite
Abstract
With the advancement of supply chain finance, the consideration of credit risk evaluation becomes increasingly important. This paper briefly analyzed supply chain business operations and management and established a credit risk evaluation system. Then, the support vector machine (SVM) was chosen as the method for credit risk evaluation, and an improved bottle sea sheath algorithm (ISSA) was developed to search for the optimal SVM parameters. The ISSA-SVM algorithm was then obtained. Experiments were conducted using data from the manufacturing supply chain finance sector. The results demonstrated that the ISSA performed well in finding the optimal solution. The ISSA-SVM exhibited superior performance in credit risk evaluation, achieving an accuracy rate of 0.981, along with F1 and area under the curve (AUC) values of 0.975 and 0.912, respectively. These results outperformed logistic regression and other algorithms, validating the reliability of the ISSA-SVM for credit risk evaluation. The method can be applied in the operational management of actual supply chain finance.
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