Abstract: Aiming at the problem that the accuracy of credit risk evaluation of supply chain finance mode was affected by credit feature subset and model parameters, we proposed a credit risk evaluation model with particle swarm cooperative optimization. On the basis of fully demonstrating the characteristic index system of supply chain financial risk, we used the binary particle swarm algorithm to optimize the feature subset and optimize parameters of support vector machines. We carried out an experiment on the risk evaluation of supply chain financial credit, and compared it with traditional radial basis support vector machines and feature extraction method of principal component analysis.  The results show that the selected feature subset and SVM parameters of the proposed model can significantly improve the accuracy of credit risk evaluation.

Key words: supply chain finance, credit risk evaluation; particle swarm algorithm, support vector machine （SVM）