A prediction model of credit risk in SCF based on IG-GA-SVM

YingJun Fu; MeiYan Li; XiaoNi Sun; BoChao Shang

doi:10.1117/12.2659354

22 November 2022 A prediction model of credit risk in SCF based on IG-GA-SVM

YingJun Fu, MeiYan Li, XiaoNi Sun, BoChao Shang

Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124750T (2022) https://doi.org/10.1117/12.2659354
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China

Abstract

To manage the credit risk in supply chain financing (SCF) more effectively and maintain the healthy operation of the supply chain, this paper proposes a prediction model of credit risk in SCF based on IG-GA-SVM. Firstly, the information gain (IG) is used to extract the feature indexes of the original index system, and then the genetic algorithm (GA) is used to optimize the parameters of the support vector machine (SVM). Finally, the feature indexes selected by the information gain are input into the GA-SVM model for training, and the final prediction model is established. The test results show that the performance of the prediction model based on IG-GA-SVM is better than that of BP, SVM, and GA-SVM models. It has good generalization ability and can provide strong decision support for financial institutions to provide financing for small and medium-sized enterprises (SMEs).

Citation Download Citation

YingJun Fu, MeiYan Li, XiaoNi Sun, and BoChao Shang "A prediction model of credit risk in SCF based on IG-GA-SVM", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124750T (22 November 2022); https://doi.org/10.1117/12.2659354

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