Prediction of Supply Chain Financial Credit Risk Based on PCA-GA-SVM Model

Abstract

Supply Chain Finance (SCF) is a new type of financing business carried out by commercial banks on the basis of supply chain management, which effectively promotes the healthy development of the supply chain. As the most typical mode of SCF, accounts receivable financing mode can use the part of accounts receivable occupying working capital for financing, which is widely used. In order to effectively manage the credit risk in the Supply Chain Finance and maintain the healthy operation of the supply chain, this paper proposes a supply chain financial credit risk prediction model based on PCA-GA-SVM. First, principal component analysis (PCA) is used to reduce the dimension of the original index system, and then genetic algorithm (GA) is used to optimize the parameters of support vector machine (SVM). Finally, the principal components selected by PCA are input into the GA-SVM model for training, and the final prediction model is established. The running results show that the prediction performance of PCA-GA-SVM model is better than that of SVM and GA-SVM models. It has a good generalization ability, which can be used as a reference for commercial banks to improve the credit risk management ability of Supply Chain Finance and is conducive to the sustainable development of supply chain finance business.

Navigating Growth: The Nexus of Supply Chain Finance, Digital Maturity, and Financial Health in Chinese A-Share Listed Corporations

“…Internationally, scholars tend to explore supply chain finance from the perspectives of corporate operations and finance, while in China, research focuses more on corporate management practices. Additionally, existing studies have concentrated on topics such as supply chain finance models, risk management, and green supply chain finance [9][10][11][12][13].…”

Apply RF-LSTM to Predicting Future Share Price

“…Combined [1] with a self -regulating mobile average model, index smooth model and RNN to form a hybrid model. The verification consequence indicate that the hybrid model is greater than simply using RNN; Li Haiyan [2] adopts the main component analysis method [2]. The method of combining PCA, genetic algorithms, and BPNN is used for the price prediction of the share marker, and the results prove that the preciseness of the prediction is more accurate; Y. J. BAEK [3] put forward a classical model named LSTM that prevents overfitting and the share market index prediction framework as the foundation of the LSTM model.…”

Bibliometric Analysis of Credit Risk Based on the Web of Science (WOS)

“…With the rapid development of technology and data, emerging models based on machine learning and artificial intelligence have emerged. Models such as support vector machine (SVM) (Harris, 2013), random forest (Wang, 2022a), deep learning (Zhang et al, 2020), and neural networks (Li & Fu, 2023;Wang, 2022b) have been shown to have higher predictive ability and flexibility in credit risk assessment. For example, Li & Fu (2023) proposed a credit risk prediction model based on PCA-GA-SVM for supply chain finance.…”

“…Models such as support vector machine (SVM) (Harris, 2013), random forest (Wang, 2022a), deep learning (Zhang et al, 2020), and neural networks (Li & Fu, 2023;Wang, 2022b) have been shown to have higher predictive ability and flexibility in credit risk assessment. For example, Li & Fu (2023) proposed a credit risk prediction model based on PCA-GA-SVM for supply chain finance. The running results show that the model has good genera-lization ability and can provide a reference for commercial banks to improve the credit risk management ability of Supply Chain Finance.…”