Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM.

Weiqiong Fu,Hanxiao Zhang,Fu Huang,Haibin Lv

doi:10.1371/journal.pone.0262222

Weiqiong Fu, Hanxiao Zhang + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0262222

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Journal: PloS one	Publication Date: Jan 21, 2022
Citations: 8	License type: CC BY 4.0

Affiliation: Huizhou University, Guangzhou Automobile Group (China)

Abstract

To better prevent the potential risks in Internet-based Supply Chain Financing (SCF) products, this paper optimizes and evaluates the Internet-based SCF-oriented Credit Risk Evaluation (CRE) method. Firstly, this paper summarizes 12 risk factors of SCF business, establishes a Risk Assessment Index System (RAIS) with good consistency and stability; then, the principles of Backpropagation (BP) Neural Network (NN) is expounded together with Support Vector Machines (SVM) and Genetic Algorithm (GA) model. Consequently, a CRE model is implemented by the NN tools in MATLAB based on the collection of multiple groups of SCF-oriented risk assessment samples. Subsequently, the assessment samples are trained and tested. Finally, the SCF-oriented CRE model is proposed and verified. The results show that the BP-GA model has presented high prediction consistency with the actual classification. According to the comparison of classification results of SVM, BP model, and BP-GA model, the classification accuracy of test samples of the proposed Internet-based SCF-oriented CRE system using BP-GA model reaches 97.19%; the Type I and Type II error rate of the CRE system based on BP-GA model is 7.2% and 14.21%, respectively. Therefore, a suitable SCF-oriented CRE method is put forward for China’s commercial banks along with scientific and feasible suggestions to manage SCF-oriented credit risks more reasonably and effectively.

Highlights

Small and Medium-sized Enterprises (SMEs) account for the most in Chinese businesses and, play an absolutely important role in the development of China’s market economy by contributing over 50% of China’s GDP and tax revenue and helping to solve the largest labor employment
The identification and control of credit risk is the main link for banks to extend credit to SMEs
This paper puts forward the Risk Assessment Index System (RAIS) for Supply Chain Financing (SCF), establishes the SCF-oriented Credit Risk Evaluation (CRE) system based on the Genetic Algorithm (GA)-BP Neural Network (NN) method, and verifies its effectiveness

Summary

Introduction

Small and Medium-sized Enterprises (SMEs) account for the most in Chinese businesses and, play an absolutely important role in the development of China’s market economy by contributing over 50% of China’s GDP and tax revenue and helping to solve the largest labor employment. According to the important role of index scale in the Analytical Hierarchy Process (AHP) scale system, this paper puts forward a four-level score assessment of 10/7/4/0 and uses four principles to select Internet-based SCF credit assessment indexes. Index data should be easy to collect and measure, and qualitative indexes should not add too many objective factors [17, 18]; the principle of independence is to avoid duplication of the information contained in indexes during index selection; the pertinence principle: during index selection, it is necessary to fully consider the business and mode of SCF stocks and select indexes that can comprehensively reflect the credit level of enterprises

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