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Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM
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 S...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8782329/ https://www.ncbi.nlm.nih.gov/pubmed/35061798 http://dx.doi.org/10.1371/journal.pone.0262222 |
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author | Fu, Weiqiong Zhang, Hanxiao Huang, Fu |
author_facet | Fu, Weiqiong Zhang, Hanxiao Huang, Fu |
author_sort | Fu, Weiqiong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8782329 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-87823292022-01-22 Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM Fu, Weiqiong Zhang, Hanxiao Huang, Fu PLoS One Research Article 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. Public Library of Science 2022-01-21 /pmc/articles/PMC8782329/ /pubmed/35061798 http://dx.doi.org/10.1371/journal.pone.0262222 Text en © 2022 Fu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fu, Weiqiong Zhang, Hanxiao Huang, Fu Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM |
title | Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM |
title_full | Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM |
title_fullStr | Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM |
title_full_unstemmed | Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM |
title_short | Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM |
title_sort | internet-based supply chain financing-oriented risk assessment using bp neural network and svm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8782329/ https://www.ncbi.nlm.nih.gov/pubmed/35061798 http://dx.doi.org/10.1371/journal.pone.0262222 |
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