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A credit risk assessment model of borrowers in P2P lending based on BP neural network
Peer-to-Peer (P2P) lending provides convenient and efficient financing channels for small and medium-sized enterprises and individuals, and therefore it has developed rapidly since entering the market. However, due to the imperfection of the credit system and the influence of cyberspace restrictions...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330911/ https://www.ncbi.nlm.nih.gov/pubmed/34343180 http://dx.doi.org/10.1371/journal.pone.0255216 |
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author | Ma, Zhengwei Hou, Wenjia Zhang, Dan |
author_facet | Ma, Zhengwei Hou, Wenjia Zhang, Dan |
author_sort | Ma, Zhengwei |
collection | PubMed |
description | Peer-to-Peer (P2P) lending provides convenient and efficient financing channels for small and medium-sized enterprises and individuals, and therefore it has developed rapidly since entering the market. However, due to the imperfection of the credit system and the influence of cyberspace restrictions, P2P network lending faces frequent borrower credit risk crises during the transaction process, with a high proportion of borrowers default. This paper first analyzes the basic development of China’s P2P online lending and the credit risks of borrowers in the industry. Then according to the characteristics of P2P network lending and previous studies, a credit risk assessment indicators system for borrowers in P2P lending is formulated with 29 indicators. Finally, on the basis of the credit risk assessment indicators system constructed in this paper, BP neural network is built based on the BP algorithm, which is trained by the LM algorithm (Levenberg-Marquardt), Scaled Conjugate Gradient, and Bayesian Regularization respectively, to complete the credit risk assessment model. By comparing the results of three mentioned training methodologies, the BP neural network trained by the LM algorithm is finally adopted to construct the credit risk assessment model of borrowers in P2P lending, in which the input layer node is 9, the hidden layer node is 11 and output layer node is 1. The model can provide practical guidance for China and other countries’ P2P lending platforms, and therefore to establish and improve an accurate and effective borrower credit risk management system. |
format | Online Article Text |
id | pubmed-8330911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83309112021-08-04 A credit risk assessment model of borrowers in P2P lending based on BP neural network Ma, Zhengwei Hou, Wenjia Zhang, Dan PLoS One Research Article Peer-to-Peer (P2P) lending provides convenient and efficient financing channels for small and medium-sized enterprises and individuals, and therefore it has developed rapidly since entering the market. However, due to the imperfection of the credit system and the influence of cyberspace restrictions, P2P network lending faces frequent borrower credit risk crises during the transaction process, with a high proportion of borrowers default. This paper first analyzes the basic development of China’s P2P online lending and the credit risks of borrowers in the industry. Then according to the characteristics of P2P network lending and previous studies, a credit risk assessment indicators system for borrowers in P2P lending is formulated with 29 indicators. Finally, on the basis of the credit risk assessment indicators system constructed in this paper, BP neural network is built based on the BP algorithm, which is trained by the LM algorithm (Levenberg-Marquardt), Scaled Conjugate Gradient, and Bayesian Regularization respectively, to complete the credit risk assessment model. By comparing the results of three mentioned training methodologies, the BP neural network trained by the LM algorithm is finally adopted to construct the credit risk assessment model of borrowers in P2P lending, in which the input layer node is 9, the hidden layer node is 11 and output layer node is 1. The model can provide practical guidance for China and other countries’ P2P lending platforms, and therefore to establish and improve an accurate and effective borrower credit risk management system. Public Library of Science 2021-08-03 /pmc/articles/PMC8330911/ /pubmed/34343180 http://dx.doi.org/10.1371/journal.pone.0255216 Text en © 2021 Ma 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 Ma, Zhengwei Hou, Wenjia Zhang, Dan A credit risk assessment model of borrowers in P2P lending based on BP neural network |
title | A credit risk assessment model of borrowers in P2P lending based on BP neural network |
title_full | A credit risk assessment model of borrowers in P2P lending based on BP neural network |
title_fullStr | A credit risk assessment model of borrowers in P2P lending based on BP neural network |
title_full_unstemmed | A credit risk assessment model of borrowers in P2P lending based on BP neural network |
title_short | A credit risk assessment model of borrowers in P2P lending based on BP neural network |
title_sort | credit risk assessment model of borrowers in p2p lending based on bp neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330911/ https://www.ncbi.nlm.nih.gov/pubmed/34343180 http://dx.doi.org/10.1371/journal.pone.0255216 |
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