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...

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Autores principales:	Zhengwei Ma, Wenjia Hou, Dan Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ea7fc0dd8d984bbc93a6cc417a041abf
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spelling	oai:doaj.org-article:ea7fc0dd8d984bbc93a6cc417a041abf2021-12-02T20:15:19ZA credit risk assessment model of borrowers in P2P lending based on BP neural network.1932-620310.1371/journal.pone.0255216https://doaj.org/article/ea7fc0dd8d984bbc93a6cc417a041abf2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255216https://doaj.org/toc/1932-6203Peer-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.Zhengwei MaWenjia HouDan ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255216 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhengwei Ma Wenjia Hou Dan Zhang A credit risk assessment model of borrowers in P2P lending based on BP neural network.
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	article
author	Zhengwei Ma Wenjia Hou Dan Zhang
author_facet	Zhengwei Ma Wenjia Hou Dan Zhang
author_sort	Zhengwei Ma
title	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_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_sort	credit risk assessment model of borrowers in p2p lending based on bp neural network.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ea7fc0dd8d984bbc93a6cc417a041abf
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A credit risk assessment model of borrowers in P2P lending based on BP neural network.

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