Credit Risk Assessment of Peer-to-Peer Lending Borrower Utilizing BP Neural Network

2017 ◽  
pp. 22-33
Author(s):  
Zhengnan Yuan ◽  
Zihao Wang ◽  
He Xu
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Credit Risk ◽  
Bp Neural Network ◽  
Peer To Peer ◽  
Credit Risk Assessment ◽  
Peer Lending

scholarly journals A credit risk assessment model of borrowers in P2P lending based on BP neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255216
Author(s):  
Zhengwei Ma ◽  
Wenjia Hou ◽  
Dan Zhang
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Credit Risk ◽  
Bp Neural Network ◽  
Assessment Model ◽  
P2p Network ◽  
Risk Assessment Model ◽  
Credit Risk Assessment ◽  
P2p Lending ◽  
Lm Algorithm

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.

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