Abstract
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.
Highlights
2013 is known as the first year of China’s Internet finance
Authors conduct the training of Back Propagation (BP) neural network by MATLAB programming
Comparing the performance curves of neural networks based on LM algorithm (Levenberg-Marquardt), Scaled Conjugate Gradient, and Bayesian Regularization as training functions, the net work model based on LM algorithm perform best, with only 27 iterations it achieve best testing error result
Summary
2013 is known as the first year of China’s Internet finance. With the comprehensive development of big data, blockchain Internet technologies such as mobile payment, the traditional service business and operation mode of financial system are constantly changing. The Chinese Internet financial business mainly includes the third-party payment, crowdfunding, P2P lending, digital currency, big data finance and others, among which the P2P lending has developed rapidly. According to the statistics of National Internet Finance Association of China (NIFA), the average overdue rate in China’s P2P lending platforms was 3.27% in 2019. By establishing scientific credit risk assessment indicators system and model, this paper provides effective ways to solve the problem of borrower credit risk. Authors focus on the credit risk assessment of borrowers in P2P lending. By summarizing and analyzing the existing research, combining the characteristics of P2P lending industry, authors establish a set of P2P lending borrower credit risk assessment indicators system, which is applicable to China’s online lending. Authors construct a credit risk assessment model of borrowers in P2P lending based on BP neural network
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