Credit risk assessment of P2P lending platform towards big data based on BP neural network

被引:18
|
作者
Guo, Yiping [1 ]
机构
[1] ZhengZhou ShengDa Univ Econ Business & Management, Sch Finance & Trade, Zhengzhou 451191, Peoples R China
关键词
Peer to peer; Credit risk assessment; Logistic regression; BP neural network; Big data; PREDICTION;
D O I
10.1016/j.jvcir.2019.102730
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Peer-to-peer (P2P) lending platform plays a significant role in modern financial systems. However, due to improper supervision, credit risk is inevitable. In this paper, we analyze the traditional financial risk and information technology risk of P2P lending platform. In order to evaluate the performance of assessment algorithms, we present a BP neural network-based algorithm for lending risk assessment. To achieve our task, we crawled large-scale lending data for 2015-2019. Logistic regression is used to compare with BP neural network method. Experimental results show that BP neural network-based algorithm outperforms traditional Logistic regression algorithm and the proposed method can effectively reduce investor risk. (c) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页数:4
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