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
相关论文
共 50 条
  • [21] Personal Credit Risk Evaluation Model of P2P Online Lending Based on AHP
    Wu, Fengpei
    Su, Xiang
    Ock, Young Seok
    Wang, Zhiying
    SYMMETRY-BASEL, 2021, 13 (01): : 1 - 20
  • [22] Leveraging network topology for credit risk assessment in P2P lending: A comparative study under the lens of machine learning
    Liu, Yiting
    Baals, Lennart John
    Osterrieder, Jorg
    Hadji-Misheva, Branka
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 252
  • [23] Analysis on Credit Risk Assessment of P2P
    Xia, Lei
    Li, Jun-feng
    PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT: CORE THEORY AND APPLICATIONS OF INDUSTRIAL ENGINEERING (VOL 1), 2016, : 907 - 914
  • [24] Improving credit risk assessment in P2P lending with explainable machine learning survival analysis
    Gero Friedrich Bone-Winkel
    Felix Reichenbach
    Digital Finance, 2024, 6 (3): : 501 - 542
  • [25] Research of P2P traffic identification based on BP neural network
    Shen Fuke
    Change Pan
    Ren Xiaoli
    2007 THIRD INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING, VOL II, PROCEEDINGS, 2007, : 75 - +
  • [26] Research on Crisis Perception Model of P2P Network Lending Platform
    Chen, Gang
    PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON ENGINEERING SCIENCE AND MANAGEMENT (ESM), 2016, 62 : 72 - 75
  • [27] P2P Lending Risk Contagion Analysis Based on a Complex Network Model
    Wei, Qi
    Zhang, Qiang
    DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2016, 2016
  • [28] 2-stage modified random forest model for credit risk assessment of P2P network lending to "Three Rurals" borrowers
    Rao, Congjun
    Liu, Ming
    Goh, Mark
    Wen, Jianghui
    APPLIED SOFT COMPUTING, 2020, 95
  • [29] Influencing factor analysis of credit risk in P2P lending based on interpretative structural modeling
    Ma, Hui-Zi
    Wang, Xiang-Rong
    JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY, 2016, 19 (03): : 777 - 786
  • [30] Building investor trust in the P2P lending platform with a focus on Chinese P2P lending platforms
    Yuwei Yan
    Zhihan Lv
    Bin Hu
    Electronic Commerce Research, 2018, 18 : 203 - 224