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Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China

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  • Xuchen Lin
  • Xiaolong Li
  • Zhong Zheng

Abstract

Recent years have witnessed the popularity of online peer-to-peer lending, which allows individuals to borrow from and lend to each other on an Internet-based platform. Using data from a large P2P platform in China, this article explores the factors that determine the default risk based on the demographic characteristics of borrowers. Moreover, we propose a credit risk evaluation model, which can quantify the default risk of each P2P loan. Empirical results reveal that gender, age, marital status, educational level, working years, company size, monthly payment, loan amount, debt to income ratio and delinquency history play a significant role in loan defaults. Finally, we analyse the relationship between default risk and these contributory variables, and the possible causes are also discussed in this study.

Suggested Citation

  • Xuchen Lin & Xiaolong Li & Zhong Zheng, 2017. "Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China," Applied Economics, Taylor & Francis Journals, vol. 49(35), pages 3538-3545, July.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:35:p:3538-3545
    DOI: 10.1080/00036846.2016.1262526
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    Cited by:

    1. Li, ZhouPing & Ge, RuYi & Guo, XiaoShuang & Cai, Lingfei, 2021. "Can individual investors learn from experience in online P2P lending? Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    2. Gaigalienė Asta & Česnys Dovydas, 2018. "Determinants of Default in Lithuanian Peer-To-Peer Platforms," Management of Organizations: Systematic Research, Sciendo, vol. 80(1), pages 19-36, December.
    3. repec:zbw:bofitp:2019_023 is not listed on IDEAS
    4. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
    5. Jin, Ming & Yin, Mingmei & Chen, Zhongfei, 2021. "Do investors prefer borrowers from high level of trust cities? Evidence from China’s P2P market," Research in International Business and Finance, Elsevier, vol. 58(C).
    6. Funke, Michael & Li, Xiang & Tsang, Andrew, 2019. "Monetary policy shocks and peer-to-peer lending in China," BOFIT Discussion Papers 23/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
    7. Ligang Zhou & Chao Ma, 2023. "A Comparison of Different Rules on Loans Evaluation in Peer-to-Peer Lending by Gradient Boosting Models Under Moving Windows with Two Timestamps," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1481-1504, December.
    8. Ji-Yoon Kim & Sung-Bae Cho, 2019. "Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning," Mathematics, MDPI, vol. 7(11), pages 1-17, November.
    9. Wang, Qi & Xiong, Xiong & Zheng, Zunxin, 2021. "Platform Characteristics and Online Peer-to-Peer Lending: Evidence from China," Finance Research Letters, Elsevier, vol. 38(C).
    10. Arif Perdana & Pearpilai Jutasompakorn & Sunghun Chung, 2023. "Shaping crowdlending investors’ trust: Technological, social, and economic exchange perspectives," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-17, December.
    11. Chen, Cathy W.S. & Dong, Manh Cuong & Liu, Nathan & Sriboonchitta, Songsak, 2019. "Inferences of default risk and borrower characteristics on P2P lending," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    12. Zhang, Man & Zhang, Zhiying & Tian, Xiujuan, 2023. "Social identity of civil servants and online peer-to-peer lending: Evidence from China," Economics Letters, Elsevier, vol. 229(C).
    13. Pankaj Kumar Maskara & Emre Kuvvet & Gengxuan Chen, 2021. "The role of P2P platforms in enhancing financial inclusion in the United States: An analysis of peer‐to‐peer lending across the rural–urban divide," Financial Management, Financial Management Association International, vol. 50(3), pages 747-774, September.
    14. Wu, Yu & Zhang, Tong, 2021. "Can credit ratings predict defaults in peer-to-peer online lending? Evidence from a Chinese platform," Finance Research Letters, Elsevier, vol. 40(C).
    15. Tian, Geran & Wang, Xiaowen & Wu, Weixing, 2021. "Borrow low, lend high: Credit arbitrage by sophisticated investors," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    16. Mengyin Li & Phillip H. Phan & Xian Sun, 2021. "Business Friendliness: A Double-Edged Sword," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    17. Kim Sia Ling & Siti Suhana Jamaian & Syahira Mansur & Alwyn Kwan Hoong Liew, 2023. "Modeling Tenant’s Credit Scoring Using Logistic Regression," SAGE Open, , vol. 13(3), pages 21582440231, August.
    18. Chen, Shou & Jiang, Xiangqian & He, Hongbo & Zhou, Xi, 2020. "A pricing model with dynamic repayment flows for guaranteed consumer loans," Economic Modelling, Elsevier, vol. 91(C), pages 1-11.
    19. Jong Wook Lee & So Young Sohn, 2021. "Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-11, December.
    20. Zhang, Yun & Liu, Yun & Zhang, Yifei & Chen, Xin, 2022. "Globalization blueprint and households’ fintech debt: Evidence from China’s One Belt One Road initiative," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 38-55.
    21. Guangyou Zhou & Yijia Zhang & Sumei Luo, 2018. "P2P Network Lending, Loss Given Default and Credit Risks," Sustainability, MDPI, vol. 10(4), pages 1-15, March.

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