IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v59y2024ics154461232301156x.html
   My bibliography  Save this article

Predicting failure of P2P lending platforms through machine learning: The case in China

Author

Listed:
  • Yeh, Jen-Yin
  • Chiu, Hsin-Yu
  • Huang, Jhih-Huei

Abstract

This study employs machine learning models to predict the failure of Peer-to-Peer (P2P) lending platforms, specifically in China. By employing the filter method and wrapper method with forward selection and backward elimination, we establish a rigorous and practical procedure that ensures the robustness and importance of variables in predicting platform failures. The research identifies a set of robust variables that consistently appear in the feature subsets across different selection methods and models, suggesting their reliability and relevance in predicting platform failures. The study highlights that reducing the number of variables in the feature subset leads to an increase in the false acceptance rate while the performance metrics remain stable, with an AUC value of approximately 0.96 and an F1 score of around 0.88. The findings of this research provide significant practical implications for regulatory authorities and investors operating in the Chinese P2P lending industry.

Suggested Citation

  • Yeh, Jen-Yin & Chiu, Hsin-Yu & Huang, Jhih-Huei, 2024. "Predicting failure of P2P lending platforms through machine learning: The case in China," Finance Research Letters, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:finlet:v:59:y:2024:i:c:s154461232301156x
    DOI: 10.1016/j.frl.2023.104784
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S154461232301156X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.104784?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    2. Boyle, Glenn & Stover, Roger & Tiwana, Amrit & Zhylyevskyy, Oleksandr, 2015. "The impact of deposit insurance on depositor behavior during a crisis: A conjoint analysis approach," Journal of Financial Intermediation, Elsevier, vol. 24(4), pages 590-601.
    3. Gao, M. & Yen, J. & Liu, M., 2021. "Determinants of defaults on P2P lending platforms in China," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 334-348.
    4. Degryse, Hans & Matthews, Kent & Zhao, Tianshu, 2018. "SMEs and access to bank credit: Evidence on the regional propagation of the financial crisis in the UK," Journal of Financial Stability, Elsevier, vol. 38(C), pages 53-70.
    5. Yichen Rao, 2021. "Dreaming like a market: The hidden script of financial inclusion in China's P2P lending platforms," Economic Anthropology, Wiley Blackwell, vol. 8(1), pages 102-115, January.
    6. Yuwei Yan & Zhihan Lv & Bin Hu, 2018. "Building investor trust in the P2P lending platform with a focus on Chinese P2P lending platforms," Electronic Commerce Research, Springer, vol. 18(2), pages 203-224, June.
    7. Che, Xin & Liebenberg, Andre P., 2017. "Effects of business diversification on asset risk-taking: Evidence from the U.S. property-liability insurance industry," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 122-136.
    8. Shuai Shao & Hong Bo, 2022. "Behavioural aspects of China's P2P lending," The European Journal of Finance, Taylor & Francis Journals, vol. 28(1), pages 30-45, January.
    9. Wang, Qian & Su, Zhongnan & Chen, Xinyang, 2021. "Information disclosure and the default risk of online peer-to-peer lending platform," Finance Research Letters, Elsevier, vol. 38(C).
    10. Yeujun Yoon & Yu Li & Yan Feng, 2019. "Factors affecting platform default risk in online peer-to-peer (P2P) lending business: an empirical study using Chinese online P2P platform data," Electronic Commerce Research, Springer, vol. 19(1), pages 131-158, March.
    11. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    12. Xueru Chen & Xiaoji Hu & Shenglin Ben, 2021. "How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China," Electronic Commerce Research, Springer, vol. 21(4), pages 1055-1082, December.
    13. Jiang, Cuixia & Xu, Qifa & Zhang, Weiming & Li, Mengting & Yang, Shanlin, 2018. "Does automatic bidding mechanism affect herding behavior? Evidence from online P2P lending in China," Journal of Behavioral and Experimental Finance, Elsevier, vol. 20(C), pages 39-44.
    14. Daniela Gabor & Sally Brooks, 2017. "The digital revolution in financial inclusion: international development in the fintech era," New Political Economy, Taylor & Francis Journals, vol. 22(4), pages 423-436, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jen-Yin Yeh & Hsin-Yu Chiu & Jhih-Huei Huang, 2023. "Predicting Failure of P2P Lending Platforms through Machine Learning: The Case in China," Papers 2311.14577, arXiv.org.
    2. Evangelos Katsamakas & J. Manuel Sanchez-Cartas, 2024. "A computational model of the effects of borrower default on the stability of P2P lending platforms," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 14(3), pages 597-618, September.
    3. Sha, Yezhou, 2022. "Rating manipulation and creditworthiness for platform economy: Evidence from peer-to-peer lending," International Review of Financial Analysis, Elsevier, vol. 84(C).
    4. Zhao Wang & Cuiqing Jiang & Huimin Zhao, 2022. "Know Where to Invest: Platform Risk Evaluation in Online Lending," Information Systems Research, INFORMS, vol. 33(3), pages 765-783, September.
    5. Ying Liu & Rui Wang & Jin Qin, 2021. "CEO influence on P2P platform survival: Education and experience do matter!," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(3), pages 622-634, April.
    6. Na Sun & Liangrong Song & Yan Sun, 2021. "Fuze Effect: A Landmine in the Way of Sustainable Development of FinTech—The Lessons from the Peer-To-Peer Risk Outbreak," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    7. Chong, Zhaohui & Wei, Xiaolin, 2023. "Exploring the spatial linkage network of peer-to-peer lending in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    8. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    9. Xueru Chen & Xiaoji Hu & Shenglin Ben, 2021. "How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China," Electronic Commerce Research, Springer, vol. 21(4), pages 1055-1082, December.
    10. Xi Yang & Wenjuan Fan & Shanlin Yang, 2020. "Identifying the Influencing Factors on Investors’ Investment Behavior: An Empirical Study Focusing on the Chinese P2P Lending Market," Sustainability, MDPI, vol. 12(13), pages 1-21, July.
    11. 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.
    12. 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).
    13. Mousumi Munmun & Dongli Zhang & Charles C. Luo, 2024. "Peer-to-Peer Lending Performance Improvement: Learn from Lean Principles," International Journal of Business and Management, Canadian Center of Science and Education, vol. 19(1), pages 101-101, February.
    14. Ajay Byanjankar & József Mezei & Markku Heikkilä, 2021. "Data‐driven optimization of peer‐to‐peer lending portfolios based on the expected value framework," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(2), pages 119-129, April.
    15. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    16. Chen, Pei-Fen & Lo, Shihmin & Tang, Hai-Yuan, 2022. "What if borrowers stop paying their loans? Investors’ rates of return on a peer-to-peer lending platform," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 359-377.
    17. Li, Jianwen, 2023. "MSMEs meet FinTech: Chance or challenge?," Finance Research Letters, Elsevier, vol. 57(C).
    18. Chen, Rongda & Chen, Yikai & Jin, Chenglu & Xu, Guorui & Bao, Weiwei & Guo, Kenan, 2021. "Characteristics and mechanisms of not-fully marketized interest rates: Evidence from Chinese online lending," Research in International Business and Finance, Elsevier, vol. 55(C).
    19. Pang, Professor Sulin & Hou, Xianyan & Xia, Lianhu, 2021. "Borrowers’ credit quality scoring model and applications, with default discriminant analysis based on the extreme learning machine," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    20. Golnoosh Babaei & Shahrooz Bamdad, 2020. "A neural‐network‐based decision‐making model in the peer‐to‐peer lending market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 142-150, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:59:y:2024:i:c:s154461232301156x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.