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Determinants of borrowers' default in P2P lending under consideration of the loan risk class

Author

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  • Michal Polena

    (School of Economics and Business Administration, Friedrich-Schiller-University Jena)

  • Tobias Regner

    (School of Economics and Business Administration, Friedrich-Schiller-University Jena)

Abstract

We study the determinants of borrowers' default in P2P lending with a new data set consisting of 70,673 loan observations from Lending Club. Previous research identified a number of default determining variables but did not distinguish between different loan risk levels. We define four loan risk classes and test the significance of the default determining variables within each loan risk class. Our findings suggest that the significance of most variables depends on the loan risk class. Only few variables are consistently significant across all risk classes. The debt-to-income ratio, inquiries in the past 6 months and a loan intended for a small business are positively correlated with the default rate. Annual income and credit card as loan purpose are negatively correlated.

Suggested Citation

  • Michal Polena & Tobias Regner, 2016. "Determinants of borrowers' default in P2P lending under consideration of the loan risk class," Jena Economics Research Papers 2016-023, Friedrich-Schiller-University Jena.
  • Handle: RePEc:jrp:jrpwrp:2016-023
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    8. Mingfeng Lin & Nagpurnanand R. Prabhala & Siva Viswanathan, 2013. "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 59(1), pages 17-35, August.
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    Cited by:

    1. Nigmonov, Asror & Shams, Syed & Alam, Khorshed, 2022. "Macroeconomic determinants of loan defaults: Evidence from the U.S. peer-to-peer lending market," Research in International Business and Finance, Elsevier, vol. 59(C).
    2. Marta Kłosok & Marcin Chlebus, 2020. "Towards better understanding of complex machine learning models using Explainable Artificial Intelligence (XAI) - case of Credit Scoring modelling," Working Papers 2020-18, Faculty of Economic Sciences, University of Warsaw.
    3. 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.
    4. Yan Wang & Xuelei Sherry Ni, 2020. "Improving Investment Suggestions for Peer-to-Peer (P2P) Lending via Integrating Credit Scoring into Profit Scoring," Papers 2009.04536, arXiv.org.
    5. Teply, Petr & Polena, Michal, 2020. "Best classification algorithms in peer-to-peer lending," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    6. Serena Gallo, 2021. "Fintech platforms: Lax or careful borrowers’ screening?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-33, December.
    7. Lu, Haitian & Wang, Bo & Wang, Haizhi & Zhao, Tianyu, 2020. "Does social capital matter for peer-to-peer-lending? Empirical evidence," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    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. Yanyan Cui & Lixin Liu, 2022. "Investor sentiment-aware prediction model for P2P lending indicators based on LSTM," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.

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    More about this item

    Keywords

    crowdfunding; peer-to-peer lending; P2P; credit grade; FICO score; default risk;
    All these keywords.

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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