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Important Factors Determining Fintech Loan Default: Evidence from the LendingClub Consumer Platform

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Abstract

This study examines key default determinants of fintech loans, using loan-level data from the LendingClub consumer platform during 2007–2018. We identify a robust set of contractual loan characteristics, borrower characteristics, and macroeconomic variables that are important in determining default. We find an important role of alternative data in determining loan default, even after controlling for the obvious risk characteristics and the local economic factors. The results are robust to different empirical approaches. We also find that homeownership and occupation are important factors in determining default. Lenders, however, are required to demonstrate that these factors do not result in any unfair credit decisions. In addition, we find that personal loans used for medical financing or small business financing are more risky than other personal loans, holding the same characteristics of the borrowers. Government support through various public-private programs could potentially make funding more accessible to those in need of medical services and small businesses without imposing excessive risk to small peer-to-peer (P2P) investors.

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  • Christophe Croux & Julapa Jagtiani & Tarunsai Korivi & Milos Vulanovic, 2020. "Important Factors Determining Fintech Loan Default: Evidence from the LendingClub Consumer Platform," Working Papers 20-15, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:87815
    DOI: 10.21799/frbp.wp.2020.15
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    Cited by:

    1. Jérémie Bertrand & Laurent Weill, 2022. "In December days are shorter but loans are cheaper," Economic Inquiry, Western Economic Association International, vol. 60(3), pages 1335-1356, July.
    2. Hughes, Joseph P. & Moon, Choon-Geol, 2022. "How bad is a bad loan? Distinguishing inherent credit risk from inefficient lending (Does the capital market price this difference?)," Journal of Economics and Business, Elsevier, vol. 120(C).
    3. Brandon Goldstein & Julapa Jagtiani & Catharine Lemieux, 2023. "Did Fintech Loans Default More During the COVID-19 Pandemic? Were Fintech Firms “Cream-Skimming” the Best Borrowers?," Working Papers 23-26, Federal Reserve Bank of Philadelphia.
    4. Franklin Allen & Xian Gu & Julapa Jagtiani, 2021. "A Survey of Fintech Research and Policy Discussion," Review of Corporate Finance, now publishers, vol. 1(3-4), pages 259-339, July.
    5. Joseph P. Hughes & Julapa Jagtiani & Choon-Geol Moon, 2022. "Consumer lending efficiency: commercial banks versus a fintech lender," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-39, December.
    6. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    7. Alexandra Mora-Cruz & Pedro R. Palos-Sanchez, 2023. "Crowdfunding platforms: a systematic literature review and a bibliometric analysis," International Entrepreneurship and Management Journal, Springer, vol. 19(3), pages 1257-1288, September.
    8. Erik Dolson & Julapa Jagtiani, 2021. "Which Lenders Are More Likely to Reach Out to Underserved Consumers: Banks versus Fintechs versus Other Nonbanks?," Working Papers 21-17, Federal Reserve Bank of Philadelphia.
    9. Krzysztof Waliszewski & Ewa Cichowicz & £ukasz Gêbski & Filip Kliber & Jakub Kubiczek & Pawe³ Niedzió³ka & Ma³gorzata Solarz & Anna Warchlewska, 2023. "The role of the Lendtech sector in the consumer credit market in the context of household financial exclusion," Oeconomia Copernicana, Institute of Economic Research, vol. 14(2), pages 609-643, June.
    10. Gao, Mingze & Leung, Henry & Liu, Linhui & Qiu, Buhui, 2023. "Consumer behaviour and credit supply: Evidence from an Australian FinTech lender," Finance Research Letters, Elsevier, vol. 57(C).
    11. 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.
    12. Franklin Allen & Xian Gu & Julapa Jagtiani, 2022. "Fintech, Cryptocurrencies, and CBDC: Financial Structural Transformation in China”," Working Papers 22-12, Federal Reserve Bank of Philadelphia.
    13. Rocío Maehara & Luis Benites & Alvaro Talavera & Alejandro Aybar-Flores & Miguel Muñoz, 2024. "Predicting Financial Inclusion in Peru: Application of Machine Learning Algorithms," JRFM, MDPI, vol. 17(1), pages 1-25, January.
    14. Allen, Franklin & Gu, Xian & Jagtiani, Julapa, 2022. "Fintech, Cryptocurrencies, and CBDC: Financial Structural Transformation in China," Journal of International Money and Finance, Elsevier, vol. 124(C).
    15. Loutfi, Ahmad Amine, 2022. "A framework for evaluating the business deployability of digital footprint based models for consumer credit," Journal of Business Research, Elsevier, vol. 152(C), pages 473-486.
    16. Danisewicz, Piotr & Elard, Ilaf, 2023. "The real effects of financial technology: Marketplace lending and personal bankruptcy," Journal of Banking & Finance, Elsevier, vol. 155(C).

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    More about this item

    Keywords

    big data; crowdfunding; financial innovations; household finances; lasso selection methods; machine learning; peer-to-peer lending; P2P/marketplace lending;
    All these keywords.

    JEL classification:

    • D10 - Microeconomics - - Household Behavior - - - General
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other

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