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Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform

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  • Croux, Christophe
  • Jagtiani, Julapa
  • Korivi, Tarunsai
  • Vulanovic, Milos

Abstract

This study examines the default determinants of Fintech loans, utilizing a sample of more than a million of personal loans that were originated through the LendingClub consumer platform during the period 2007–2018. We identify a robust set of contractual loan characteristics, borrower characteristics, and macroeconomic variables that are important in determining the likelihood of default, such as loan maturity, homeownership, loan purposes, occupation, etc. We also find an important role of alternative data in determining the default, even after controlling for the obvious risk characteristics of the borrowers, loan characteristics, and the local economic factors. The results are robust to different empirical approaches. Results imply that it would be important for regulators to provide greater transparency in terms of guidance and regulatory clarity on which alternative data can be used legally without violating fair lending rules. Lenders need to pay closer attention to how they make decisions and understand their own decisions that may be driven by complex algorithms inside the “black boxes.”

Suggested Citation

  • Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
  • Handle: RePEc:eee:jeborg:v:173:y:2020:i:c:p:270-296
    DOI: 10.1016/j.jebo.2020.03.016
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    1. Giuseppe De Luca & Valeria Perotti, 2011. "Estimation of ordered response models with sample selection," Stata Journal, StataCorp LLC, vol. 11(2), pages 213-239, June.
    2. Peter Koudijs & Hans-Joachim Voth, 2016. "Leverage and Beliefs: Personal Experience and Risk-Taking in Margin Lending," American Economic Review, American Economic Association, vol. 106(11), pages 3367-3400, November.
    3. Thomas Philippon, 2016. "The FinTech Opportunity," NBER Working Papers 22476, National Bureau of Economic Research, Inc.
    4. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    5. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    6. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    7. Robert J. Shiller, 2007. "Understanding recent trends in house prices and homeownership," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 89-123.
    8. Jagtiani, Julapa & Lemieux, Catharine, 2018. "Do fintech lenders penetrate areas that are underserved by traditional banks?," Journal of Economics and Business, Elsevier, vol. 100(C), pages 43-54.
    9. Calebe de Roure & Loriana Pelizzon & Anjan Thakor, 2022. "P2P Lenders versus Banks: Cream Skimming or Bottom Fishing? [Loan officer incentives, internal rating models and default rates]," The Review of Corporate Finance Studies, Society for Financial Studies, vol. 11(2), pages 213-262.
    10. Bhanot, Syon P., 2017. "Cheap promises: Evidence from loan repayment pledges in an online experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 140(C), pages 246-266.
    11. Mollick, Ethan, 2014. "The dynamics of crowdfunding: An exploratory study," Journal of Business Venturing, Elsevier, vol. 29(1), pages 1-16.
    12. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    13. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    14. Daniel Björkegren & Darrell Grissen, 0. "Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment," The World Bank Economic Review, World Bank, vol. 34(3), pages 618-634.
    15. Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2020. "Dissecting Characteristics Nonparametrically," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    16. Robert J. Shiller, 2007. "Understanding recent trends in house prices and homeownership," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 89-123.
    17. Buchak, Greg & Matvos, Gregor & Piskorski, Tomasz & Seru, Amit, 2018. "Fintech, regulatory arbitrage, and the rise of shadow banks," Journal of Financial Economics, Elsevier, vol. 130(3), pages 453-483.
    18. Julapa Jagtiani & Catharine Lemieux, 2019. "The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform," Financial Management, Financial Management Association International, vol. 48(4), pages 1009-1029, December.
    19. Huan Tang, 2019. "Peer-to-Peer Lenders Versus Banks: Substitutes or Complements?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1900-1938.
    20. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    21. 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.
    22. Boris Vallée & Yao Zeng, 2019. "Marketplace Lending: A New Banking Paradigm?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1939-1982.
    23. James Heckman, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    24. Laurie S. Goodman & Christopher Mayer, 2018. "Homeownership and the American Dream," Journal of Economic Perspectives, American Economic Association, vol. 32(1), pages 31-58, Winter.
    25. Andrew Hertzberg & Andres Liberman & Daniel Paravisini, 2018. "Screening on Loan Terms: Evidence from Maturity Choice in Consumer Credit," The Review of Financial Studies, Society for Financial Studies, vol. 31(9), pages 3532-3567.
    26. Lukas Meier & Sara Van De Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71, February.
    27. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    28. Manconi, Alberto & Braggion, Fabio & Zhu, Haikun, 2018. "Can Technology Undermine Macroprudential Regulation? Evidence from Peer-to-Peer Credit in China," CEPR Discussion Papers 12668, C.E.P.R. Discussion Papers.
    29. Kräussl, Roman & Kräussl, Zsofia & Pollet, Joshua & Rinne, Kalle, 2018. "The performance of marketplace lenders: Evidence from lending club payment data," CFS Working Paper Series 598, Center for Financial Studies (CFS).
    30. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 649-688, August.
    31. Julapa Jagtiani & Lauren Lambie-Hanson & Timothy Lambie-Hanson, 2019. "Fintech Lending and Mortgage Credit Access," Working Papers 19-47, Federal Reserve Bank of Philadelphia.
    32. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    33. Van de Ven, Wynand P. M. M. & Van Praag, Bernard M. S., 1981. "The demand for deductibles in private health insurance : A probit model with sample selection," Journal of Econometrics, Elsevier, vol. 17(2), pages 229-252, November.
    34. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    35. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    36. Andrija Đurović, 2017. "Estimating Probability of Default on Peer to Peer Market – Survival Analysis Approach," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 6(2), pages 149-167.
    37. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    38. Robert M. Adams, 2018. "Do Marketplace Lending Platforms Offer Lower Rates to Consumers?," FEDS Notes 2018-10-22, Board of Governors of the Federal Reserve System (U.S.).
    39. Jefferson Duarte & Stephan Siegel & Lance Young, 2012. "Trust and Credit: The Role of Appearance in Peer-to-peer Lending," The Review of Financial Studies, Society for Financial Studies, vol. 25(8), pages 2455-2484.
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    Keywords

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    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
    • G - Financial Economics

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