[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/p/fip/fedpwp/87815.html
   My bibliography  Save this paper

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

Author

Listed:
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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://www.philadelphiafed.org/-/media/frbp/assets/working-papers/2020/wp20-15.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.21799/frbp.wp.2020.15?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Giuseppe De Luca & Valeria Perotti, 2011. "Estimation of ordered response models with sample selection," Stata Journal, StataCorp LP, 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. Heckman, James, 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Hu, Xiao & Jin, Ye & Li, Yilin & Wu, Banggang, 2023. "Learning from credit default," Finance Research Letters, Elsevier, vol. 58(PD).
    3. Gunay, Samet & Goodell, John W. & Muhammed, Shahnawaz & Kirimhan, Destan, 2023. "Frequency connectedness between FinTech, NFT and DeFi: Considering linkages to investor sentiment," International Review of Financial Analysis, Elsevier, vol. 90(C).
    4. 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.
    5. 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).
    6. 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).
    7. 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.
    8. Cornelli, Giulio & Frost, Jon & Gambacorta, Leonardo & Jagtiani, Julapa, 2024. "The impact of fintech lending on credit access for U.S. small businesses," Journal of Financial Stability, Elsevier, vol. 73(C).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Danisewicz, Piotr & Elard, Ilaf, 2023. "The real effects of financial technology: Marketplace lending and personal bankruptcy," Journal of Banking & Finance, Elsevier, vol. 155(C).
    15. 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.
    16. 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.
    17. Choudhary, Priya & Thenmozhi, M., 2024. "Fintech and financial sector: ADO analysis and future research agenda," International Review of Financial Analysis, Elsevier, vol. 93(C).
    18. 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.
    19. 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).
    20. 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.

    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. Bollaert, Helen & Lopez-de-Silanes, Florencio & Schwienbacher, Armin, 2021. "Fintech and access to finance," Journal of Corporate Finance, Elsevier, vol. 68(C).
    2. 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.
    3. Thomas J. Chemmanur & Michael B. Imerman & Harshit Rajaiya & Qianqian Yu, 2020. "Recent Developments In The Fintech Industry," Journal of Financial Management, Markets and Institutions (JFMMI), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 1-31, June.
    4. Nam, Rachel J., 2022. "Open banking and customer data sharing: Implications for FinTech borrowers," SAFE Working Paper Series 364, Leibniz Institute for Financial Research SAFE.
    5. Kowalewski, Oskar & Pisany, Paweł, 2022. "Banks' consumer lending reaction to fintech and bigtech credit emergence in the context of soft versus hard credit information processing," International Review of Financial Analysis, Elsevier, vol. 81(C).
    6. Zhang, Xiaoyan & Li, Jinbao & Xiang, Dong & Worthington, Andrew C., 2023. "Digitalization, financial inclusion, and small and medium-sized enterprise financing: Evidence from China," Economic Modelling, Elsevier, vol. 126(C).
    7. Nigmonov, Asror & Shams, Syed & Alam, Khorshed, 2024. "Liquidity risk in FinTech lending: Early impact of the COVID-19 pandemic on the P2P lending market," Emerging Markets Review, Elsevier, vol. 58(C).
    8. 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.
    9. 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.
    10. Kräussl, Roman & Kräussl, Zsofia & Pollet, Joshua & Rinne, Kalle, 2024. "The performance of marketplace lenders," Journal of Banking & Finance, Elsevier, vol. 162(C).
    11. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    12. Biancini, Sara & Verdier, Marianne, 2023. "Bank-platform competition in the credit market," International Journal of Industrial Organization, Elsevier, vol. 91(C).
    13. Jiang, Jinglin & Liao, Li & Wang, Zhengwei & Zhang, Xiaoyan, 2021. "Government Affiliation and Peer-To-Peer Lending Platforms in China," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 87-106.
    14. Cuadros-Solas, Pedro J. & Cubillas, Elena & Salvador, Carlos, 2023. "Does alternative digital lending affect bank performance? Cross-country and bank-level evidence," International Review of Financial Analysis, Elsevier, vol. 90(C).
    15. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    16. Oskar Kowalewski & Pawel Pisany & Emil Slazak, 2021. "What determines cross-country differences in fintech and bigtech credit markets?," Working Papers 2021-ACF-02, IESEG School of Management.
    17. Nicola Branzoli & Ilaria Supino, 2020. "FinTech credit: a critical review of empirical research," Questioni di Economia e Finanza (Occasional Papers) 549, Bank of Italy, Economic Research and International Relations Area.
    18. 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.
    19. DeFusco, Anthony A. & Tang, Huan & Yannelis, Constantine, 2022. "Measuring the welfare cost of asymmetric information in consumer credit markets," Journal of Financial Economics, Elsevier, vol. 146(3), pages 821-840.
    20. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:fip:fedpwp:87815. 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: Beth Paul (email available below). General contact details of provider: https://edirc.repec.org/data/frbphus.html .

    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.