IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/26165.html
   My bibliography  Save this paper

Predicting Consumer Default: A Deep Learning Approach

Author

Listed:
  • Stefania Albanesi
  • Domonkos F. Vamossy

Abstract

We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation.

Suggested Citation

  • Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," NBER Working Papers 26165, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26165
    Note: EFG ME
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w26165.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Victor Rios-Rull & Dean Corbae: & Satyajit Chatterjee, 2011. "A Theory of Credit Scoring and the Competitive Pricing of Default Risk," 2011 Meeting Papers 1115, Society for Economic Dynamics.
    2. Dean Corbae & Andrew Glover, 2018. "Employer Credit Checks: Poverty Traps versus Matching Efficiency," NBER Working Papers 25005, National Bureau of Economic Research, Inc.
    3. Sumit Agarwal & Souphala Chomsisengphet & Neale Mahoney & Johannes Stroebel, 2015. "Regulating Consumer Financial Products: Evidence from Credit Cards," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(1), pages 111-164.
    4. Kartik Athreya & Xuan S. Tam & Eric R. Young, 2012. "A Quantitative Theory of Information and Unsecured Credit," American Economic Journal: Macroeconomics, American Economic Association, vol. 4(3), pages 153-183, July.
    5. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    6. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    7. 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.
    8. Satyajit Chatterjee & Dean Corbae & Makoto Nakajima & José-Víctor Ríos-Rull, 2007. "A Quantitative Theory of Unsecured Consumer Credit with Risk of Default," Econometrica, Econometric Society, vol. 75(6), pages 1525-1589, November.
    9. 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.
    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. Gajendran Raveendranathan & Georgios Stefanidis, 2020. "The Unprecedented Fall in U.S. Revolving Credit," Department of Economics Working Papers 2020-05, McMaster University.
    2. Kyle F. Herkenhoff & Gajendran Raveendranathan, 2019. "Who Bears the Welfare Costs of Monopoly? The Case of the Credit Card Industry," Working Papers 2019-071, Human Capital and Economic Opportunity Working Group.
    3. Gajendran Raveendranathan, 2018. "Improved Matching, Directed Search, and Bargaining in the Credit Card Market," Department of Economics Working Papers 2018-05, McMaster University.
    4. Satyajit Chatterjee & Dean Corbae & Kyle Dempsey & José‐Víctor Ríos‐Rull, 2023. "A Quantitative Theory of the Credit Score," Econometrica, Econometric Society, vol. 91(5), pages 1803-1840, September.
    5. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    6. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    7. Ionescu, Felicia & Simpson, Nicole, 2016. "Default risk and private student loans: Implications for higher education policies," Journal of Economic Dynamics and Control, Elsevier, vol. 64(C), pages 119-147.
    8. Raveendranathan, Gajendran, 2020. "Revolving credit lines and targeted search," Journal of Economic Dynamics and Control, Elsevier, vol. 118(C).
    9. Juan M. Sanchez, 2009. "The role of information in the rise in consumer bankruptcies," Working Paper 09-04, Federal Reserve Bank of Richmond.
    10. Daphne Chen & Jake Zhao, 2017. "The Impact of Personal Bankruptcy on Labor Supply Decisions," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 26, pages 40-61, October.
    11. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
    12. Andrew Glover & Dean Corbae, 2015. "A Simple Dynamic Theory of Credit Scores Under Adverse Selection," 2015 Meeting Papers 1265, Society for Economic Dynamics.
    13. John Gathergood & Neale Mahoney & Neil Stewart & Jörg Weber, 2019. "How Do Individuals Repay Their Debt? The Balance-Matching Heuristic," American Economic Review, American Economic Association, vol. 109(3), pages 844-875, March.
    14. Florian Exler & Michéle Tertilt, 2020. "Consumer Debt and default: A Macro Perspective," CRC TR 224 Discussion Paper Series crctr224_2020_153v2, University of Bonn and University of Mannheim, Germany.
    15. Blankenship, Brian & Aklin, Michaël & Urpelainen, Johannes & Nandan, Vagisha, 2022. "Jobs for a just transition: Evidence on coal job preferences from India," Energy Policy, Elsevier, vol. 165(C).
    16. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
    17. Patrick Bajari & Chenghuan Sean Chu & Minjung Park, 2008. "An Empirical Model of Subprime Mortgage Default From 2000 to 2007," NBER Working Papers 14625, National Bureau of Economic Research, Inc.
    18. J. Carter Braxton & Gordon Phillips & Kyle Herkenhoff, 2018. "Can the Unemployed Borrow? Implications for Public Insurance," 2018 Meeting Papers 564, Society for Economic Dynamics.
    19. Igor Livshits & James MacGee & Michèle Tertilt, 2010. "Accounting for the Rise in Consumer Bankruptcies," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(2), pages 165-193, April.
    20. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.

    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D18 - Microeconomics - - Household Behavior - - - Consumer Protection
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G0 - Financial Economics - - General
    • G2 - Financial Economics - - Financial Institutions and Services

    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:nbr:nberwo:26165. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.