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Predicting Consumer Default: A Deep Learning Approach

Author

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  • 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," Papers 1908.11498, arXiv.org, revised Oct 2019.
    • Handle: RePEc:arx:papers:1908.11498
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    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. 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.
    3. 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.
    4. 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.
    5. Dean Corbae & Andrew Glover, 2018. "Employer Credit Checks: Poverty Traps versus Matching Efficiency," NBER Working Papers 25005, National Bureau of Economic Research, Inc.
    6. 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.
    7. 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.
    8. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    9. 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.
    Full references (including those not matched with items on IDEAS)

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    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

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