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Credit Scores: Performance and Equity

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  • Stefania Albanesi
  • Domonkos F. Vamossy

Abstract

Credit scores are critical for allocating consumer debt in the United States, yet little evidence is available on their performance. We benchmark a widely used credit score against a machine learning model of consumer default and find significant misclassification of borrowers, especially those with low scores. Our model improves predictive accuracy for young, low-income, and minority groups due to its superior performance with low quality data, resulting in a gain in standing for these populations. Our findings suggest that improving credit scoring performance could lead to more equitable access to credit.

Suggested Citation

  • Stefania Albanesi & Domonkos F. Vamossy, 2024. "Credit Scores: Performance and Equity," Papers 2409.00296, arXiv.org.
    • Handle: RePEc:arx:papers:2409.00296
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    References listed on IDEAS

    as
    1. Laura Blattner & Scott Nelson, 2021. "How Costly is Noise? Data and Disparities in Consumer Credit," Papers 2105.07554, arXiv.org.
    2. LaVoice, Jessica & Vamossy, Domonkos F., 2024. "Racial disparities in debt collection," Journal of Banking & Finance, Elsevier, vol. 164(C).
    3. 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.
    4. Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
    5. 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.
    6. Blattner, Laura & Nelson, Scott, 2021. "How Costly Is Noise? Data and Disparities in Consumer Credit," Research Papers 3978, Stanford University, Graduate School of Business.
    7. Edelberg, Wendy, 2006. "Risk-based pricing of interest rates for consumer loans," Journal of Monetary Economics, Elsevier, vol. 53(8), pages 2283-2298, November.
    8. Albanesi, Stefania & DeGiorgi, Giacomo & Nosal, Jaromir, 2022. "Credit growth and the financial crisis: A new narrative," Journal of Monetary Economics, Elsevier, vol. 132(C), pages 118-139.
    9. Dean Corbae & Andrew Glover, 2018. "Employer Credit Checks: Poverty Traps versus Matching Efficiency," NBER Working Papers 25005, National Bureau of Economic Research, Inc.
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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
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G5 - Financial Economics - - Household Finance
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

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