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Credit Scores: Performance and Equity

Author

Listed:
  • 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. Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
    3. LaVoice, Jessica & Vamossy, Domonkos F., 2024. "Racial disparities in debt collection," Journal of Banking & Finance, Elsevier, vol. 164(C).
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    7. Dean Corbae & Andrew Glover, 2018. "Employer Credit Checks: Poverty Traps versus Matching Efficiency," NBER Working Papers 25005, National Bureau of Economic Research, Inc.
    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. Robert B. Avery & Paul S. Calem & Glenn B. Canner, 2003. "An overview of consumer data and credit reporting," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), vol. 89(Feb), pages 47-73, February.
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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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