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Consumer credit scoring: do situational circumstances matter?

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Author Info

  • Robert B. Avery

    (Board of Governors of the Federal Reserve System)

  • Paul S. Calem

    (Board of Governors of the Federal Reserve System - Division of Research)

  • Glenn B. Canner

    (Statistics)

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    Abstract

    Although credit history scoring offers benefits to lenders and borrowers, failure to consider situational circumstances raises important statistical issues that may affect the ability of scoring systems to accurately quantify an individual's credit risk. Evidence from a national sample of credit reporting agency records suggests that failure to consider measures of local economic circumstances and individual trigger events when developing credit history scores can diminish the potential effectiveness of such models. There are practical difficulties, however, associated with developing scoring models that incorporate situational data, arising largely because of inherent limitations of the credit reporting agency databases used to build scoring models.

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    Bibliographic Info

    Paper provided by Bank for International Settlements in its series BIS Working Papers with number 146.

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    Length: 24 pages
    Date of creation: Jan 2004
    Date of revision:
    Handle: RePEc:bis:biswps:146

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    Related research

    Keywords: Credit scoring; Consumer credit; Credit risk;

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    References

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    1. Robert M. Hunt, 2002. "The development and regulation of consumer credit reporting in America," Working Papers 02-21, Federal Reserve Bank of Philadelphia.
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    Citations

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    Cited by:
    1. de Andrade, Fabio Wendling Muniz & Thomas, Lyn, 2007. "Structural models in consumer credit," European Journal of Operational Research, Elsevier, Elsevier, vol. 183(3), pages 1569-1581, December.
    2. Federico Ferretti, 2007. "Consumer credit information systems: a critical review of the literature. Too little attention paid by Lawyers?," European Journal of Law and Economics, Springer, Springer, vol. 23(1), pages 71-88, February.
    3. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, Elsevier, vol. 34(11), pages 2767-2787, November.
    4. Iulia Iuga & Ruxandra Lazea, 2012. "Study Regarding The Influence Of The Unemployment Rate Over Non-Performing Loans In Romania Using The Correlation Indicator," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(14), pages 18.
    5. Javier Gutiérrez Rueda & Dairo Estrada & Laura Capera, . "Un análisis del endeudamiento de los hogares," Temas de Estabilidad Financiera, Banco de la Republica de Colombia 061, Banco de la Republica de Colombia.
    6. Evžen Kocenda & Martin Vojtek, 2009. "Default Predictors and Credit Scoring Models for Retail Banking," CESifo Working Paper Series, CESifo Group Munich 2862, CESifo Group Munich.
    7. Rodrigo Alfaro & Natalia Gallardo & Roberto Stein, 2010. "The Determinants of Household Debt Defa," Working Papers Central Bank of Chile, Central Bank of Chile 574, Central Bank of Chile.
    8. Leow, Mindy & Crook, Jonathan, 2014. "Intensity models and transition probabilities for credit card loan delinquencies," European Journal of Operational Research, Elsevier, Elsevier, vol. 236(2), pages 685-694.
    9. Souphala Chomsisengphet & Ronel Elul, 2005. "Bankruptcy exemptions, credit history, and the mortgage market," Working Papers 04-14, Federal Reserve Bank of Philadelphia.

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