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Predictive models of expenditure and over-indebtedness for assessing the affordability of new consumer credit applications

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  • S M Finlay

    (Lancaster University)

Abstract

Lenders are under increasing pressure to consider measures of affordability and indebtedness as well as risk, when assessing consumer credit applications. In order to evaluate the affordability of a new credit product, a lender needs information about the applicant's income and outgoings. However, while most lenders obtain information about income and credit commitments many do not have much, if any, pertaining to other expenditure. Therefore, they are not well positioned to determine an individual's ability to fund new borrowing. This paper demonstrates that using only data captured on a typical application form, combined with data from a credit bureau, it is possible to develop good predictive models of expenditure and over-indebtedness that can be used in conjunction with measures of risk to reject applications from individuals who are likely to already be over-indebted, or to restrict the volume of credit advanced to that which the applicant can afford.

Suggested Citation

  • S M Finlay, 2006. "Predictive models of expenditure and over-indebtedness for assessing the affordability of new consumer credit applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(6), pages 655-669, June.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:6:d:10.1057_palgrave.jors.2602030
    DOI: 10.1057/palgrave.jors.2602030
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    References listed on IDEAS

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    1. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    2. David Hand & Niall Adams, 2000. "Defining attributes for scorecard construction in credit scoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 527-540.
    3. Thomas, Lyn C. & Edelman, David B. & Crook, Jonathan, 2004. "Readings in Credit Scoring: Foundations, Developments, and Aims," OUP Catalogue, Oxford University Press, number 9780198527978.
    4. Shelley A. Phipps & Peter S. Burton, 1998. "What’s Mine is Yours? The Influence of Male and Female Incomes on Patterns of Household Expenditure," Economica, London School of Economics and Political Science, vol. 65(260), pages 599-613, November.
    5. Paul Blacklow & Ranjan Ray, 2003. "Intra‐Household Resource Allocation, Consumer Preferences and Commodity Tax Reforms: Australian Evidence," The Economic Record, The Economic Society of Australia, vol. 79(247), pages 425-433, December.
    6. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    7. L C Thomas & J Banasik & J N Crook, 2001. "Recalibrating scorecards," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 981-988, September.
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    Cited by:

    1. Cesar Leandro, Julio & Botelho, Delane, 2022. "Consumer over-indebtedness: A review and future research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 535-551.
    2. H J Jeon & S Y Sohn, 2008. "The risk management for technology credit guarantee fund," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(12), pages 1624-1632, December.
    3. Christopher T. Whelan & Brian Nolan & Bertrand Maitre, 2017. "Polarization or “Squeezed Middle” in the Great Recession?: A Comparative European Analysis of the Distribution of Economic Stress," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 163-184, August.
    4. Russell, Helen & Maître, Bertrand & Whelan, Christopher T., 2011. "Economic Vulnerability and Severity of Debt Problems: An Analysis of the Irish EU-SILC 2008," Papers WP402, Economic and Social Research Institute (ESRI).
    5. Anne-Sophie Bergerès & Philippe d'Astous & Georges Dionne, 2011. "Is there Any Dependence Between Consumer Credit Line Utilization and Default Probability on a Term Loan? Evidence from Bank-Level Data," Cahiers de recherche 1119, CIRPEE.
    6. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    7. Bergerès, Anne-Sophie & d'Astous, Philippe & Dionne, Georges, 2015. "Is there any dependence between consumer credit line utilization and default probability on a term loan? Evidence from bank-customer data," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 276-286.
    8. Christopher T. Whelan & Brian Nolan & Bertrand Maître, 2018. "Economic Stress and the Great Recession in Ireland: The Erosion of Social Class Advantage," The Economic and Social Review, Economic and Social Studies, vol. 49(3), pages 259-286.
    9. Christopher T. Whelan & Brian Nolan & Bertrand Maítre, 2016. "The Great Recession and the Changing Distribution of Economic Stress across Income Classes and the Life Course in Ireland: A Comparative Perspective," Working Papers 201603, Geary Institute, University College Dublin.
    10. Jaime Ruiz-Tagle & Leidy García & Álvaro Miranda, 2013. "Proceso de Endeudamiento y Sobre Endeudamiento de los Hogares en Chile," Working Papers Central Bank of Chile 703, Central Bank of Chile.

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