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A comparison of strategies to develop a customer default scoring model

Author

Listed:
  • Gustavo Henrique Araujo Pereira

    (Federal University of São Carlos)

  • Rinaldo Artes

    (Insper Institute of Education and Research)

Abstract

Behavioural scoring models are generally used to estimate the probability that a customer of a financial institution who owns a credit product will default on this product in a fixed time horizon. However, one single customer usually purchases many credit products from an institution while behavioural scoring models generally treat each of these products independently. In order to make credit risk management easier and more efficient, it is interesting to develop customer default scoring models. These models estimate the probability that a customer of a certain financial institution will have credit issues with at least one product in a fixed time horizon. In this study, three strategies to develop customer default scoring models are described. One of the strategies is regularly utilized by financial institutions and the other two will be proposed herein. The performance of these strategies is compared by means of an actual data bank supplied by a financial institution and a Monte Carlo simulation study.

Suggested Citation

  • Gustavo Henrique Araujo Pereira & Rinaldo Artes, 2016. "A comparison of strategies to develop a customer default scoring model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(11), pages 1341-1352, November.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:11:d:10.1057_jors.2016.23
    DOI: 10.1057/jors.2016.23
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    References listed on IDEAS

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    1. Ruey-Ching Hwang, 2013. "Predicting issuer credit ratings using generalized estimating equations," Quantitative Finance, Taylor & Francis Journals, vol. 13(3), pages 383-398, February.
    2. Thomas, L.C. & Ho, J. & Scherer, W.T., 2001. "Time will tell: Behavioural Scoring and the Dynamics of Consumer Credit Assessment," Papers 01-174, University of Southampton - Department of Accounting and Management Science.
    3. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
    4. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
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    Cited by:

    1. Silva, Diego M.B. & Pereira, Gustavo H.A. & Magalhães, Tiago M., 2022. "A class of categorization methods for credit scoring models," European Journal of Operational Research, Elsevier, vol. 296(1), pages 323-331.

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