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Time will tell: Behavioural Scoring and the Dynamics of Consumer Credit Assessment

Author

Listed:
  • Thomas, L.C.
  • Ho, J.
  • Scherer, W.T.

Abstract

This paper discusses the use of dynamic modelling in consumer credit risk assessment. It surveys the approaches and objectives of behavioural scoring, customer scoring and profit scoring. It then investigates how Markov chain stochastic processes can be used to model the dynamics of the delinquency status and behavioural scores of consumers.

Suggested Citation

  • 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.
  • Handle: RePEc:fth:sotoam:01-174
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    Citations

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    Cited by:

    1. repec:pal:jorsoc:v:56:y:2005:i:8:d:10.1057_palgrave.jors.2601920 is not listed on IDEAS
    2. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.
    3. repec:pal:jorsoc:v:61:y:2010:i:3:d:10.1057_jors.2009.99 is not listed on IDEAS
    4. G. Verstraeten & D. Van Den Poel, 2004. "The Impact of Sample Bias on Consumer Credit Scoring Performance and Profitability," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/232, Ghent University, Faculty of Economics and Business Administration.
    5. So, Meko M.C. & Thomas, Lyn C., 2011. "Modelling the profitability of credit cards by Markov decision processes," European Journal of Operational Research, Elsevier, vol. 212(1), pages 123-130, July.
    6. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
    7. Malik, Madhur & Thomas, Lyn C., 2012. "Transition matrix models of consumer credit ratings," International Journal of Forecasting, Elsevier, vol. 28(1), pages 261-272.
    8. Bellotti, Tony & Crook, Jonathan, 2013. "Forecasting and stress testing credit card default using dynamic models," International Journal of Forecasting, Elsevier, vol. 29(4), pages 563-574.
    9. Régis, Daniel E. & Artes, Rinaldo, 2008. "Modelo Multi-Estado de Markov em Cartões de Crédito," Insper Working Papers wpe_129, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    10. 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.
    11. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    12. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.

    More about this item

    Keywords

    RISK ; CONSUMERS ; CREDIT;

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory

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