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Lookahead scorecards for new fixed term credit products

Author

Listed:
  • D J Hand

    (Imperial College of Science, Technology and Medicine)

  • M G Kelly

    (Imperial College of Science, Technology and Medicine)

Abstract

Standard approaches to scorecard construction require that a body of data has already been collected for which the customers have known good/bad outcomes, so that scorecards can be built using this information. This requirement is not satisfied by new financial products. To overcome this lack, we describe a class of models based on using information about the length of time customers have been using the product, as well as any available information which does exist about true good/bad outcome classes. These models not only predict the probability that a new customer will go bad at some time during the product's term, but also evolve as new information becomes available. Particular choices of functional form in such models can lead to scorecards with very simple structures. The models are illustrated on a data set relating to loans.

Suggested Citation

  • D J Hand & M G Kelly, 2001. "Lookahead scorecards for new fixed term credit products," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 989-996, September.
    • Handle: RePEc:pal:jorsoc:v:52:y:2001:i:9:d:10.1057_palgrave.jors.2601151
    DOI: 10.1057/palgrave.jors.2601151
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    Citations

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    Cited by:

    1. G Andreeva & J Ansell & J N Crook, 2005. "Modelling the purchase propensity: analysis of a revolving store card," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1041-1050, September.
    2. Andreeva, Galina & Ansell, Jake & Crook, Jonathan, 2007. "Modelling profitability using survival combination scores," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1537-1549, December.
    3. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    4. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    5. G Andreeva, 2006. "European generic scoring models using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1180-1187, October.
    6. Sanchez-Barrios, Luis Javier & Andreeva, Galina & Ansell, Jake, 2016. "“Time-to-profit scorecards for revolving credit”," European Journal of Operational Research, Elsevier, vol. 249(2), pages 397-406.
    7. T H Moon & S Y Sohn, 2011. "Survival analysis for technology credit scoring adjusting total perception," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1159-1168, June.
    8. Liu, Fan & Hua, Zhongsheng & Lim, Andrew, 2015. "Identifying future defaulters: A hierarchical Bayesian method," European Journal of Operational Research, Elsevier, vol. 241(1), pages 202-211.
    9. L C Thomas, 2010. "Consumer finance: challenges for operational research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 41-52, January.
    10. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.
    11. D J Hand, 2005. "Good practice in retail credit scorecard assessment," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1109-1117, September.

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