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Behavioural models of credit card usage

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  • Robert Till
  • David Hand

Abstract

Behavioural models characterize the way customers behave in their use of a credit product. In this paper, we examine repayment and transaction behaviour with credit cards. In particular, we describe the development of Markov chain models for late repayment, investigate the extent to which there are different classes of behaviour pattern, and explore the extent to which distinct behaviours can be predicted. We also develop overall models for transaction time distributions. Once such models have been built to summarize the data, they can be used to predict likely future behaviour, and can also serve as the basis of predictions of what one might expect when economic circumstances change.

Suggested Citation

  • Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
  • Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1201-1220
    DOI: 10.1080/0266476032000107196
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    References listed on IDEAS

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    1. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
    2. 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.
    3. 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.
    4. J. Hand, David & Gui Li, Hua & M. Adams, Niall, 2001. "Supervised classification with structured class definitions," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 209-225, April.
    5. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    6. A. Wayne Corcoran, 1978. "The Use of Exponentially-Smoothed Transition Matrices to Improve Forecasting of Cash Flows from Accounts Receivable," Management Science, INFORMS, vol. 24(7), pages 732-739, March.
    7. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, January.
    8. Leon H. Liebman, 1972. "A Markov Decision Model for Selecting Optimal Credit Control Policies," Management Science, INFORMS, vol. 18(10), pages 519-525, June.
    9. Mehta, Dileep, 1970. "Optimal Credit Policy Selection: A Dynamic Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 5(4-5), pages 421-444, December.
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    Cited by:

    1. Jonathan K. Budd & Peter G. Taylor, 2015. "Calculating optimal limits for transacting credit card customers," Papers 1506.05376, arXiv.org, revised Aug 2015.
    2. Maha Bakoben & Tony Bellotti & Niall Adams, 2017. "Identification of Credit Risk Based on Cluster Analysis of Account Behaviours," Papers 1706.07466, arXiv.org.
    3. Lukasz A. Drozd & Ricardo Serrano-Padial, 2017. "Modeling the Revolving Revolution: The Debt Collection Channel," American Economic Review, American Economic Association, vol. 107(3), pages 897-930, March.
    4. Thomas, Lyn C., 2009. "Modelling the credit risk for portfolios of consumer loans: Analogies with corporate loan models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2525-2534.

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