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Modelling the profitability of credit cards by Markov decision processes

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  • So, Meko M.C.
  • Thomas, Lyn C.

Abstract

This paper derives a Markov decision process model for the profitability of credit cards, which allows lenders to find an optimal dynamic credit limit policy. The states of the system are based on the borrower's behavioural score and the decisions are what credit limit to give the borrower each period. In determining which Markov chain best describes the borrower's performance, second order as well as first order Markov chains are considered and estimation procedures developed that deal with the low default levels that may exist in the data. A case study is given in which the optimal credit limit is derived and the results compared with the actual outcomes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:212:y:2011:i:1:p:123-130
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    References listed on IDEAS

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    Cited by:

    1. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    2. Alfonso-Sánchez, Sherly & Solano, Jesús & Correa-Bahnsen, Alejandro & Sendova, Kristina P. & Bravo, Cristián, 2024. "Optimizing credit limit adjustments under adversarial goals using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 315(2), pages 802-817.
    3. Jonathan K. Budd & Peter G. Taylor, 2015. "Calculating optimal limits for transacting credit card customers," Papers 1506.05376, arXiv.org, revised Aug 2015.
    4. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    5. van der Heijden, Hans & Garn, Wolfgang, 2013. "Profitability in the car industry: New measures for estimating targets and target directions," European Journal of Operational Research, Elsevier, vol. 225(3), pages 420-428.
    6. Özlem Çavuş & Andrzej Ruszczyński, 2014. "Computational Methods for Risk-Averse Undiscounted Transient Markov Models," Operations Research, INFORMS, vol. 62(2), pages 401-417, April.
    7. Dimitrov, Nedialko B. & Dimitrov, Stanko & Chukova, Stefanka, 2014. "Robust decomposable Markov decision processes motivated by allocating school budgets," European Journal of Operational Research, Elsevier, vol. 239(1), pages 199-213.
    8. He, Ping & Hua, Zhongsheng & Liu, Zhixin, 2015. "A quantification method for the collection effect on consumer term loans," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 17-26.

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