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A Markov Decision Model for Selecting Optimal Credit Control Policies

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  • Leon H. Liebman

    (University of Pennsylvania)

Abstract

The rapid growth of consumer credit has created a need for improved credit control policies which result in lower total credit costs. This paper investigates one approach for achieving that objective. The credit control problem is formulated as one of developing optimal policies for an infinite horizon Markov decision model. The model utilizes standard financial data; it also requires the measurement of the costs and returns from alternative credit control policies. The Markov model is transformed into an equivalent linear program. A sample problem is solved and the resulting policies analyzed.

Suggested Citation

  • Leon H. Liebman, 1972. "A Markov Decision Model for Selecting Optimal Credit Control Policies," Management Science, INFORMS, vol. 18(10), pages 519-525, June.
    • Handle: RePEc:inm:ormnsc:v:18:y:1972:i:10:p:b519-b525
    DOI: 10.1287/mnsc.18.10.B519
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    Cited by:

    1. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    2. Zhixin Liu & Ping He & Bo Chen, 2019. "A Markov decision model for consumer term-loan collections," Review of Quantitative Finance and Accounting, Springer, vol. 52(4), pages 1043-1064, May.
    3. Naveed Chehrazi & Peter W. Glynn & Thomas A. Weber, 2019. "Dynamic Credit-Collections Optimization," Management Science, INFORMS, vol. 67(6), pages 2737-2769, June.
    4. 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.
    5. Shoghi , Amirhossein, 2019. "Debt Collection Industry: Machine Learning Approach," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 14(4), pages 453-473, October.

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