IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v18y1972i10pb519-b525.html
   My bibliography  Save this article

A Markov Decision Model for Selecting Optimal Credit Control Policies

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.18.10.B519
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.18.10.B519?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. Naveed Chehrazi & Peter W. Glynn & Thomas A. Weber, 2019. "Dynamic Credit-Collections Optimization," Management Science, INFORMS, vol. 67(6), pages 2737-2769, June.
    5. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:18:y:1972:i:10:p:b519-b525. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.