IDEAS home Printed from https://ideas.repec.org/a/cup/jfinqa/v11y1976i02p313-328_02.html
   My bibliography  Save this article

Credit Screening System Selection

Author

Listed:
  • Long, Michael S.

Abstract

Recent financial literature has discussed how a creditor should determine its investigation and extension policy. Mehta [8,9] has developed a sequential process for credit extension, and others [1,2,4,7,10,12,14] have used credit-scoring functions to develop decisions rules. Instead of discussing the use of a particular system or the development of a new system, this paper shifts the focus to selection of the best of alternative systems. Different creditors face different profit-loss ratios on loans, business volume, and prior probabilities of good and bad customers. Furthermore, since the alternative systems have different initial costs, effectiveness, and investigation costs per application, no one system is optimal for all creditors. Finally, any credit-scoring alternative declines in effectiveness over time. Measurement of the overall effectiveness of a system requires that the optimal time between updating the system be known.

Suggested Citation

  • Long, Michael S., 1976. "Credit Screening System Selection," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 11(2), pages 313-328, June.
    • Handle: RePEc:cup:jfinqa:v:11:y:1976:i:02:p:313-328_02
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0022109000020664/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.
    2. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.

    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:cup:jfinqa:v:11:y:1976:i:02:p:313-328_02. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/jfq .

    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.