IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v63y2012i3p306-321.html
   My bibliography  Save this article

A time-dependent proportional hazards survival model for credit risk analysis

Author

Listed:
  • J-K Im

    (Northwestern University, Evanston, IL, USA)

  • D W Apley

    (Northwestern University, Evanston, IL, USA)

  • C Qi

    (Consultant, Lake Forest, IL, USA)

  • X Shan

    (Consultant, Deerfield, IL, USA)

Abstract

In the consumer credit industry, assessment of default risk is critically important for the financial health of both the lender and the borrower. Methods for predicting risk for an applicant using credit bureau and application data, typically based on logistic regression or survival analysis, are universally employed by credit card companies. Because of the manner in which the predictive models are fit using large historical sets of existing customer data that extend over many years, default trends, anomalies, and other temporal phenomena that result from dynamic economic conditions are not brought to light. We introduce a modification of the proportional hazards survival model that includes a time-dependency mechanism for capturing temporal phenomena, and we develop a maximum likelihood algorithm for fitting the model. Using a very large, real data set, we demonstrate that incorporating the time dependency can provide more accurate risk scoring, as well as important insight into dynamic market effects that can inform and enhance related decision making.

Suggested Citation

  • J-K Im & D W Apley & C Qi & X Shan, 2012. "A time-dependent proportional hazards survival model for credit risk analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(3), pages 306-321, March.
  • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:3:p:306-321
    as

    Download full text from publisher

    File URL: http://www.palgrave-journals.com/jors/journal/v63/n3/pdf/jors201134a.pdf
    File Function: Link to full text PDF
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: http://www.palgrave-journals.com/jors/journal/v63/n3/full/jors201134a.html
    File Function: Link to full text HTML
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Liu, Fan & Hua, Zhongsheng & Lim, Andrew, 2015. "Identifying future defaulters: A hierarchical Bayesian method," European Journal of Operational Research, Elsevier, vol. 241(1), pages 202-211.
    2. Aimée Backiel & Bart Baesens & Gerda Claeskens, 2016. "Predicting time-to-churn of prepaid mobile telephone customers using social network analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1135-1145, September.
    3. Azamat Abdymomunov & Sharon Blei & Bakhodir Ergashev, 2015. "Integrating Stress Scenarios into Risk Quantification Models," Journal of Financial Services Research, Springer;Western Finance Association, vol. 47(1), pages 57-79, February.

    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:pal:jorsoc:v:63:y:2012:i:3:p:306-321. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.palgrave-journals.com/ .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.