IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Development and Validation of Credit-Scoring Models

Listed author(s):
  • Glennon, Dennis

    (US Department of the Treasury)

  • Kiefer, Nicholas M.

    (Cornell U and US Department of the Treasury)

  • Larson, C. Erik
  • Choi, Hwan-sik

    (Cornell U and Fannie Mae)

Accurate credit-granting decisions are crucial to the efficiency of the decentralized capital allocation mechanisms in modern market economies. Credit bureaus and many .nancial institutions have developed and used credit-scoring models to standardize and automate, to the extent possible, credit decisions. We build credit scoring models for bankcard markets using the Office of the Comptroller of the Currency, Risk Analysis Division (OCC/RAD) consumer credit database (CCDB). This unusu- ally rich data set allows us to evaluate a number of methods in common practice. We introduce, estimate, and validate our models, using both out-of-sample contempora- neous and future validation data sets. Model performance is compared using both separation and accuracy measures. A vendor-developed generic bureau-based score is also included in the model performance comparisons. Our results indicate that current industry practices, when carefully applied, can produce models that robustly rank-order potential borrowers both at the time of development and through the near future. However, these same methodologies are likely to fail when the the objective is to accurately estimate future rates of delinquency or probabilities of default for individual or groups of borrowers.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Paper provided by Cornell University, Center for Analytic Economics in its series Working Papers with number 07-12.

in new window

Date of creation: Jul 2007
Handle: RePEc:ecl:corcae:07-12
Contact details of provider: Postal:
402 Uris Hall, Ithaca, NY 14853

Phone: (607) 255-9901
Fax: (607) 255-2818
Web page:

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

in new window

  1. Kiefer, Nicholas M. & Larson, C. Erik, 2006. "Specification and Informational Issues in Credit Scoring," Working Papers 06-11, Cornell University, Center for Analytic Economics.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:ecl:corcae:07-12. 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: ()

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.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.