IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

Consumer credit-risk models via machine-learning algorithms

  • Khandani, Amir E.
  • Kim, Adlar J.
  • Lo, Andrew W.

We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank's customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R2's of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggest that aggregated consumer credit-risk analytics may have important applications in forecasting systemic risk.

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: http://www.sciencedirect.com/science/article/B6VCY-508PPS2-1/2/1441298050f00e5423c5f35792f442a1
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Elsevier in its journal Journal of Banking & Finance.

Volume (Year): 34 (2010)
Issue (Month): 11 (November)
Pages: 2767-2787

as
in new window

Handle: RePEc:eee:jbfina:v:34:y:2010:i:11:p:2767-2787
Contact details of provider: Web page: http://www.elsevier.com/locate/jbf

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.:

as in new window
  1. Büyükkarabacak, Berrak & Valev, Neven T., 2010. "The role of household and business credit in banking crises," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1247-1256, June.
  2. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541.
  3. Robert B. Avery & Paul S. Calem & Glenn B. Canner, 2004. "Consumer credit scoring: do situational circumstances matter?," BIS Working Papers 146, Bank for International Settlements.
  4. Drehmann, Mathias & Sorensen, Steffen & Stringa, Marco, 2010. "The integrated impact of credit and interest rate risk on banks: A dynamic framework and stress testing application," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 713-729, April.
  5. Jonathan Zinman, 2005. "Debit or credit?," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
  6. Dean P. Foster & Robert A. Stine, 2001. "Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy," Center for Financial Institutions Working Papers 01-05, Wharton School Center for Financial Institutions, University of Pennsylvania.
  7. Galindo, J & Tamayo, P, 2000. "Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications," Computational Economics, Society for Computational Economics, vol. 15(1-2), pages 107-43, April.
  8. Han, Liang & Fraser, Stuart & Storey, David J., 2009. "Are good or bad borrowers discouraged from applying for loans? Evidence from US small business credit markets," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 415-424, February.
  9. Carbó Valverde Santiago & Massoud Nadia & Rodríguez-Fernández Francisco & Saunders Anthony & Scholnick Barry, 2007. "The Economics of Credit Cards, Debit Cards and ATMs: A Survey and Some New Evidence," Working Papers 201074, Fundacion BBVA / BBVA Foundation.
  10. Robert B. Avery & Paul S. Calem & Glenn B. Canner, 2003. "An overview of consumer data and credit reporting," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Feb, pages 47-73.
  11. Dwyer, Douglas W. & Stein, Roger M., 2006. "Inferring the default rate in a population by comparing two incomplete default databases," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 797-810, March.
  12. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
  13. Avery, Robert B. & Calem, Paul S. & Canner, Glenn B., 2004. "Consumer credit scoring: Do situational circumstances matter?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 835-856, April.
  14. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130, March.
  15. John Simon & Kylie Smith & Tim West, 2009. "Price Incentives and Consumer Payment Behaviour," RBA Research Discussion Papers rdp2009-04, Reserve Bank of Australia.
  16. Sumit Agarwal & Souphala Chomsisengphet & Chunlin Liu & Nicholas S. Souleles, 2010. "Benefits of relationship banking: evidence from consumer credit markets," Working Paper Series WP-2010-05, Federal Reserve Bank of Chicago.
  17. Pérignon, Christophe & Smith, Daniel R., 2010. "The level and quality of Value-at-Risk disclosure by commercial banks," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 362-377, February.
  18. Boot, Arnoud W. A., 2000. "Relationship Banking: What Do We Know?," Journal of Financial Intermediation, Elsevier, vol. 9(1), pages 7-25, January.
  19. Stein, Roger M., 2005. "The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing," Journal of Banking & Finance, Elsevier, vol. 29(5), pages 1213-1236, May.
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:eee:jbfina:v:34:y:2010:i:11:p:2767-2787. 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: (Zhang, Lei)

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.