Consumer credit-risk models via machine-learning algorithms
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.
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.
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.:
- 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.
- 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.
- Scholnick, Barry & Massoud, Nadia & Saunders, Anthony & Carbo-Valverde, Santiago & Rodríguez-Fernández, Francisco, 2008.
"The economics of credit cards, debit cards and ATMs: A survey and some new evidence,"
Journal of Banking & Finance,
Elsevier, vol. 32(8), pages 1468-1483, August.
- 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.
- Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130, March.
- 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.
- 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.
- 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.
- 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.
- John Simon & Kylie Smith & Tim West, 2009.
"Price Incentives and Consumer Payment Behaviour,"
RBA Research Discussion Papers
rdp2009-04, Reserve Bank of Australia.
- 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.
- 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.
- Jonathan Zinman, 2005.
"Debit or credit?,"
Conference Series ; [Proceedings],
Federal Reserve Bank of Boston.
- 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.
- 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.
- 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.
- 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.
- 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.
- Boot, Arnoud W. A., 2000. "Relationship Banking: What Do We Know?," Journal of Financial Intermediation, Elsevier, vol. 9(1), pages 7-25, January.
- 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.
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.