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.
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- 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.
- Jonathan Zinman, 2005.
"Debit or credit?,"
Conference Series ; [Proceedings],
Federal Reserve Bank of Boston.
- Boot, Arnoud W. A., 2000. "Relationship Banking: What Do We Know?," Journal of Financial Intermediation, Elsevier, vol. 9(1), pages 7-25, January.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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