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Variable reduction, sample selection bias and bank retail credit scoring

  • Marshall, Andrew
  • Tang, Leilei
  • Milne, Alistair

This paper investigates the effect of including the customer loan approval process to the estimation of loan performance and explores the influence of sample selection bias in predicting the probability of default. The bootstrap variable reduction technique is applied to reduce the variable dimension for a large data-set drawn from a major UK retail bank. The results show a statistically significant correlation between the loan approval and performance processes. We further demonstrate an economically significant improvement in forecasting performance when taking into account sample selection bias. We conclude that financial institutions can obtain benefits by correcting for sample selection bias in their credit scoring models.

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Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 17 (2010)
Issue (Month): 3 (June)
Pages: 501-512

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Handle: RePEc:eee:empfin:v:17:y:2010:i:3:p:501-512
Contact details of provider: Web page: http://www.elsevier.com/locate/jempfin

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  1. Jacobson, Tor & Roszbach, Kasper, 1998. "Bank Lending Policy, Credit Scoring and Value at Risk," Working Paper Series 68, Sveriges Riksbank (Central Bank of Sweden).
  2. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, vol. 40(1), pages 3-14, January.
  3. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
  4. Machauer, Achim & Weber, Martin, 1998. "Bank behavior based on internal credit ratings of borrowers," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1355-1383, October.
  5. Kanatas, George, 1987. "Commercial paper, bank reserve requirements, and the informational role of loan commitments," Journal of Banking & Finance, Elsevier, vol. 11(3), pages 425-448, September.
  6. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
  7. Kasper Roszbach, 2004. "Bank Lending Policy, Credit Scoring, and the Survival of Loans," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 946-958, November.
  8. Santos Silva, J.M.C. & Murteira, J.M.R., 2009. "Estimation of default probabilities using incomplete contracts data," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 457-465, June.
  9. Dinh, Thi Huyen Thanh & Kleimeier, Stefanie, 2007. "A credit scoring model for Vietnam's retail banking market," International Review of Financial Analysis, Elsevier, vol. 16(5), pages 471-495.
  10. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
  11. Steven A. Sharpe, 1989. "Asymmetric information, bank lending, and implicit contracts: a stylized model of customer relationships," Finance and Economics Discussion Series 70, Board of Governors of the Federal Reserve System (U.S.).
  12. Montserrat Guillen & Manuel Artis, 1994. "Count Data Models For A Credit Scoring System," Risk and Insurance 9407004, EconWPA.
  13. Greene, William, 1998. "Sample selection in credit-scoring models1," Japan and the World Economy, Elsevier, vol. 10(3), pages 299-316, July.
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