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Variable reduction, sample selection bias and bank retail credit scoring

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Author Info

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

Abstract

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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Bibliographic Info

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

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Web page: http://www.elsevier.com/locate/jempfin

Related research

Keywords: Bootstrap variable selection Credit scoring Loan performance forecasting Sample selection bias;

References

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  1. Sharpe, Steven A, 1990. " Asymmetric Information, Bank Lending, and Implicit Contracts: A Stylized Model of Customer Relationships," Journal of Finance, American Finance Association, vol. 45(4), pages 1069-87, September.
  2. Roszbach, Kasper, 2003. "Bank Lending Policy, Credit Scoring and the Survival of Loans," Working Paper Series 154, Sveriges Riksbank (Central Bank of Sweden).
  3. Jacobson, Tor & Roszbach, Kasper, 1998. "Bank Lending Policy, Credit Scoring and Value at Risk," Working Paper Series in Economics and Finance 260, Stockholm School of Economics.
  4. 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.
  5. 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.
  6. Montserrat Guillen & Manuel Artis, 1994. "Count Data Models For A Credit Scoring System," Risk and Insurance 9407004, EconWPA.
  7. 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.
  8. 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.
  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. 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.
  11. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
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Cited by:
  1. Bücker, Michael & van Kampen, Maarten & Krämer, Walter, 2013. "Reject inference in consumer credit scoring with nonignorable missing data," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1040-1045.

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