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Reject inference in consumer credit scoring with nonignorable missing data

  • Bücker, Michael
  • van Kampen, Maarten
  • Krämer, Walter

We generalize an empirical likelihood approach to deal with missing data to a model of consumer credit scoring. An application to recent consumer credit data shows that our procedure yields parameter estimates which are significantly different (both statistically and economically) from the case where customers who were refused credit are ignored. This has obvious implications for commercial banks as it shows that refused customers should not be ignored when developing scorecards for the retail business. We also show that forecasts of defaults derived from the method proposed in this paper improve upon the standard ones when refused customers do not enter the estimation data set.

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

Volume (Year): 37 (2013)
Issue (Month): 3 ()
Pages: 1040-1045

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Handle: RePEc:eee:jbfina:v:37:y:2013:i:3:p:1040-1045
Contact details of provider: Web page: http://www.elsevier.com/locate/jbf

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  1. 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.
  2. Walter Krämer, 2011. "The Cult of Statistical Significance – What Economists Should and Should Not Do to Make their Data Talk," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 131(3), pages 455-468.
  3. Marshall, Andrew & Tang, Leilei & Milne, Alistair, 2010. "Variable reduction, sample selection bias and bank retail credit scoring," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 501-512, June.
  4. 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.
  5. Jacobson, Tor & Roszbach, Kasper, 1998. "Bank Lending Policy, Credit Scoring and Value at Risk," SSE/EFI Working Paper Series in Economics and Finance 260, Stockholm School of Economics.
  6. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
  7. Qin J. & Leung D. & Shao J., 2002. "Estimation With Survey Data Under Nonignorable Nonresponse or Informative Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 193-200, March.
  8. Bücker, Michael & Krämer, Walter & Arnold, Matthias, 2012. "A Hausman test for non-ignorability," Economics Letters, Elsevier, vol. 114(1), pages 23-25.
  9. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
  10. Crook, Jonathan & Banasik, John, 2004. "Does reject inference really improve the performance of application scoring models?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 857-874, April.
  11. Feess, Eberhard & Walzl, Markus, 2006. "Why it Pays to Conceal - On the Optimal Timing of Acquiring Verifiable Information," Research Memorandum 020, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
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