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Count data models for a credit scoring system

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Author Info

  • Dionne, Georges
  • Artis, Manuel
  • Guillen, Montserrat

Abstract

Credit scoring systems created for the evaluation of new applications are based on the available statistical information which is related to the behaviour of former clients with credit. Usually, financial institutions apply discriminant analysis techniques to create these systems but they lack of good properties due, for example, to the presence of non-normal variables. As an alternative, the future repayment behaviour is predicted by means of the expected number of unpaid instalments. The use of this latter variable suggests that appropriate models might be of interest, in which some covariant exogenous variables are included in order to specify the expected level of debt. At this point, prepayment is not explicitly considered. These models should be used as explanatory tools when evaluating the level of risk involved in personal credit transactions. Negative Binomial Distribution models are suitable when heterogeneity is taken into account. Some results related to prediction performance are shown for different model specifications in the case of data from a Spanish bank.

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Bibliographic Info

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

Volume (Year): 3 (1996)
Issue (Month): 3 (September)
Pages: 303-325
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Handle: RePEc:eee:empfin:v:3:y:1996:i:3:p:303-325

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For corrections or technical questions regarding this item, or to correct its listing, contact: (Jeroen Loos).

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References

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  1. Gourieroux Christian & Monfort Alain & Trognon A, 1981. "Pseudo maximum likelihood methods : theory," CEPREMAP Working Papers (Couverture Orange) 8129, CEPREMAP.
  2. Jerry A. Hausman & Bronwyn H. Hall & Zvi Griliches, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," NBER Technical Working Papers 0017, National Bureau of Economic Research, Inc.
  3. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-20, May.
  4. 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.
  5. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
  6. Steenackers, A. & Goovaerts, M. J., 1989. "A credit scoring model for personal loans," Insurance: Mathematics and Economics, Elsevier, vol. 8(1), pages 31-34, March.
  7. Grogger, J T & Carson, Richard T, 1991. "Models for Truncated Counts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(3), pages 225-38, July-Sept.
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Citations

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Cited by:
  1. Georges Dionne & Florence Giuliano & Pierre Picard, 2005. "Optimal Auditing with Scoring Theory and Application to Insurance Fraud," Working Papers hal-00243026, HAL.
  2. Olfa N. Ghali, 2001. "An Empirical Evaluation of the Implementation of the Bonus-Malus System in the Tunisian Automobile Insurance Ratemaking," Working Papers 0135, Economic Research Forum, revised Nov 2001.
  3. 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.
  4. Roszbach, Kasper, 1998. "Bank Lending Policy, Credit Scoring and the Survival of Loans," Working Paper Series in Economics and Finance 261, Stockholm School of Economics.
  5. Ulrich Kaiser & Andrea Szczesny, 2000. "Einfache oekonomische Verfahren fuer die Kreditrisikomessung," CoFE Discussion Paper 00-28, Center of Finance and Econometrics, University of Konstanz.
  6. G. Dionne & F. Giuliano & P. Picard, 2002. "Optimal auditing for insurance fraud," THEMA Working Papers 2002-32, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  7. Ulrich Kaiser & Andrea Szczesny, 2000. "Einfache ökonometrische Verfahren für die Kreditrisikomessung: Verweildauermodelle," Working Paper Series: Finance and Accounting 62, Department of Finance, Goethe University Frankfurt am Main.
  8. Murray Smith, 2003. "On dependency in double-hurdle models," Statistical Papers, Springer, vol. 44(4), pages 581-595, October.

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