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

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References

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  1. 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.
  2. Crocker, Keith J & Snow, Arthur, 1986. "The Efficiency Effects of Categorical Discrimination in the Insurance Industry," Journal of Political Economy, University of Chicago Press, vol. 94(2), pages 321-44, April.
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  4. Harris Milton & Townsend, Robert M, 1981. "Resource Allocation under Asymmetric Information," Econometrica, Econometric Society, vol. 49(1), pages 33-64, January.
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  6. 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.
  7. Boyd, J.h. & Smith, B.D., 1991. "The Equilibrium Allocation of Investment Capital in the Presence of Adverse Selection and Costly State Verification," RCER Working Papers 289, University of Rochester - Center for Economic Research (RCER).
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  11. 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.
  12. Dionne, Georges & Gagne, Robert & Gagnon, Francois & Vanasse, Charles, 1997. "Debt, moral hazard and airline safety An empirical evidence," Journal of Econometrics, Elsevier, vol. 79(2), pages 379-402, August.
  13. Jaffee, Dwight M & Russell, Thomas, 1976. "Imperfect Information, Uncertainty, and Credit Rationing," The Quarterly Journal of Economics, MIT Press, vol. 90(4), pages 651-66, November.
  14. Fenn, Paul T, 1981. "Sickness Duration, Residual Disability, and Income Replacement: An Empirical Analysis," Economic Journal, Royal Economic Society, vol. 91(361), pages 158-73, March.
  15. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
  16. Fourgeaud Claude & Gourieroux Christian & Pradel Jacqueline, 1990. "Sélection de clientèle et tarification de prêt bancaire," CEPREMAP Working Papers (Couverture Orange) 9004, CEPREMAP.
  17. Gourieroux Christian & Monfort Alain & Trognon A, 1982. "Pseudo maximum lilelihood methods : applications to poisson models," CEPREMAP Working Papers (Couverture Orange) 8203, CEPREMAP.
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Citations

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Cited by:
  1. 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.
  2. 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.
  3. Carling, Kenneth & Jacobson, Tor & Roszbach, Kasper, 2001. "Dormancy risk and expected profits of consumer loans," Journal of Banking & Finance, Elsevier, vol. 25(4), pages 717-739, April.
  4. Yaldız Hanedar, Elmas & Broccardo, Eleonora & Bazzana, Flavio, 2014. "Collateral requirements of SMEs: The evidence from less-developed countries," Journal of Banking & Finance, Elsevier, vol. 38(C), pages 106-121.
  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. Georges Dionne & Florence Giuliano & Pierre Picard, 2009. "Optimal Auditing with Scoring: Theory and Application to Insurance Fraud," Management Science, INFORMS, vol. 55(1), pages 58-70, January.
  7. Georges Dionne & Florence Giuliano & Pierre Picard, 2003. "Optimal Auditing for Insurance Fraud," Cahiers de recherche 0329, CIRPEE.
  8. 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.
  9. Murray Smith, 2003. "On dependency in double-hurdle models," Statistical Papers, Springer, vol. 44(4), pages 581-595, October.
  10. Woo, Mi-Ja & Sriram, T.N., 2007. "Robust estimation of mixture complexity for count data," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4379-4392, May.
  11. Artis, Manuel & Ayuso, Mercedes & Guillen, Montserrat, 1999. "Modelling different types of automobile insurance fraud behaviour in the Spanish market," Insurance: Mathematics and Economics, Elsevier, vol. 24(1-2), pages 67-81, March.
  12. Elmas Yaldiz Hanedar & Eleonora Broccardo & Flavio Bazzana, 2012. "Collateral Requirements of SMEs:The Evidence from Less–Developed Countries," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 12111, Universita di Modena e Reggio Emilia, Facoltà di Economia "Marco Biagi".

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