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Count Data Models For A Credit Scoring System

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  • Montserrat Guillen

    (Universitat de Barcelona)

  • Manuel Artis

    (Universitat de Barcelona, Spain)

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

Paper provided by EconWPA in its series Risk and Insurance with number 9407004.

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Date of creation: 14 Jul 1994
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Handle: RePEc:wpa:wuwpri:9407004

Note: Postscript (ASCII) RMI GUILLEN.ABS
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Web page: http://128.118.178.162

Related research

Keywords: count data; NBD models; credit scoring.;

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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.
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  5. 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.
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Cited by:
  1. 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.
  2. 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.
  3. Ulrich Kaiser & Andrea Szczesny, 2000. "Einfache oekonomische Verfahren fuer die Kreditrisikomessung," CoFE Discussion Paper 00-28, Center of Finance and Econometrics, University of Konstanz.
  4. 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.
  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. Roszbach, Kasper, 2003. "Bank Lending Policy, Credit Scoring and the Survival of Loans," Working Paper Series 154, Sveriges Riksbank (Central Bank of Sweden).
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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".
  12. Murray Smith, 2003. "On dependency in double-hurdle models," Statistical Papers, Springer, vol. 44(4), pages 581-595, October.

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