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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.

Suggested Citation

  • Montserrat Guillen & Manuel Artis, 1994. "Count Data Models For A Credit Scoring System," Risk and Insurance 9407004, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpri:9407004
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    2. 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.
    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. Umashanger, T. & Sriram, T.N., 2009. "L2E estimation of mixture complexity for count data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4243-4254, October.
    5. 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) 0034, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    6. P G Moffatt, 2005. "Hurdle models of loan default," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1063-1071, September.
    7. 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 11 2001.
    8. 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.
    9. 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.
    10. 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.
    11. Murray Smith, 2003. "On dependency in double-hurdle models," Statistical Papers, Springer, vol. 44(4), pages 581-595, October.
    12. 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.
    13. 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.
    14. R. Winkelmann, 1998. "Count data models with selectivity," Econometric Reviews, Taylor & Francis Journals, vol. 17(4), pages 339-359.
    15. Kaiser, Ulrich & Szczesny, Andrea, 2000. "Einfache ökonometrische Verfahren für die Kreditrisikomessung," CoFE Discussion Papers 00/28, University of Konstanz, Center of Finance and Econometrics (CoFE).
    16. Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2021. "Bayes estimates of multimodal density features using DNA and Economic Data," Tinbergen Institute Discussion Papers 21-017/III, Tinbergen Institute.
    17. Sami Mestiri & Abdeljelil Farhat, 2021. "Using Non-parametric Count Model for Credit Scoring," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 39-49, March.
    18. Drivas, Kyriakos & Economidou, Claire & Tsionas, Efthymios G., 2014. "A Poisson Stochastic Frontier Model with Finite Mixture Structure," MPRA Paper 57485, University Library of Munich, Germany.
    19. Michael J. Peel, 2014. "Addressing unobserved endogeneity bias in accounting studies: control and sensitivity methods by variable type," Accounting and Business Research, Taylor & Francis Journals, vol. 44(5), pages 545-571, October.
    20. 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.

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