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Count Data Models For A Credit Scoring System

  • Montserrat Guillen

    (Universitat de Barcelona)

  • Manuel Artis

    (Universitat de Barcelona, Spain)

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

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Date of creation: 14 Jul 1994
Date of revision:
Handle: RePEc:wpa:wuwpri:9407004
Note: Postscript (ASCII) RMI GUILLEN.ABS
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