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The Consumer Loan’s Payment Default Predictive Model: An Application in a Tunisian Commercial Bank

Author

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  • Lobna Abid
  • Afif Masmoudi
  • Sonia Zouari-Ghorbel

Abstract

For the alarming growth in consumer credit in recent years, consumer credit scoring is the term used to describe methods of classifying credits’ applicants as `good' and `bad' risk classes.. In the current paper, we use the logistic regression as well as the discriminant analysis in order to develop predictive models allowing to distinguish between “good” and “bad” borrowers. The data have been collected from a commercial Tunisian bank over a 3-year period, from 2010 to 2012. These data consist of four selected and ordered variables. By comparing the respective performances of the Logistic Regression (LR) and the Discriminant Analysis (DA), we notice that the LR model yields a 89% good classification rate in predicting customer types and then, a significantly low error rate (11%), as compared with the DA approach (where the good classification rate is only equal to 68.49%, leading to a significantly high error rate, i.e. 31.51%).

Suggested Citation

  • Lobna Abid & Afif Masmoudi & Sonia Zouari-Ghorbel, 2016. "The Consumer Loan’s Payment Default Predictive Model: An Application in a Tunisian Commercial Bank," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(1), pages 27-42.
    • Handle: RePEc:asi:aeafrj:v:6:y:2016:i:1:p:27-42:id:1445
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    Cited by:

    1. Laura Cristina Lanzarini & Augusto Villa Monte & Aurelio F. Bariviera & Patricia Jimbo Santana, 2017. "Simplifying credit scoring rules using LVQ+PSO," Papers 1704.04450, arXiv.org.

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