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LA PREVENTION DU RISQUE DE DEFAUT DANS LES BANQUES TUNISIENNES. Analyse comparative entre les méthodes linéaires classiques et les méthodes de l'intelligence artificielle : les réseaux de neurones artificiels

Author

Listed:
  • Hamadi Matoussi

    (ISCAE - ISCAE)

  • Aida Krichène Abdelmoula

    (UCAR - Université de Carthage (Tunisie))

Abstract

This paper addresses the question of default prediction of short term loans for a Tunisian commercial bank. We make a comparative analysis of three different statistical method of classification (artificial neural network and linear logistic regression with panel data). We use a database of 1435 files of credits granted to industrial Tunisian companies by a commercial bank in 2003, 2004, 2005 and 2006.The results show that the best prediction model is the multilayer neural network model and the best information set is the one combining accrual, cash-flow and collateral variables. We got a good classification rate of 97% in the training data set and 89.8% in the validation data set.

Suggested Citation

  • Hamadi Matoussi & Aida Krichène Abdelmoula, 2010. "LA PREVENTION DU RISQUE DE DEFAUT DANS LES BANQUES TUNISIENNES. Analyse comparative entre les méthodes linéaires classiques et les méthodes de l'intelligence artificielle : les réseaux de neurones art," Post-Print hal-00481087, HAL.
    • Handle: RePEc:hal:journl:hal-00481087
    Note: View the original document on HAL open archive server: https://hal.science/hal-00481087
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