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Prévision du risque de crédit : Une étude comparative entre l'Analyse Discriminante et l'Approche Neuronale

Author

Listed:
  • Younes Boujelbène

    (UREA - FSEG Sfax)

  • Sihem Khemakhem

    (UREA - FSEG Sfax)

Abstract

Les organismes bancaires s'intéressent à évaluer le risque de la détresse financière avant l'octroi d'un crédit. Plusieurs chercheurs ont proposé l'emploi de modèles basés sur les réseaux de neurones en vue d'améliorer la prise de décision du banquier. L'objectif de cette recherche est d'explorer une nouvelle démarche pratique basée sur les réseaux de neurones en vue d'améliorer la capacité du banquier à prévoir le risque de non remboursement des entreprises demandant un crédit. Cette recherche est motivée par les insuffisances des modèles de prévision traditionnels. L'échantillon est composé de 86 entreprises tunisiennes et une batterie de 15 ratios financiers a été calculée sur la période 2005-2007. Les prévisions issues de la technique des réseaux de neurones sont comparées à celle de l'analyse discriminante. Les résultats de l'étude montrent que la technique "neuronale" est meilleure en termes de prévisibilité.

Suggested Citation

  • Younes Boujelbène & Sihem Khemakhem, 2013. "Prévision du risque de crédit : Une étude comparative entre l'Analyse Discriminante et l'Approche Neuronale," Working Papers hal-00905199, HAL.
    • Handle: RePEc:hal:wpaper:hal-00905199
    Note: View the original document on HAL open archive server: https://hal.science/hal-00905199
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    References listed on IDEAS

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    1. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
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