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Using a naive Bayesian classifier methodology for loan risk assessment: Evidence from a Tunisian commercial bank

Author

Listed:
  • Krichene, Aida

    (Department of Accounting, IHEC Carthage, Tunis, Tunisia)

Abstract

Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank.

Suggested Citation

  • Krichene, Aida, 2017. "Using a naive Bayesian classifier methodology for loan risk assessment: Evidence from a Tunisian commercial bank," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 22(42), pages 3-24.
    • Handle: RePEc:ris:joefas:0105
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    More about this item

    Keywords

    ROC curve; Risk assessment; Default risk; Banking sector; Bayesian classifier algorithm;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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