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

Aida Krichene ()
Additional contact information
Aida Krichene: Department of Accounting, IHEC Carthage, Tunis, Tunisia

Journal of Economics, Finance and Administrative Science, 2017, vol. 22, issue 42, 3-24

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.

Keywords: ROC curve; Risk assessment; Default risk; Banking sector; Bayesian classifier algorithm (search for similar items in EconPapers)
JEL-codes: G14 (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3004121 Full text (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:joefas:0105

Access Statistics for this article

Journal of Economics, Finance and Administrative Science is currently edited by Nestor U. Salcedo

More articles in Journal of Economics, Finance and Administrative Science from Universidad ESAN 1652 Alonso de Molina, Santiago de Surco 15023, Lima, Peru. Contact information at EDIRC.
Bibliographic data for series maintained by ESAN Ediciones ().