Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/179782
Authors: 
Krichene, Aida
Year of Publication: 
2017
Citation: 
[Journal:] Journal of Economics, Finance and Administrative Science [ISSN:] 2218-0648 [Volume:] 22 [Year:] 2017 [Issue:] 42 [Pages:] 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.
Subjects: 
ROC curve
Risk assessment
Default risk
Banking sector
Bayesian classifier algorithm
JEL: 
G14
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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