Artikel
Using a naive Bayesian classifier methodology for loan risk assessment: Evidence from a Tunisian commercial bank
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Economics, Finance and Administrative Science ; ISSN: 2218-0648 ; Volume: 22 ; Year: 2017 ; Issue: 42 ; Pages: 3-24 ; Bingley: Emerald Publishing Limited
- Classification
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Wirtschaft
Information and Market Efficiency; Event Studies; Insider Trading
- Subject
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ROC curve
Risk assessment
Default risk
Banking sector
Bayesian classifier algorithm
- Event
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Geistige Schöpfung
- (who)
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Krichene, Aida
- Event
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Veröffentlichung
- (who)
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Emerald Publishing Limited
- (where)
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Bingley
- (when)
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2017
- DOI
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doi:10.1108/JEFAS-02-2017-0039
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Krichene, Aida
- Emerald Publishing Limited
Time of origin
- 2017
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