Okesola, J. O. and Okokpujie, Kennedy O. and John, S. N. and Omoruyi, Osemwegie (2017) An improved Bank Credit Scoring Model A Naïve Bayesian Approach. In: 2017 International Conference on Computational Science and Computational Intelligence, 14-16 December 2017, Las Vegas, USA.
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Abstract
Credit scoring is a decision tool used by organizations to grant or reject credit requests from their customers. Series of artificial intelligent and traditional approaches have been used to building credit scoring model and credit risk evaluation. Despite being ranked amongst the top 10 algorithm in Data mining, Naïve Bayesian algorithm has not been extensively used in building credit score cards. Using demographic and material indicators as input variables, this paper investigate the ability of Bayesian classifier towards building credit scoring model in banking sector.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | A General Works > AS Academies and learned societies (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Engr. Kennedy O. Okokpujie |
| Date Deposited: | 25 Apr 2018 08:46 |
| Last Modified: | 25 Apr 2018 08:46 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/10684 |
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