An Improved Bank Credit Scoring Model: A Naïve Bayesian Approach
| Autor: | Samuel John, Olatunji Okesola, Kennedy O. Okokpujie, Adeyinka A. Adewale, Osemwegie Omoruyi |
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| Rok vydání: | 2017 |
| Předmět: |
Decision tool
Credit score business.industry Computer science 02 engineering and technology Machine learning computer.software_genre Banking sector Bank credit Naive Bayes classifier 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer ComputingMilieux_MISCELLANEOUS Credit risk |
| Zdroj: | 2017 International Conference on Computational Science and Computational Intelligence (CSCI). |
| DOI: | 10.1109/csci.2017.36 |
| Popis: | 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, Naive 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. |
| Databáze: | OpenAIRE |
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