Assessing naïve Bayes as a method for screening credit applicants

Assessing naïve Bayes as a method for screening credit applicants

The naïve Bayes rule (NBR) is a popular and often highly effective technique for constructing classification rules. This study examines the effectiveness of NBR as a method for constructing classification rules (credit scorecards) in the context of screening credit applicants (credit scoring). For t...

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Bibliographic Details
Published in:Journal of applied statistics Vol. 36; no. 5; pp. 537 - 545
Main Authors: Antonakis, A. C., Sfakianakis, M. E.
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 01-05-2009
Taylor and Francis Journals
Taylor & Francis Ltd
Series:Journal of Applied Statistics
Subjects:
Banking industry
Bayesian analysis
Benchmarks
Classification
credit risk
credit scorecard
Credit scoring
Datasets
Discriminant analysis
Effectiveness studies
naive Bayes
naïve Bayes
retail banking
Greece
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Summary:The naïve Bayes rule (NBR) is a popular and often highly effective technique for constructing classification rules. This study examines the effectiveness of NBR as a method for constructing classification rules (credit scorecards) in the context of screening credit applicants (credit scoring). For this purpose, the study uses two real-world credit scoring data sets to benchmark NBR against linear discriminant analysis, logistic regression analysis, k-nearest neighbours, classification trees and neural networks. Of the two aforementioned data sets, the first one is taken from a major Greek bank whereas the second one is the Australian Credit Approval data set taken from the UCI Machine Learning Repository (available at http://www.ics.uci.edu/~mlearn/MLRepository.html ). The predictive ability of scorecards is measured by the total percentage of correctly classified cases, the Gini coefficient and the bad rate amongst accepts. In each of the data sets, NBR is found to have a lower predictive ability than some of the other five methods under all measures used. Reasons that may negatively affect the predictive ability of NBR relative to that of alternative methods in the context of credit scoring are examined.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664760802554263