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Assessing naive Bayes as a method for screening credit applicants

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  • A. C. Antonakis
  • M. E. Sfakianakis

Abstract

The naive 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.

Suggested Citation

  • A. C. Antonakis & M. E. Sfakianakis, 2009. "Assessing naive Bayes as a method for screening credit applicants," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(5), pages 537-545.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:5:p:537-545
    DOI: 10.1080/02664760802554263
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    References listed on IDEAS

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    1. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    2. David Hand & Niall Adams, 2000. "Defining attributes for scorecard construction in credit scoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 527-540.
    3. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    4. D J Hand, 2005. "Good practice in retail credit scorecard assessment," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1109-1117, September.
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    2. Mohammad Siami & Mohammad Reza Gholamian & Javad Basiri, 2014. "An application of locally linear model tree algorithm with combination of feature selection in credit scoring," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(10), pages 2213-2222, October.
    3. Mo Leo S. F. & Yau Kelvin K. W., 2010. "Survival Mixture Model for Credit Risk Analysis," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 4(2), pages 1-20, July.

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