Article2009

Assessing naïve Bayes as a method for screening credit applicants

Anthony C. Antonakis · Michael E. Sfakianakis

46 citations0/0 files storedJournal of Applied Statistics

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Financial Distress and Bankruptcy Prediction Data Mining and Machine Learning Applications Imbalanced Data Classification Techniques

Abstract

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.

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References

1997Statistical Classification Methods in Consumer Credit Scoring: A Review 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers 2002Credit Scoring and Its Applications 1996A k-Nearest-Neighbour Classifier for Assessing Consumer Credit Risk 2003Credit scoring and rejected instances reassigning through evolutionary computation techniques 2000Credit scoring using neural and evolutionary techniques 2005Good practice in retail credit scorecard assessment 2001Modelling consumer credit risk

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2024Advancing credit risk modelling with Machine Learning: A comprehensive review of the state-of-the-art 2014Business Bankruptcy Prediction Based on Survival Analysis Approach 2023A recent review on optimisation methods applied to credit scoring models 2013An application of locally linear model tree algorithm with combination of feature selection in credit scoring 2020Ten-year evolution on credit risk research: a systematic literature review approach and discussion

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