Benchmarking state-of-the-art classification algorithms for credit scoring
Baesens, B Van Gestel, T Viaene, S Stepanova, M Suykens, J Vanthienen, J
Baesens, B
Van Gestel, T
Viaene, S
Stepanova, M
Suykens, J
Vanthienen, J
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2003-06
Journal
Journal of the Operational Research Society
Book
Publication Volume
54
Publication Issue
6
Publication Begin page
627
Publication End page
635
Publication Number of pages
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Abstract
In this paper, we study the performance of various state-of-the-art classification algorithms applied to eight real-life credit scoring data sets. Some of the data sets originate from major Benelux and UK financial institutions. Different types of classifiers are evaluated and compared. Besides the well-known classification algorithms (eg logistic regression, discriminant analysis, k-nearest neighbour, neural networks and decision trees), this study also investigates the suitability and performance of some recently proposed, advanced kernel-based classification algorithms such as support vector machines and least-squares support vector machines (LS-SVMs). The performance is assessed using the classification accuracy and the area under the receiver operating characteristic curve. Statistically significant performance differences are identified using the appropriate test statistics. It is found that both the LS-SVM and neural network classifiers yield a very good performance, but also simple classifiers such as logistic regression and linear discriminant analysis perform very well for credit scoring.
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Keywords
4605 Data Management and Data Science, 46 Information and Computing Sciences, 4611 Machine Learning