Benchmarking state-of-the-art classification algorithms for credit scoring

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.

Keywords:

• credit scoring
• classification
• benchmarking

Acknowledgements

Part of this work was supported by GOA-Mefisto 666, IUAP V-10-29 and FWO project G.0407.02. TVG is on leave as a postdoctoral researcher with the FWO-Flanders at the K.U.Leuven - ESAT/SCD and is currently with Credit Methodology, Global Market Risk, Dexia Group. JS is a postdoctoral researcher with the FWO Flanders.