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

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

0.00 Avg rating0 Votes
Article ID: iaor20041045
Country: United Kingdom
Volume: 54
Issue: 6
Start Page Number: 627
End Page Number: 635
Publication Date: Jun 2003
Journal: Journal of the Operational Research Society
Authors: , , , , ,
Keywords: performance
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 (e.g. 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.

Reviews

Required fields are marked *. Your email address will not be published.