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

Benchmarking state-of-the-art classification algorithms for credit scoring
Benchmarking state-of-the-art classification algorithms for credit scoring
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.
credit scoring, classification, benchmarking
0160-5682
627-635
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Gestel, T.
ebd266da-f429-4493-a4e1-1f9a45c4c1c9
Viaene, S.
68e01ebc-8a4d-4460-86bf-18711fcee8d6
Stepanova, M.
11a1bef1-9fe4-46d2-8b99-94391ebffe50
Suykens, J.
753fa16a-8922-4ae4-97b0-2011bb37f3b0
Vanthienen, J.
808131f1-b77b-4dee-bdda-90a94124c999
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Gestel, T.
ebd266da-f429-4493-a4e1-1f9a45c4c1c9
Viaene, S.
68e01ebc-8a4d-4460-86bf-18711fcee8d6
Stepanova, M.
11a1bef1-9fe4-46d2-8b99-94391ebffe50
Suykens, J.
753fa16a-8922-4ae4-97b0-2011bb37f3b0
Vanthienen, J.
808131f1-b77b-4dee-bdda-90a94124c999

Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J. and Vanthienen, J. (2003) Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54 (6), 627-635. (doi:10.1057/palgrave.jors.2601545).

Record type: Article

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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More information

Published date: 2003
Keywords: credit scoring, classification, benchmarking

Identifiers

Local EPrints ID: 36518
URI: https://eprints.soton.ac.uk/id/eprint/36518
ISSN: 0160-5682
PURE UUID: 83603cb2-5b61-4024-80e8-9c47aea40b98

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Date deposited: 23 May 2006
Last modified: 15 Jul 2019 19:04

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