The University of Southampton
University of Southampton Institutional Repository

Bayesian kernel based classification for financial distress detection

Van Gestel, Tony, Baesens, Bart, Suykens, Johan A.K., Van den Poel, Dirk, Baestaens, Dirk-Emma and Willekens, Marleen (2006) Bayesian kernel based classification for financial distress detection European Journal of Operational Research, 172, (3), pp. 979-1003. (doi:10.1016/j.ejor.2004.11.009).

Record type: Article


Corporate credit granting is a key commercial activity of financial institutions nowadays. A critical first step in the credit granting process usually involves a careful financial analysis of the creditworthiness of the potential client. Wrong decisions result either in foregoing valuable clients or, more severely, in substantial capital losses if the client subsequently defaults. It is thus of crucial importance to develop models that estimate the probability of corporate bankruptcy with a high degree of accuracy. Many studies focused on the use of financial ratios in linear statistical models, such as linear discriminant analysis and logistic regression. However, the obtained error rates are often high. In this paper, Least Squares Support Vector Machine (LS-SVM) classifiers, also known as kernel Fisher discriminant analysis, are applied within the Bayesian evidence framework in order to automatically infer and analyze the creditworthiness of potential corporate clients. The inferred posterior class probabilities of bankruptcy are then used to analyze the sensitivity of the classifier output with respect to the given inputs and to assist in the credit assignment decision making process. The suggested nonlinear kernel based classifiers yield better performances than linear discriminant analysis and logistic regression when applied to a real-life data set concerning commercial credit granting to mid-cap Belgian and Dutch firms.

Full text not available from this repository.

More information

Published date: 2006
Additional Information: Interfaces with Other Disciplines
Keywords: credit scoring, kernel, fisher discriminant analysis, least squares support vector machine classifiers, bayesian inference


Local EPrints ID: 36733
ISSN: 0377-2217
PURE UUID: b1b0b3a9-64de-47e2-ab9e-72b2a6bc48bc

Catalogue record

Date deposited: 11 Jul 2006
Last modified: 17 Jul 2017 15:44

Export record



Author: Tony Van Gestel
Author: Bart Baesens
Author: Johan A.K. Suykens
Author: Dirk Van den Poel
Author: Dirk-Emma Baestaens
Author: Marleen Willekens

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.