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Bayesian kernel based classification for financial distress detection

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.

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Citation

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).

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

Identifiers

Local EPrints ID: 36733
URI: http://eprints.soton.ac.uk/id/eprint/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

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Contributors

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

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