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Linear and nonlinear credit scoring by combining logistic regression and support vector machines

Linear and nonlinear credit scoring by combining logistic regression and support vector machines
Linear and nonlinear credit scoring by combining logistic regression and support vector machines
The Basel II capital accord encourages banks to develop internal rating models that are financially intuitive, easily interpretable and optimally predictive for default. Standard linear logistic models are very easily readable but have limited model flexibility. Advanced neural network and support vector machine models (SVMs) are less straightforward to interpret but can capture more complex multivariate non-linear relations. A gradual approach that balances the interpretability and predictability requirements is applied here to rate banks. First, a linear model is estimated; it is then improved by identifying univariate non-linear ratio transformations that emphasize distressed conditions; and finally SVMs are added to capture remaining multivariate non-linear relations.
1744-6619
Van Gestel, T.
ebd266da-f429-4493-a4e1-1f9a45c4c1c9
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Dijcke, P.
a0e0e691-04ba-4d28-bd7d-46303c4a3391
Suykens, J.
753fa16a-8922-4ae4-97b0-2011bb37f3b0
Garcia, J.
5db52c32-f640-498a-8766-76a2641096e4
Alderweireld, T.
eaa7d5ac-ef80-4c4a-8ba5-89630bd6f7cc
Van Gestel, T.
ebd266da-f429-4493-a4e1-1f9a45c4c1c9
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Dijcke, P.
a0e0e691-04ba-4d28-bd7d-46303c4a3391
Suykens, J.
753fa16a-8922-4ae4-97b0-2011bb37f3b0
Garcia, J.
5db52c32-f640-498a-8766-76a2641096e4
Alderweireld, T.
eaa7d5ac-ef80-4c4a-8ba5-89630bd6f7cc

Van Gestel, T., Baesens, B., Van Dijcke, P., Suykens, J., Garcia, J. and Alderweireld, T. (2005) Linear and nonlinear credit scoring by combining logistic regression and support vector machines. Journal of Credit Risk, 1 (4).

Record type: Article

Abstract

The Basel II capital accord encourages banks to develop internal rating models that are financially intuitive, easily interpretable and optimally predictive for default. Standard linear logistic models are very easily readable but have limited model flexibility. Advanced neural network and support vector machine models (SVMs) are less straightforward to interpret but can capture more complex multivariate non-linear relations. A gradual approach that balances the interpretability and predictability requirements is applied here to rate banks. First, a linear model is estimated; it is then improved by identifying univariate non-linear ratio transformations that emphasize distressed conditions; and finally SVMs are added to capture remaining multivariate non-linear relations.

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Published date: 2005

Identifiers

Local EPrints ID: 42649
URI: http://eprints.soton.ac.uk/id/eprint/42649
ISSN: 1744-6619
PURE UUID: 3bccb272-a329-4009-a8be-b832b23345d3
ORCID for B. Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 20 Dec 2006
Last modified: 24 Jul 2020 01:36

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Contributors

Author: T. Van Gestel
Author: B. Baesens ORCID iD
Author: P. Van Dijcke
Author: J. Suykens
Author: J. Garcia
Author: T. Alderweireld

University divisions

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