The Relevance of the Application of the Neural Approaches in the Premature Detection of the Banking Difficulties: Case of Tunisia
Abstract
We propose in this study to test the relevance of the use of an early warning system (EWS) for banking difficulties in the Tunisian case. This model is based on the use of a multilayer neural network with a back-propagation algorithm. From a sample of 18 Tunisian banks, we try to establish the different ratios of the financial health of banks, extracted from CAMEL rating system. With a good ranking percentage obtained for the total sample of more than 93%, we conclude that neural networks are found to be robust for the prediction of the fragility of the Tunisian banks.
Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.
Copyrights for articles published in MTI journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.
