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Identification of failing banks using clustering with self-organising neural networks
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ORCID: 0000-0002-5130-419X 2017
, 'Identification of failing banks using clustering with self-organising neural networks'
, Procedia Computer Science, vol. 108C
, pp. 1327-1333
, doi: https://doi.org/10.1016/j.procs.2017.05.125.
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Abstract
This paper presents experimental results of cluster analysis using self organising neural networks for identifying failing banks. The paper first describes major reasons and likelihoods of bank failures. Then it demonstrates an application of a self-organising neural network and presents results of the study. Findings of the paper demonstrate that a self-organising neural network is a powerful tool for identifying potentially failing banks. Finally, the paper discusses some of the limitations of cluster analysis related to understanding of the exact meaning of each cluster.
| Item Type: | Article |
|---|---|
| Authors/Creators: | Negnevitsky, M |
| Keywords: | Cluster analysis, self-organising neural network, kohonen layer |
| Journal or Publication Title: | Procedia Computer Science |
| Publisher: | Elsevier BV |
| ISSN: | 1877-0509 |
| DOI / ID Number: | https://doi.org/10.1016/j.procs.2017.05.125 |
| Copyright Information: | Copyright 2017 The Authors. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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