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Identification of failing banks using clustering with self-organising neural networks

Citation

Negnevitsky, M, Identification of failing banks using clustering with self-organising neural networks, Proceedings of INTELLI 2013: The Second International Conference on Intelligent Systems and Applications, 21-26 April 2013, Venice, Italy, pp. 1-5. ISBN 9781627484817 (2013) [Refereed Conference Paper]

Copyright Statement

Copyright 2013 International Academy, Research, and Industry Association (IARIA)

Official URL: http://www.iaria.org/conferences2013/INTELLI13.htm...

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 Details

Item Type:Refereed Conference Paper
Keywords:cluster analysis, self-organising neural network, Kohonen layer, likelihood of bank failure
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Power and Energy Systems Engineering (excl. Renewable Power)
Objective Division:Energy
Objective Group:Energy Conservation and Efficiency
Objective Field:Industrial Energy Conservation and Efficiency
Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:88399
Year Published:2013
Deposited By:Engineering
Deposited On:2014-01-31
Last Modified:2017-11-06
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