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


Negnevitsky, M, Identification of failing banks using clustering with self-organising neural networks, Procedia Computer Science, 12-14 June 2017, Zurich, Switzerland, pp. 1327-1333. ISSN 1877-0509 (2017) [Conference Extract]


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Copyright 2017 The Authors. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

DOI: doi:10.1016/j.procs.2017.05.125


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:Conference Extract
Keywords:cluster analysis, self-organising neural network, kohonen layer
Research Division:Engineering
Research Group:Control engineering, mechatronics and robotics
Research Field:Field robotics
Objective Division:Manufacturing
Objective Group:Machinery and equipment
Objective Field:Machinery and equipment not elsewhere classified
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:129718
Year Published:2017
Web of Science® Times Cited:4
Deposited By:Engineering
Deposited On:2018-12-17
Last Modified:2018-12-17
Downloads:120 View Download Statistics

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