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124530 - Identification of failing banks using clustering with self-organising neural networks.pdf (484.79 kB)

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

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conference contribution
posted on 2023-05-24, 18:20 authored by Michael NegnevitskyMichael Negnevitsky
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.

History

Publication title

Procedia Computer Science

Volume

108C

Editors

P Koumoutsakos, M Lees, V Krzhizhanovskaya, J Dongarra, P Sloot

Pagination

1327-1333

ISSN

1877-0509

Department/School

School of Engineering

Publisher

Elsevier BV

Place of publication

Netherlands

Event title

International Conference on Computational Science

Event Venue

Zurich, Switzerland

Date of Event (Start Date)

2017-06-12

Date of Event (End Date)

2017-06-14

Rights statement

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/

Repository Status

  • Open

Socio-economic Objectives

Machinery and equipment not elsewhere classified

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