Artificial Neural Network Model for Systems of Economic Security of Bank

DOI

10.2991/iscfec-18.2019.181How to use a DOI?

Keywords

Economic Security, Bank, Artificial Neural Network.

Abstract

Relevancy of the scientific work is due to necessity in studying problems of securing economic security, as in the modern market conditions the process of successful functioning and development of the bank system depends, to a large extent, on selection and perfection of measures to ensure its financial stability. The purpose of the research is to substantiate theoretically and practically the necessity in researching and developing additional mechanisms, improving the level of financial sector economic security. In accordance with the purpose, the work sets out and consecutively solves the following tasks: review of modern assessment methods of bank sphere economic security level, substantiates the necessity in perfection of existing approaches to provide high level of economic security. The work, using the method of neural modeling, analyzes macroeconomic indices of the Russian bank system during 2000-2017. The re-search results can be used for development of strategic landmarks of financial sector, perfection of mechanisms, improving economic security level. The value of the work is in accentuating the attention on the use of neural networks for analysis of economic networks, which can bring about major economic effect as these technologies can substantiate, by quantity, management decisions, which are taken as a rule, based on intuition.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - A. Gontar
AU  - D. B. Solovev
PY  - 2019/01
DA  - 2019/01
TI  - Artificial Neural Network Model for Systems of Economic Security of Bank
BT  - Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2018)
PB  - Atlantis Press
SP  - 802
EP  - 805
SN  - 2352-5428
UR  - https://doi.org/10.2991/iscfec-18.2019.181
DO  - 10.2991/iscfec-18.2019.181
ID  - Gontar2019/01
ER  -