Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2020)

Analysis and Forecasting of Credit Institutions Bankruptcy Using Neural Network Modeling

Authors
V P Pervadchuk, D B Vladimirova, A A Yudin
Corresponding Author
V P Pervadchuk
Available Online 17 March 2020.
DOI
https://doi.org/10.2991/aebmr.k.200312.260How to use a DOI?
Abstract

This work is devoted to the study of the probabilistic bankruptcy state of Russian Federation credit institutions. It develops a tool (neural network) designed to assess the financial condition (bankruptcy) of banks. Data collection and generalization are carried out, the results of numerical modeling are shown. The neural network is created and optimized with the help of collected training sample. Subsequently, several tasks related to the assessment of financial condition are solved. The work has an applied nature, as the results can be useful for credit institutions of the Russian Federation.

Copyright
© 2020, 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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Volume Title
Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2020)
Series
Advances in Economics, Business and Management Research
Publication Date
17 March 2020
ISBN
978-94-6252-929-8
ISSN
2352-5428
DOI
https://doi.org/10.2991/aebmr.k.200312.260How to use a DOI?
Copyright
© 2020, 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/).

Cite this article

TY  - CONF
AU  - V P Pervadchuk
AU  - D B Vladimirova
AU  - A A Yudin
PY  - 2020
DA  - 2020/03/17
TI  - Analysis and Forecasting of Credit Institutions Bankruptcy Using Neural Network Modeling
BT  - Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2020)
PB  - Atlantis Press
SP  - 1865
EP  - 1869
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.200312.260
DO  - https://doi.org/10.2991/aebmr.k.200312.260
ID  - Pervadchuk2020
ER  -