ASSESSMENT OF RISK OF BANKRUPTCY OF BANKS OF UKRAINE BY A MODERN METHOD OF ARTIFICIAL NEURAL NETWORKS

  • Zoya Gadetska Cherkassy Bogdan Khmelnitsky National University
Keywords: method of artificial neural networks, assessment of risk of bankru, bank, bankruptcy forecasting

Abstract

The article examines modern methods of bankruptcy risk assessment of Ukrainian banks. Bankruptcy of any bank has negative consequences for a wide range of subjects and can lead to negative processes in all economy. Diagnostics of bankruptcy is a timely detection of insolvency, unprofitability, financial dependence on external sources of financing, low business activity. As a rule, in classical models of diagnostics of bankruptcy use indicators of profitability, financial stability, liquidity and business activity. For diagnostics of bankruptcy in the world it is used the models constructed on the basis of financial coefficients today. In article the comparative analysis of such most known models is carried out. From a set of foreign methods of management of risk of the simplest and widespread method of the analysis the GАР-management method is. But as practice shows, one method for exact assessment of probability of bankruptcy is not enough. And uses of models and methods focused on the developed countries is it is not quite relevant to economy of Ukraine. Today there is an urgent need of development of modern model of forecasting of bankruptcy of banks in the conditions of uncertainty and doubtful data for realities of the Ukrainian bank sphere, but which would be simple and convenient in use. As an alternative to statistical methods, for forecasting of risk of bankruptcy of the Ukrainian banks, the modern neural network model can be used. This model will be useful to clients of banks which want to define insolvent banks in the nearest future (1–1,5 years). Therefore in this article the modern method of modeling of assessment of probability of bankruptcy of banks ₋ a method of artificial neural networks is offered. Direct testing of a possibility of application of neural network for definition of bankruptcy of banks of Ukraine was held on a set from 126 test data sets which were selected from quarterly financial statements of 5 banks of Ukraine, three of which are solvent, and two are in an elimination stage. The research showed that it is expedient to use a method of artificial neural networks at assessment of risk of bankruptcy of the banking sector in general. It will allow to allocate, in the presence of necessary data, banks which are solvent and insolvent, that is, have rather high percent of risk of bankruptcy.

References

Харченко Ю.А. Дослідження ймовірності банкрутства підприємства (на прикладі ПАТ «Укргазвидобування») / Ю.А. Харченко, К.В. Волкорез // Економічний простір. 2016. № 111. С. 208-218. URL: https://drive.google.com/file/d/0B7pprnAm_UuOMU1pbDlHSDM4NlE/view.

Жердецька Л.В. Розвиток моделей прогнозування банкрутства банків / Л.В. Жердецька, І.С. Постирнак // Глобальні та національні проблеми економіки. 2016. № 14. С. 796-801. URL: http://global-national.in.ua/issue-14-2016/22-vipusk-14-gruden-2016-r/2653-zherdetska-l-v-postirnak-i-s-rozvitok-modelej-prognozuvannya-bankrutstva-bankiv.

Москаленко В.М. Характеристика методів та моделей діагностики кризового стану підприємства / В.М. Москаленко // Наукові праці Кіровоградського національного технічного університету. Економічні науки. 2012. Вип. 22(2). С. 297-303. URL: http://dspace.kntu.kr.ua/jspui/bitstream/123456789/884/1/Z22_%d0%86%d0%86_2012.pdf.

Думенко Н.М. Методика GAP менеджменту в оцінці ризику зміни процентних ставок в банківській системі України / Н.М. Думенко // Економічний вісник Національного гірничого університету. 2013. № 4. С. 97-102. URL: http://ev.nmu.org.ua/index.php/ru/archive?arh_article=750.

Проскуряков К.І. Методологічні підходи запобігання банкрутству банків / К.І. Проскуряков, В.В. Бондаренко // Сталий розвиток економіки. 2015. № 1. С. 245-251. URL: https://www.uniep.km.ua/pdf/_1_2015.pdf.

Марченко Д. Управління конкурентоспроможністю банку / Д. Марченко, З. Гадецька // Гуманітарний простір науки: досвід та перспективи»: зб. Матеріалів ХI Міжнарод. наук. практ. інтернет-конф., 17 травня 2017 р. Переяслав-Хмельницький, 2017. Вип. 11. С. 22-25. URL: http://files.humanitarica.webnode.com.ua/200000137-d4029d4fb7/Гуманітарика%2011.pdf.

Роберт Каллан. Основные концепции нейронной сети / Роберт Каллан; [пер. с англ. А. Г. Сивака]. М.: Издательский дом «Вильямс», 2001. 287 с.

Тарик Рашид. Создаем нейронную сеть / Рашид Тарик; [пер. с англ. и ред. к. х. н. А.Г. Гузікевич СПб.: ООО «Альфа-книга»]. М. СПб. К.: «Диалектика», 2017. 272 с.

Kharchenko, Yu.A., Volkorez K.V. (2016) Doslidzhennia ymovirnosti bankrutstva pidpryiemstva (na prykladi PAT "Ukrhazvydobuvannia") [Investigation of the probability of bankruptcy of the enterprise (for example, PAT "Ukrgazvydobuvannya")]. Ekonomichnyi prostir [Economic space] (electronic journal), no.111, pp. 208−218. Available at: https://drive.google.com/file/d/0B7pprnAm_UuOMU1pbDlHSDM4NlE/view (Accessed 03 February 2019).

Zherdetska L.V., Postman I.S. (2016) Rozvytok modelei prohnozuvannia bankrutstva bankiv [Development of bankruptcy forecasting models for banks]. Hlobalni ta natsionalni problemy ekonomiky [Global and national problems of the economy] (electronic journal), no.14, pp. 796−801. Available at: http://global-national.in.ua/issue-14-2016/22-vipusk-14-gruden-2016-r/2653-zherdetska-l-v-postirnak-i-s-rozvitok-modelej-prognozuvannya-bankrutstva-bankiv (Accessed 03 February 2019).

Moskalenko, V.M. (2012), Kharakterystyka metodiv ta modelei diahnostyky kryzovoho stanu pidpryiemstva [Characteristics of methods and models of diagnostics of the crisis state of the enterprise], Naukovi pratsi Kirovohradskoho natsionalnoho tekhnichnoho universytetu [Scientific works of the Kirovohrad National Technical University. Economic Sciences] (electronic journal), vol. 22(2), pp. 297−303. Available at: http://dspace.kntu.kr.ua/jspui/bitstream/123456789/884/1/Z22_%d0%86%d0%86_2012.pdf (Accessed 03 February 2019).

Dumenko, N.M. (2013) Metodyka GAP menedzhmentu v otsintsi ryzyku zminy protsentnykh stavok v bankivskii systemi Ukrainy [GAP Management Methodology in Assessing the Risk of Changing Interest Rates in the Banking System of Ukraine]. Ekonomichnyi visnyk Natsionalnoho hirnychoho universytetu [Economic Bulletin of the National Mining University] (electronic journal), no. 4, pp. 97−102. Available at: http://ev.nmu.org.ua/index.php/ru/archive?arh_article=750 (Accessed 03 February 2019).

Proskuryakov, K.I., Bondarenko, V.V. (2015) Metodolohichni pidkhody zapobihannia bankrutstvu bankiv [Methodological approaches to prevent bankruptcy of banks]. Stalyi rozvytok ekonomiky [Sustainable development of the economy] (electronic journal), no.1, pp. 245−251. Available at: https://www.uniep.km.ua/pdf/_1_2015.pdf (Accessed 03 February 2019).

Marchenko, D., Gadetskaya Z. (2017) Upravlinnia konkurentospromozhnistiu banku [Managing the Bank's Competitiveness]. Proceedings of the Humanitarnyi prostir nauky: dosvid ta perspektyvy (Pereyaslav-Khmelnitsky, May 17, 2013), vol. 11, pp. 22−25. Available at: http://files.humanitarica.webnode.com.ua/200000137-d4029d4fb7/Гуманітарика%2011.pdf (Accessed 03 February 2019).

Robert Callan (2001) Osnovnye koncepcii nejronnoj seti [Basic concepts of the neural network]. M.: Williams Publishing House, 287 p.

Tarik Rashid (2017) Sozdaem nejronnuju set' [Create a Neural Network]. K .: "Dialectics". 272 p.

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Published
2019-12-25
How to Cite
Gadetska, Z. (2019). ASSESSMENT OF RISK OF BANKRUPTCY OF BANKS OF UKRAINE BY A MODERN METHOD OF ARTIFICIAL NEURAL NETWORKS. Economy and Society, (20). Retrieved from http://economyandsociety.in.ua/index.php/journal/article/view/20
Section
MATHEMATICAL METHODS, MODELS AND INFORMATION TECHNOLOGIES IN ECONOMY