The article is devoted to developing a definition of the indicator of the bank’s competitiveness which based on the theory of fuzzy sets and neural networks techniques. Uncertainties that have a place when considering and analyzing the components of evaluating the success and effectiveness of the bank have been considered and analyzed. The sequence of construction and structure for generalizing parameter of bank competitiveness are presented and grounded. Stages of obtaining an integrated assessment of bank competitiveness by sequential application of fuzzy logic and neural networks approaches are determined and described. Corresponding fuzzy terms, membership functions and fuzzy inference rules are described. Overall sequence and steps to resolve the problem are processed. The practical implementation of the summary fuzzy inference of the bank’s competitiveness is given. In particular, numerical calculations on the proposed model for Ukrainian commercial bank “Khreshchatyk” was carried out. Comparison of obtained evaluation results for the competitiveness of specified bank with available data and other scientific information sources showed their compliance with factual situation. In this way, the expediency of application fuzzy modeling has been confirmed to determine the generalized indicators of bank competitiveness. Adequacy and accuracy of the proposed model and the results of calculations were proved. The proposed approach is quite general. This or similar model can be successfully used in other tasks of building and generalized evaluation of integrated indicators for the presence of several local, individual parameters for different economic processes and tasks.
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1. Anfilatov V. Emelyanov A. & Kukushkin A. (2003). System analysis in the management [Sistemnyy analiz v upravlenii]. Finance and Statistics Moscow 368 p.
2. Asanovic Z. (2017). Predicting Systemic Banking Crises Using Early Warning Models: The Case of Montenegro. Journal of Central Banking Theory and Practice6(3) 157–182
3. Barros C. (2016). Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping. European Journal of Operational Research249(1) 378-389.
4. Barsky A. (2004). Neural networks: recognition decision-making [Neyronnyye seti: raspoznavaniye prinyatiye resheniy]. Finance and Statistics Moscow 176 p.
5. Borisov V. Kruglov V. & Fedulov A. (2007). Fuzzy models and networks [Nechetkiye modeli i seti]. Hotline - Telecom Moscow 284 p.
6. Borisov A. & Krumberg O.(1990). Decision-making based on fuzzy models: examples of use [Prinyatiye resheniy na osnove nechetkikh modeley: primery]. Zinatne Riga 184 p.
7. Braendle U. & Sepasi S. (2014). Fuzzy Evaluation Of Service Quality In The Banking Sector: A Decision Support System. Fuzzy Economic Review10(2) 47-79.
8. Cveshnikov C. & Bocharnikov V. (2007). Fexcel program to work with fuzzy numbers in an environment MSExcel version 4.0 [Programma Fexcel dlya raboty s nechetkimi chislami v srede MSExcel versiya 4.0]. INEX Consulting Group Kiev 60 p.
9. Degl’Innocenti M. Kourtzidis S. Sevic Z. & Tzeremes N. (2017). Bank productivity growth and convergence in the European Union during the financial crisis. Journal of Banking & Finance75(C) 184-199.
10. Dolgikh V. (2016). Non-parametric estimation of efficiency of the Ukrainian banking system” [“Neparametrychni otsinky efektyvnosti ukrayinsʹkoyi bankivsʹkoyi systemy”]. Proceedings of the National Bank of Ukraine2(204) 29–35.
11. Dorokhov O. Chernov V. Dorokhova L. & Streimkis J. (2018). Multicriteria choice of alternatives under fuzzy information. Transformations in Business and Economics17(2) 95-106.
12. Dorokhov O. & Dorokhova L. (2011). Fuzzy model in fuzzy-tech environment for the evaluation of transportation’s quality for cargo enterprises in Ukraine. Transport and Telecommunication12(1) 25-33
13. Fabris N. & Vujanovic N. (2017). The Impact of Financial Dollarization on Inflation Targeting: Empirical Evidence from Serbia. Journal of Central Banking Theory and Practice6(2) 23–43.
14. Fernando A. Ferreira F. Jalali M. Ferreira J. Stankevičienė J. & Marques C. (2016) Understanding the dynamics behind bank branch service quality in Portugal: pursuing a holistic view using fuzzy cognitive mapping. Service Business10(3) 469-487.
15. Goncharuk A. (2016). Banking Sector Challenges in Research. Journal of Applied Management and Investments5(1) 34-39.
16. Hooman A. Marthandan G. Yusoff W. & Omid M. (2016). Statistical and data mining methods in credit scoring. Journal of Developing Areas50(5) 371-381.
17. Koybichuk V. (2013). Formation of models for features space of bank competitiveness [Formuvannya oznakovoho prostoru modeli konkurentospromozhnosti banku]. Bulletin of Khmelnytsky National University4(1) 173–179.
18. Koybichuk V. (2012). Conceptual model of the bank’s competitiveness in modern conditions [Kontseptualʹna modelʹ konkurentosti banku v suchasnykh umovakh]. Bulletin of the University of Banking of National Bank of Ukraine2(14) 323–329.
19. Kukal J. & Vanquang T. (2014). A Monetary Policy Rule Based on Fuzzy Control in an Inflation Targeting Framework. Prague Economic Papers2014(3) 290-314.
20. Lapteacru I. (2014). Do more competitive banks have less market power? The evidence from Central and Eastern Europe. Journal of International Money and Finance46 41-60.
21. Leonenkov F. (2005). Fuzzy modeling in MATLAB environment and fuzzyTECH [Nechetkoye modelirovaniye v srede MATLAB i fuzzyTECH]. BHV S.Petersburg 736 p.
22. Luburic R. & Fabris N. (2017). Money and the Quality of Life. Journal of Central Banking Theory and Practice6(3) 17–34
23. Malyaretz L. Dorokhov O. & Dorokhova L. (2018).Method of constructing the fuzzy regression model of bank competitiveness. Journal of Central Banking Theory and Practice7(2) 139-160.
24. Mandic K. Delibasic B. Knezevic S.& Benkovic S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling43(C) 30-37.
25. Matviychuk A. (2005) Analysis and forecasting of financial and economic systems with the use of fuzzy logic [Analiz ta prohnozuvannya rozvytku finansovo-ekonomichnykh system iz vykorystannyam teoriyi nechitkoyi lohiky]. Center of educational literature Kiev 209 p.
26. Menicucci E. Paolucci G. Zain M. & Rasit Z. (2016). The determinants of bank profitability: empirical evidence from European banking sector. Journal of Financial Reporting and Accounting14(1) 86-115.
27. Omelchenko O. Dorokhov O. Kolodiziev O. & Dorokhova L. (2018). Fuzzy modeling of the creditworthiness assessments of bank’s potential borrowers in Ukraine. Ikonomicheski Izsledvania (Economic Studies)27(4) 100-125.
28. Ponomarenko V. Gontareva I. & Dorokhov O. (2014). Statistical testing of key effectiveness indicators of the companies (Case for Ukraine in 2012). Ikonomicheski Izsledvania (Economic Studies)23(4) 108-124.
29. Ponomarenko V. & Malyarets L. (2009). Multivariate analysis of socioeconomic systems [Bahatovymirnyy analiz sotsialʹno-ekonomichnykh system]. KhNUE Kharkiv 384 p.
30. Rezaei M. & Ketabi S. (2016). Ranking the Banks through Performance Evaluation by Integrating Fuzzy AHP and TOPSIS Methods. International Journal of Academic Research in Accounting Finance and Management Sciences6(3) 19-30.
32. Shiryaev V. (2007). Financial markets and neural networks [Finansovyye rynki i neyronnyye seti]. LKI Publisher Moscow 224 p.
33. Shtovba S. (2006). Construction of functions of fuzzy sets for clustering experimental data [Pobudova funktsiy nalezhnosti nechitkykh mnozhyn klasteryzatsiyeyu eksperymentalʹnykh danykh]. Information technologies and computer engineering2 92–95.
34. Yarushkina N. & Afanasyeva T. (2007). Fuzzy time series as a tool for assessing and measuring the dynamics of the processes [Nechetkiye vremennyye ryady kak instrument dlya otsenki i izmereniya dinamiki protsessov]. Sensors and Systems12 46–51.
35. Yarushkina N. Afanasyeva T. & Perfyleva I. (2010). An intellectual analysis of time series [Intellektual’nyy analiz vremennykh ryadov]. UlSTU Ulyanovsk 320 p.
36. Yarushkina N. (2004). Fundamentals of the theory of fuzzy and hybrid systems [Osnovy teorii nechetkikh i gibridnykh sistem]. Finance and Statistics Moscow 320 p.