| JEL Classification: G21; G30; G34; M10. | DOI: https://doi.org/10.31521/modecon.V56(2026)-16 |
Eduard Zaiats, Postgraduate student of the Department of Banking, Odesa National Economic University, Odesa, Ukraine
ORCID: 0009-0006-6110-8033
e-mail: zaiats_eduard@ukr.net
Business Model Identification of Ukrainian Banks During 2022–2024: Challenges, Adaptation, and Future Outlook
Abstract. Introduction. This article explores how Ukrainian banks have transformed their business models during the period from 2022 to 2024 amid profound economic, social, and political upheaval caused by the full-scale war in Ukraine. The topic’s relevance is determined by the urgent need for banks to promptly respond to external challenges and revise their strategic orientations to ensure the banking sector’s resilience, profitability, and effectiveness.
Purpose. The primary objective is to identify, classify, and analyze the business models of Ukrainian banks from 2022 to 2024, trace their evolution in response to external shocks, and outline the prospects for developing the banking system in the postwar period.
Object. The study focuses on the formation of Ukrainian banks’ business models and aims to identify and classify the dominant types during the specified period. Additionally, it seeks to analyze the key factors contributing to their transformation. The research is theoretically founded on modern approaches to strategic management, banking operations, and economic-statistical modeling.
Results. The study uses hierarchical cluster analysis — specifically, Ward’s method — and the k-means method in the STATISTICA software environment to identify and verify banks’ business models. These models are based on indicators that reflect the specifics of their operational activity. The analysis used monthly supervisory statistics from the National Bank of Ukraine. Key indicators included the proportion of loans, deposits, investment portfolios, and interbank operations within the banks’ balance sheets. The analysis covered 67 banks classified into six business models: corporate, retail, universal, investment, investment with capital funding, and traditional.
Conclusions. The results indicate a growing trend toward universal business models as an adaptive response to rising economic uncertainty, changes in demand for financial services, and digital transformation. At the same time, the proportion of banks highly dependent on investment activity is increasing, requiring greater regulatory oversight.
Keywords: bank; bank business model; bank migration; bank clustering; factor symptoms.
References:
- Zherdetska, L.V., & Batenieva, K.O. (2023). Modern business models of Ukrainian banks: identification and evaluation. Scientific Bulletin of Odesa National Economic University, 5–6, 306–307. DOI: 10.32680/2409-9260-2023-5-6-306-307-47-53.
- Zarutska, O.P., Novikova, L.F., & Hryhel, A.V. (2023). Features of business models of Ukrainian banks. Scientific View: Economics and Management, 4(84), 101–110.
- Zaiats, E.L., & Onyshchenko, Yu.I. (2019). Identification and assessment of business models of Ukrainian banks based on cluster analysis. Regional Economy and Management, 2, 100–104.
- Grouped balance data (by banks) of supervisory statistics of the National Bank of Ukraine. https://bank.gov.ua/ua/statistic/supervision-statist.
- Onyshchenko, Y.I. (2020). Bank business model: essence and interrelation with the strategy for development. Eсonomic scope, 160, 113-117. DOI: https://doi.org/10.32782/2224-6282/160-21.
- Onyshchenko, Yu.I., & Zaiats, E.L. (2020). Defining types of bank business models in the banking system of Ukraine. Eastern Europe: Economics, Business and Management, 4(27). https://easterneurope-ebm.in.ua/journal/27_2020/22.pdf.
- Rashkovan, V., & Pokidin, D. (2016). Cluster analysis of business models of Ukrainian banks: application of Kohonen neural networks. Bulletin of the National Bank of Ukraine, 12, 13–40.
- Sotska, Yu.I. (2015). Methodological principles of cluster analysis of the competitiveness of Ukrainian banks. Financial and Credit Activity: Problems of Theory and Practice, 2, 177–185. http://nbuv.gov.ua/UJRN/Fkd_2015_2_22.
- Shkolnyk, I.O., & Akopyan, D.E. (2021). Theoretical justification and classification of bank business models. Bulletin of Sumy State University. Economics Series, 1, 128–136. DOI: 10.21272/1817-9215.2021.1-15. https://essuir.sumdu.edu.ua/bitstream-download/123456789/84073/1/Shkolnyk_business_model.pdf.
- Shulha, N., & Omelenchuk, V. (2021). Clustering banks by business models. Bulletin of Kyiv National University of Trade and Economics, 140 (6). DOI: https://doi.org/10.31617/visnik.knute.2021(140)10.
- Strauss T., von Maltitz M.J. (2017). Generalising Ward’s Method for Use with Manhattan Distances. PLoS ONE, 12(1): e0168288. DOI: https://doi.org/10.1371/journal.pone.0168288.
- Arora P., Deepali, Varshney S. (2016). Analysis of K-Means and K-Medoids Algorithm for Big Data. International Conference on Information Security & Privacy (ICISP 2015), 11–12 December 2015, Nagpur, India. Procedia Computer Science, 78, 507–512. DOI: https://doi.org/10.1016/j.procs.2016.02.095.
Received: 21 March 2026

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How to quote this article? |
| Zaiats E. (2026). Business Model Identification of Ukrainian Banks During 2022–2024: Challenges, Adaptation, and Future Outlook. Modern Economics, 56(2026), 112-121. DOI: https://doi.org/10.31521/modecon.V56(2026)-16. |







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