The analysis of business models of banks is a new approach to determining the financial condition and financial soundness of an individual bank and the entire banking system. The definition and analysis of banks' business models allow understanding better financial and economic activities, risk appetite, and management system. The National Bank of Ukraine moves to SREP based banking supervision. Such an analysis involves the verification of banks' business models for their viability and sustainability. No regulatory act provides a precise definition of these concepts. It is still no single approach to the analysis of business models among scientists and researchers. At the same time, traditional methods that focused on the analysis of the bank's capital adequacy, its liquidity, and compliance with mandatory NBU economic norms are not sufficient. The study shows that most researchers use cluster analysis methods with a variety of sets of variables, the number of cluster groups, and business models. To determine the business models of Ukrainian banks, to analyze them, and to form on this basis the risk profile of each bank, the authors proposed an innovative methodology of structural-functional groups of banks (SFGB-method). The method is based on the use of neural networks, in particular self-organizing Kohonen maps (SOM). For cluster analysis, it is suggested to use the system of financial indicators calculated by the National Bank of Ukraine in the SREP system. The cluster analysis allows identifying ten business models of Ukrainian banks. The article describes the features of each cluster and its propensity to take risks. The distribution of banks by cluster and their place on the map depends mostly on the structure of its assets, liabilities, income, and expenses, currency position, as well as other qualitative and quantitative indicators. The conducted research has confirmed that the definition of business models of banks allows forming the basis for introducing a differentiated approach to banking regulation and supervision, taking into account the essential characteristics of each bank, its risk profile, and the main distinguishing features. Keywords: bank business model, innovative approaches, bank risk profile, structural-functional group, bank.

The article analyzes changes in the business models of Ukrainian banks using the author's method of structural and functional groups of banks (SFGB). The method’s basis is the processing, systematization, and visualization of the system’s values of banks’ financial indicators using Kohonen’s self-organizing map (SOM). Depending on the level distribution of a large number of indicators that characterize the structure of assets, liabilities, income, expenses, and other qualitative indicators that describe the business models of each bank on successive reporting dates, homogeneous groups of banks are formed. The purpose of this study is to compare the key features of the banking system as of January 1 and September 1, 2022, and the corresponding changes in business models.Over the eight months of 2022, the number of banks with corporate lending increased slightly, but the resource base of these banks gradually changed. The number of banks with retail financing decreased at the expense of banks with current resources. During an increase in the discount rate and in the price of refinancing loans, banks’ business model that attracts resources on the interbank market and places them in securities has shrunk. At the same time, the number of banks with an increased level of securities in assets and corporate financing increased. The quality of the portfolio indicates the increased credit risks of the respective large state banks.The drawback of the proposed method is the dependence of conclusions on official banks 'statements that do not always reflect nuisances of financial position. Within small banks, we can sometimes observe that current changes in clients' account balances affect the position in SFGB. The SFGB method can be applied to analyze trends and estimate the probability of subsequent structural changes. For each bank, one can observe the trajectory change on the map and investigate the reasons for the change in business strategy.

Abstract. The purpose of the article is to determine the monetary processes peculiarities during the crisis period of economic development and areas of monetary policy adjustment. The article presents the results of the main monetary indicators of Ukraine for 2005—2020 dynamics regularities assessment. The cyclic nature of their dynamics with a cycle length of 12 months, an increase cyclical swing range and the level of «white noise» of random deviations from the cyclic dynamics line are confirmed. Entropy and entropy production are calculated for the main monetary indicators. The hypothesis that the monetary indicators dynamics uncertainty is determined by the uncertainty of the «white noise» of their random deviations from the lines of cyclic dynamics based on the calculation of the entropies of the dynamics and the dynamics of entropies is put forward and confirmed. Three groups of monetary indicators are formed according to the level of uncertainty in them. According to the research results, the monetary sphere of Ukraine dissipation was stated during the whole period of 2005—2020 with increase of its rates in time intervals, which preceded the crisis phenomena aggravation (07.2007—07.2008, 03.2013—01.2014, 01.2020—06.2020). The sources of uncertainty in the monetary sphere are identified: the amount of cash due to «cash — M0» with 5.4 months lag and the amount of cash due to «cash — M3» with 7.8 months lag. Based on the results of relations between monetary indicators analysis, the directions of monetary policy for overcoming the crisis phenomena in the monetary sphere of Ukraine are proposed. In particular, it is appropriate to change the restrictive monetary policy to an expansionary one, taking into account the lag of action in the relations between entropies / entropies production of the main monetary indicators.The obtained results can be of practical importance in the system of state regulation to stimulate monetary circulation not only in crisis but also in post-crisis periods, ensuring key monetary indicators long-term stability and monetary policy effectiveness improving. Keywords: monetary indicators, monetary aggregates, uncertainty, entropy, entropy production, dissipation, regulation, monetary policy. JEL Classification E50, E51, E52 Formulas: 1; fig.: 3; tabl.: 2; bibl.: 25.

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A method of identifying banks’ business models and studying the features of their risk profile, considering the system of indicators featuring the structure of assets, liabilities, income, expenses, and other qualitative indicators based on monthly statistical reporting. Kohonen's self-organizing maps (SOM) are used to process large data sets, revealing objects’ hidden features by forming homogeneous groups according to similar values of a large system of indicators. The choice of the system of indicators that play the most significant role in describing the business models of modern banks is substantiated. The proposed method makes it possible to group banks with homogeneous characteristics into so-called structural-functional groups and studies the change in the characteristics of groups of banks over time to compare their behavior during periods of active development of the system and during a crisis. That approach is useful for studying the banking system at the macro level, as it provides a quantitative measure of its financial stability. The more banks are in groups with negative values of parameters, increased risks, and unprofitable performance, the worse the general state of the system. The method also allows studying the features of each structural and functional group and the business models’ features at the meso-level. The number and composition of banks inherent in any group change dynamically, which characterizes the features of the relevant business model in a particular period. The averages of each group reflect the objective changes in the banking system structure. In addition, the SOM trajectory can be built for each individual bank determining the development of its strategy, features of a particular business model, and risk profile. At the micro-level, it allows comparing the features of individual banks within the SFGB and models ways to improve efficiency and financial stability by forecast values for SOM. An extensive system of indicators used to form structural and functional groups of banks allows to quickly respond to changes in the banking system, identify areas of increased risk and explore the adequacy and effectiveness of banks’ business models.