The financial services industry is currently undergoing a major transformation, with digitization and sustainability being the core drivers. While both concepts have been researched in recent years, their intersection, often conceived as “green FinTech,” remains under-determined. Therefore, this paper contributes to this important discussion about green FinTech by, first, synthesizing the relevant literature systematically. Second, it shows the results of an empirical, in-depth analysis of the Swiss FinTech landscape both in terms of green FinTech startups as well as the services offered by the incumbents. The research results show that literature in this new domain has only emerged recently, is mostly characterized by a specific focus on isolated aspects of green FinTech and does not provide a comprehensive perspective on the topic yet. In addition, the results from the literature and the market analysis indicate that green FinTech has an impact along the whole value chain of financial services covering customer-to-customer (c2c), business-to-customer (b2c), and business-to-business (b2b) services. Today the field is predominantly captured by startup companies in contrast to the incumbents whose solutions are still rare.

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.

number of observations-4436). We used the toolkit of vector autoregressive modelling to determine the sources of Ukrainian stock index PFTS response to the US non-monetary information signals, which is based on the decomposition of changes in stock market excess return through the channels of economic transmission ("expected future dividends", "real interest rate" and "risk premium") and takes into account the unexpected values of the informational context of selected non-monetary signals. Target time series are stationary according to the KPSS and ADF criteria. The results show that four of the six selected non-monetary information signals of the USA do not have a significant effect on the response of endogenous variables of econometric model. The existence of significant direct influence of US non-monetary informational signals "Personal Spending" and "Consumer Confidence" on the response of the excess return of Ukrainian stock index PFTS has been established. It is substantiated that the actual and forecast state of the USA national economy is considered by the participants of the local stock markets, in particular in Ukraine, as one of the most important sources of macroeconomic information while making strategic and tactical investment decisions. Thus, the increasing importance of the component of "surprise" of such non-monetary information signals of the USA is considered as "positive" news for the domestic stock market by investors, which increases the excess return of the stock index PFTS.

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