In the system of global information space, it is important to adequately calculate and assess the factors of successful functioning of the banking system, which are directly the engines of the country's development, economic stability, especially from the standpoint of qualitative measurement of innovative technology and human capital in rapid cyber fraud. The work is devoted to studying factors that describe the components of efficiency and potential use of innovative technologies in neo-banking in 90 countries to determine the level of risk of their use for money laundering based on gravitational modeling methodology. The authors substantiated that using four factors consisting of 13 components of digital evolution is expedient. Three indicators (access infrastructure, transaction infrastructure, and fulfillment infrastructure) allowed forming the supply condition factor. It provides a score on developing digital and physical infrastructure to ensure the digital economic system's quality. The «demand conditions» factor consists of 4 indicators (human quality level, device absorption level and digital broadband level, digital payment absorption), which show how much consumers are willing and able to participate in the digital economic system and whether they have the tools and skills needed to connect to the digital economy. Three indicators (institutional effectiveness and trust, institutions and the business environment, institutions and the digital ecosystem) shaped the «institutional environment» factor. It relates to research on countries' support for digital legislation, governments' investment in digitalization, and regulations. Regulate the quality of storage and access to digital data. The fourth factor of «innovation and change» consists of three features that characterize the state of key innovative economic system inputs (talents and capital), processes (i.e., cooperation between universities and industry), and outputs (i.e., new scalable digital products and services). The generalized indicator was formed based on these indicators. It characterizes the degree of risk of using the services of neobanks of the studied countries by economic agents or individuals to legalize criminal proceeds. At the first stage of the proposed method, the authors substantiated the statistical significance and possibility of using the studied indicators. The procedure of logarithmic normalization was carried out. The toolkit of descriptive statistics of the Statgaphics Centurion package provided the normalization parameters. In the second stage, the indicators were collapsed using a geometric weighted average, which provides meaningful information about the average dynamics rate. The third stage provided calculating the value of the integrated rating assessment of the degree of risk of using innovative technologies, services, and neobanking services for money laundering based on gravity modeling methods. The findings showed that 12.22% of the studied countries had a high degree of risk, 25.56% – a medium level of risk, 25.56% – a risk below the average level, for 36.66% of countries – the risk was almost absent.

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The wide variety of schemes to use companies for money laundering, such as oil smuggling, illegal gas sales, misappropriation of the Central Bank refinancing, misappropriation of bank funds and state-owned enterprises, form the research issues. The sample under study includes 102 countries around the world, which are closely monitored by the Financial Action Task Force (FATF) and have different levels of sociopolitical and economic development. The scientific and methodological approach to assess the financial monitoring risk in terms of the use of financial institutions for money laundering is based on the methods of multidimensional static analysis, descriptive, cluster and dispersive data analysis, gravity theory, nonlinear econometric modeling, differential and bifurcation analysis of dynamic nonlinear systems. The result of the study is a developed model of comprehensive risk assessment for the countries' financial institutions for money laundering, which considers grouping of countries by the

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.

The aim is to develop an integrated indicator that characterizes the degree of satisfaction of the population with medical services. Materials and methods: integrated indicator was formed in terms of three stages for 24 regions of Ukraine and Kyiv. At the first stage, the expediency of using five influential groups of input indicators (behavioral, physical, economic, social and legal orientation) with a total of 59 features using descriptive modeling is substantiated. At the second stage, canonical correlation models were developed for the most correlated complex features, which form an indicator of the population satisfaction degree with the received medical services: physical condition features, social and behavioral orientation qualities. The third stage of factor modeling (using orthogonal transformation methods Varimax, Quartimax and Equimax) allowed identifying the five most influential factors for the formation of an integrated indicator and the development of econometric models for the healthcare state. Results: the necessity to improve the medical service quality and innovation in healthcare reform is confirmed since there were no regions where citizens were fully satisfied with the medical service level. The population of most Ukrainian regions (76% of regions), including Kyiv, is rather dissatisfied with the received medical services. Conclusions: the study results provide ample opportunities for healthcare workers, medical professionals, and public authorities to ensure quality and timely adjustment of existing rules and regulations within the Health Care Reform, improving the level of public satisfaction with the received medical services, and the immediate improvement of the nation’s health.