Addressing global disparities in Anti-Money Laundering and Counter-Terrorist Financing (AML/CFT) compliance and effectiveness is increasingly critical due to escalating financial crime risks. This study aims to identify natural clusters of countries based on their performance in FATF technical compliance and effectiveness assessments, thereby facilitating tailored AML/CFT support strategies. The study utilised hierarchical clustering, Principal Component Analysis (PCA), and ANOVA tests, employing FATF assessment ratings data for Immediate Outcomes (IO1-IO11) and Recommendations (R.1-R.40). Four distinct clusters were identified, highlighting significant variations in AML/CFT compliance and effectiveness. Advanced economies demonstrated high compliance and effectiveness, emphasising the strategic use of technology, cybersecurity, and effective regulatory oversight. Developing and transitional countries exhibited mixed or low performance, reflecting institutional, socio-economic, and governance-related challenges, including weaker institutional frameworks, higher corruption rates, and socio-economic pressures driving financial crime. The research also underscores persistent global challenges in adapting to new technologies and adequately supervising non-financial sectors. These clusters underline the necessity of differentiated, context-specific AML/CFT strategies, emphasising targeted interventions, technology integration, ethical frameworks, and regional cooperation to enhance global financial integrity. Additionally, these findings differ from the FATF’s traditional grouping approach, which typically classifies countries primarily based on risk assessments and geopolitical factors rather than performance-based data analysis.

The rapid development of digital technologies and their active implementation in all spheres of the economy, on the one hand, contribute to increased control over the activities of economic entities, and on the other hand, create new challenges associated with the dynamic development of cybercrime. The purpose of the article is to analyze the global trends in cybercrime in 2016-2023 (by calculating national levels of cybercrime) and to model the mechanisms of combating it in order to form a global, open and safe cyberspace, protect the population from cyber threats and cyber fraud, increase the effectiveness of financial monitoring procedures and control over transactions in cyberspace. The article presents the dominant directions, evolutionary, temporal and spatial patterns of the development of crime in cyberspace, clustering of the countries,and identification of leaders and outsiders in the field of cybercrime (through visualization density maps based on the construction of self-organized Kohonen maps). The results of the calculations confirm a significant increase in the level of cybercrime in the world since 2016 (in particular, due to the COVID-19 pandemic and active digital transformation). A comparative analysis of the indicator across countries made it possible to distinguish those with the highest rates of cybercrime (Slovenia, Iceland, Moldova, Georgia) and those with a significantly higher level of cyber security (Greece, Belgium, France, Germany).

The unfolding of the COVID-19 pandemic has revealed "bottlenecks" not only in the healthcare system, which was unable to cope with a significant influx of patients and quickly eliminate the spread of coronavirus infection, but also the vulnerability of the socioeconomic systems of countries all over the world. The research aims to determine country-specific trend patterns of volatility of the integral level of macroeconomic stability and its components and forecast their values for the medium term to determine the dynamics of post-pandemic recovery. The implementation of the research objectives involves the implementation of the following steps: 1) determining outliers in data series that characterise the dynamics of the components of macroeconomic stability in the context of each of the 10 studied countries; 2) eliminating outliers; 3) determining the highest-quality functional form of the dependence of the change in the corresponding individual macroeconomic indicator over time; 4) forecasting the change in individual indicators and the integral level of macroeconomic stability for the medium term (2023-2025); 5) determining the deviations of the forecast values of the above indicators from their pre-pandemic level (2019) and end-of-pandemic level (2022); 6) qualitative interpretation of the forecasting results.

Artificial intelligence is expanding rapidly, intensifying policy concerns that more vibrant AI ecosystems may displace workers and increase unemployment. This study aims to test whether national AI vibrancy is associated with higher unemployment across education groups (advanced, intermediate and basic). Using an unbalanced panel of 34–35 countries from 2017 to 2023, the analysis combines Stanford’s AI Vibrancy Score with World Bank indicators and estimates two-way fixed-and random-effects models, employing Box–Cox/log transformations and dependence-robust inference (including country/time clustering and Driscoll–Kraay standard errors). The results provide little support for the displacement hypothesis. For advanced-education unemployment, AI vibrancy is statistically insignificant in the two-way FE model. It remains insignificant under all robust corrections (ln(AI vibrancy): β=−0.099, country-clustered p=0.494, time-clustered p=0.544, Driscoll–Kraay p=0.468). For basic-education unemployment, AI vibrancy is likewise insignificant in the two-way FE model (p=0.782). It remains insignificant under country clustering (p=0.830), time clustering (p=0.813) and Driscoll–Kraay inference (p=0.819). For intermediate-education unemployment, the AI coefficient remains insignificant under country clustering (p=0.273), time clustering (p=0.310), and Driscoll–Kraay corrections (p=0.226), indicating no robust unemployment-increasing effect across education groups during the observed period.