The growing deployment of artificial intelligence (AI) in banking raises critical questions about whether national-level readiness, defined by ethics, institutions, infrastructure, and governance (EIIG), translates into measurable financial performance gains. This article provides empirical evidence on the link between government AI readiness and banking sector profitability. Using an unbalanced panel dataset of 136 countries covering 2020–2024, the study integrates Return on Assets (ROA) from the IMF with the Government AI Readiness Index from Oxford Insights, which embeds EIIG dimensions of ethical frameworks, institutional quality, infrastructural robustness, and governance capacity. Data preprocessing involved applying Yeo–Johnson transformations to address non-normal distributions, and panel econometric models were estimated using both fixed and random effects, with the Hausman test guiding model selection. The results indicate that stronger national EIIG readiness has a significant positive impact on bank profitability. The fixed effects model indicates that a one-unit increase in the transformed AI readiness index is associated with a 0.524 increase in transformed ROA (p < 0.001). In contrast, the random effects specification produced a negative coefficient (β = –0.126, p < 0.01). The Hausman test (χ² = 42.98, p < 0.001) confirmed fixed effects as the consistent estimator. Robust covariance estimators (clustered by country, clustered by year, and Driscoll–Kraay) further confirmed the stability of the coefficients, which remained consistently positive and significant. Country-specific fixed effects highlight structural heterogeneity: advanced economies such as Germany (α = –4.14) and the United Kingdom (α = –4.15) exhibit structurally lower profitability, while emerging economies, including Malawi (α = +0.72), Ghana (α = –0.59), and Mozambique (α = –0.24) align more closely with or exceed global averages. These findings underscore that ethical safeguards, institutional capacities, digital infrastructures, and governance mechanisms are not peripheral but central in enabling AI readiness to deliver sustainable financial performance.

The financial stability of banks remains a critical component of macroeconomic resilience, particularly in the face of ongoing geopolitical uncertainties and post-pandemic recovery. In this context, there is an urgent need for data-driven models capable of accurately assessing institutional soundness. This study aims to develop and empirically validate an econometric model to evaluate the financial stability of JSC “Oschadbank” (State Savings Bank of Ukraine) by analysing the impact of key regulatory indicators. The methodological approach involved descriptive statistical analysis, normality testing, Box–Cox transformations, and multiple regression modelling using annual data from 2018 to 2024. The model incorporated four financial ratios (Autonomy Ratio (N6), Own Funds Adequacy Ratio (N8), Equity Manoeuvrability Ratio (N9), Loan Loss Provision Coverage Ratio (N12)), with transformations applied to ensure statistical robustness. Key diagnostic tests – multicollinearity (VIF), heteroskedasticity (Breusch–Pagan), and residual analysis – were conducted, and robust standard errors were applied due to kurtosis-related violations of normality. The final regression model, based on three explanatory variables (N6, N8, and transformed N9), explains approximately 45% of the variation in the financial stability ratio (R² = 0.4489; Adj. R² = 0.3801; F(3,24) = 6.52; p < 0.01). All coefficients were statistically significant, with N9 exhibiting the strongest positive effect (β = 0.00327; p = 0.019), while H6 had a negative effect (β = –0.00046; p = 0.001), and N8 a moderate positive effect (β = 0.000044; p = 0.000). These results confirm that financial stability is sensitive to internal capital structure dynamics, providing a foundation for regulatory optimisation and strategic bank management.

The study investigates the influence of foreign direct investment (FDI) on the sustainable development of OECD countries by applying a comprehensive quantitative methodology. Statistical data from nine countries with different levels of achievement in the SDGs Index between 2000 and 2022 were analyzed. 17 indicators corresponding to the SDGs are utilized: one indicator for each SDG. Three countries with the highest SDG Index scores (Finland, Denmark, and Sweden), three with average scores (Belgium, Japan, and Portugal), and three with the lowest scores (Turkey, Mexico, and Colombia) have been included in the analysis. It ensures a comprehensive view of countries at different levels of sustainable development. Descriptive statistics were employed to assess the distribution and variation of sustainability indicators, while correlation analysis revealed the strength and direction of linear relationships between FDI inflows and key economic, social, and environmental variables. Pairwise regression models were used to estimate the significance and explanatory power of FDI in relation to sustainable development indicators. The correlation analysis has shown that there is no relationship between FDI and indicators of sustainable development in Finland, Denmark and Sweden, as the correlation coefficients are insignificant. The FDI has an impact on various sustainable development indicators of only 4 countries out of 9 selected: 2 countries with the average SDGs Index results (Belgium and Japan), the other ones are the countries with the lowest SDGs Index achievement (Turkey and Colombia). FDI has a significant impact on almost all of the selected sustainable development indicators only in Colombia. Although, FDI has a substantial and important role in achieving sustainable development and SDGs, the sample’s groups show that the majority of the explanatory factors are statistically insignificant for some countries. Forecasting techniques further highlighted potential long-term trends, confirming that FDI can act as a catalyst for sustainable development only under favorable institutional and policy frameworks.

Government interventions has long been a question of great interest in a wide range of fields. Scholars have been debating the scope and degree of intervention in the banking sphere that considers government capabilities. This study set out to provide some empirical evidence on the intertwined relationship between government interventions carried out through banking regulations and trust in the European Central Bank (ECB), taking into account the mediating role of financial system stability. A combination of quantitative approaches was used in the data analysis. The confirmatory factor analysis in STATISTICA was applied for hypothesis development, followed by the structural equation modeling (SEM) based on the statistical package SEPATH used for research hypothesis testing. This study aims to contribute to this growing area of research by exploring that financial system stability mediates the path between banking regulation and trust in central banking. It was found that stricter government regulatory and supervisory interventions in the banking sphere are changing the imprudent financial institutions’ behavior, however, negatively accomplishing financial development financial markets and institutions. Meanwhile, both financial system stability and banking regulations contribute to trust in the ECB. The research findings add to the growing body of research that indicates that stricter government regulatory and supervisory interventions in the banking sphere drive trust violations in central banking upon the causal chain by virtue of financial development financial markets and institutions deterioration.

The main hypothesis of the paper was the thesis that banking regulation is intended to minimize the probability of financial instability, including banking crises, which have long-lasting and destructive consequences for the economy.The practical aim of this investigation is to explore the impact of banking regulation instruments on the banking crisis probability. Despite a large and growing body of literature that has investigated the role of banking regulation in ensuring financial stability, only a few of them explored the aspect of this problem we are considering, and this constitutes the scientific novelty of the research.The results confirm the effectiveness of banking regulation in predicting periods of stability in banking systems.Based on the use of bibliometric analysis with the software tool VOSviewer v.1.6.10, the main patterns in the theory of banking regulation development have been identified. To conduct an empirical analysis, the author used a database of eleven European countries from 1998 to 2017, whose banking systems had manifestations of a systemic banking crisis. Binary modeling (logit model) was used as a scientific and methodological tool for statistical research. The conducted empirical analysis declared the need to tighten banking regulations in the field of non-performing loan control since it leads to an increase in the banking crisis probability. The results of binary modeling also emphasized the importance of macroeconomic and monetary factors, the neglect of which leads to the vulnerability of banking institutions and, consequently, to banking crises.An important conclusion of the analysis is that in order to minimize systemic banking crises, it is necessary to ensure the achievement of the target parameters of the main macroeconomic indicators, expressed in terms of the optimal level of inflation and annual GDP growth. The proposed binary model can be used to further study the causes of a banking crisis, as well as methodological and empirical clarification of the role of banking regulation in the probability of its occurrence.