Oxana Kirichok

Type of the article: Research Article

Abstract
As emphasized by the FATF, IMF, and World Bank, technological readiness and institutional quality are increasingly decisive in shaping AML effectiveness. By mitigating money-laundering vulnerabilities, strengthening AI readiness, and enhancing institutional quality, tax collection efficiency can be improved and fiscal leakages reduced. These improvements expand the fiscal space available for national budgets, strengthening the financial foundations of public administration. The study aims to examine the impact of Government AI Readiness on AML risk, measured by the Basel AML Index, and the moderating role of institutional quality as captured by the Rule of Law Index. An unbalanced panel dataset that covers up to 168 countries for 2020–2024 was analyzed using fixed effects and random effects models, with variable transformations applied where necessary. All estimations were performed in R Studio. The results indicate that a one-point increase in the Government AI Readiness Index is associated with a 0.048–0.040 point reduction in the Basel AML Index, while a one-unit increase in log GDP per capita decreases the Basel AML Index by 0.54–0.34 points, holding other factors constant. The interaction term between AI readiness and the Rule of Law Index is positive (0.067–0.072), confirming that the risk-reducing effect of AI readiness diminishes as institutional quality strengthens. These findings support the hypotheses and confirm the complementary roles of technological preparedness and institutional integrity in shaping AML outcomes. Fixed effects analysis reveals structural vulnerabilities in AML in Gabon and China, while Sweden exhibits the lowest residual risk after accounting for AI readiness and institutional strength.

Acknowledgment
This article was supported by the Ministry of Education and Science of Ukraine (project No. 0123U101945 – National security of Ukraine through prevention of financial fraud and money laundering: war and post-war challenges).

Type of the article: Research Article

Abstract
Global progress toward universal access to affordable, reliable, and clean energy has stalled, with over two billion people still lacking access to clean cooking, and affordability pressures are rising. AI is emerging as an energy-intensive technology and a potential enabler of more equitable energy systems. This paper assesses whether AI vibrancy contributes to advancing energy equity across countries while accounting for differences in economic capacity. The study employs a balanced panel of 36 countries from 2017 to 2023 (252 observations), drawing on the Global AI Vibrancy Tool, World Bank Open Data, and the World Energy Council’s Energy Trilemma Index. Box–Cox transformations were applied to address skewness, and panel econometric models (fixed and random effects) with robust standard errors were estimated. The FE model shows no significant within-country effect of AI vibrancy on energy equity (R² = 0.012). The RE model indicates a positive association: a one-unit increase in the AI vibrancy score results in an improvement of 0.00165 in the energy equity index (p < 0.01). At the same time, GDP per capita exerts a strong and highly significant effect (p < 0.001). The RE model explains 12.4% of the overall variation in energy equity. After correcting for heteroscedasticity and cross-sectional dependence, GDP per capita remains significant, whereas the effect of AI vibrancy weakens to marginal significance (p ≈ 0.09). Country-specific effects further reveal systematic over- and under-performance beyond what AI vibrancy and income predict, underscoring the critical role of governance and institutional quality in shaping energy equity outcomes.

Acknowledgment
The article was prepared as a part of the MSCA4Ukraine project 06030419, European Union’s Horizon 2020 Research and Innovation Programme. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union, the European Research Executive Agency, or the MSCA4Ukraine Consortium. Neither the European Union nor the European Research Executive Agency, nor the MSCA4Ukraine Consortium as a whole, nor any individual member institutions of the MSCA4Ukraine Consortium can be held responsible for them.

Type of the article: Research Article

Abstract
Whether digital transformation in the public sector and in financial services jointly contributes to banking stability – or whether the two strands proceed along parallel trajectories – remains an open empirical question for post-socialist economies undergoing both reforms simultaneously. This study addresses the question in three components. First, a cross-country mediation analysis covers up to 130 economies over 2018–2024 (853 country-year observations), drawing on the World Bank GovTech Maturity Index, the IMF Financial Access Survey, and the IMF Financial Soundness Indicators, with panel OLS, country-clustered standard errors, and bootstrap mediation tests. Second, the results are decomposed via fixed-effect deviations for three post-Soviet economies from distinct EBRD regions: Ukraine, Armenia, and Kazakhstan. Pre-shock GovTech maturity is positively associated with digital banking adoption (β = +2.91, p = 0.017); sub-pillars differ in channel: core government systems for transaction intensity, public service delivery for account ownership. Bootstrap mediation tests do not support the indirect path through digital banking adoption (six specifications, lowest p = 0.132). GovTech maturity instead shows a substantial direct association with the non-performing-loan ratio – a 13-percentage-point reduction per unit increase in GTMI (p = 0.037) – plausibly operating through institutional infrastructure such as property registries, e-courts, and tax-credit information systems. The two strands are linked but not chained: GovTech is associated with digital banking adoption, yet the route to lower non-performing loans runs through institutional infrastructure. Country-level decomposition reveals heterogeneous GTMI trajectories and identifies reform priorities across public service delivery and core government systems.

Acknowledgment
This article was prepared based on the results of a study funded by the Ministry of Education and Science of Ukraine entitled “GovTech for Ukraine: A Digital, Secure, Transparent, and Equitable State in Times of War and Post-War Reconstruction” (registration number: 0126U000544).