Oleksii Zakharkin

The effectiveness of anti-money laundering systems is vital for national economic resilience, especially in transitional economies facing wartime challenges, such as Ukraine. This study aims to identify key managerial determinants of the effectiveness of Ukraine’s anti-money laundering and counter-terrorist financing (AML/CFT) system and to develop evidence-based recommendations for improving its performance. Based on data from Ukrainian national institutions for the period 2011–2023, the study employs principal component analysis and multiple linear regression to evaluate 44 statistical indicators related to institutional workload, procedural efficiency, and inter-agency coordination. The findings reveal that a small set of indicators, including the volume of suspicious transaction reports from non-banking institutions, the number of dossiers compiled, and the backlog of unresolved judicial cases, explain over 70% of the system’s output variance. The final model exhibits high explanatory power (R² = 0.963), underscoring the importance of prioritizing high-impact operational metrics. The study concludes that targeted procedural reforms and enhanced coordination between institutions can significantly strengthen AML/CFT outcomes in fragile and reforming contexts.

Acknowledgment
This study 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
Transitional banking systems increasingly rely on both digitalization and corporate social responsibility (CSR) to sustain resilience and public trust. This study evaluates whether the integration of digital capabilities and CSR relates to bank-level trust and reputation in Ukraine and Kazakhstan. The study assembled 2019–2023 indicators for four banks – PrivatBank, Monobank (Ukraine), Kaspi.kz, and Halyk Bank (Kazakhstan) – based on publicly available information. All measures were standardized, and Principal Component Analysis (PCA) was used to construct a Digital-CSR Index (DCSI). The first principal component (PC1), which jointly loads on digital innovation, CSR, and sustainability variables, explains 54.5% of total variance and serves as the composite index; document coding achieved Krippendorff’s α = 0.82. Results show a clear rank order: Kaspi.kz = 4.48, PrivatBank = 0.88, Monobank = −0.27, and Halyk Bank = −4.07. Higher DCSI values are associated with stronger outcomes in customer trust and reputation across cases (e.g., trust levels up to 8.5/10), and descriptive fits suggest that the integrated index captures more cross-sectional variation in these outcomes than single-domain proxies. Robustness checks excluding one indicator at a time and restricting Ukrainian observations to the post-2022 period preserve the loading structure and rank order. The study concludes that integration, rather than parallel pursuit, of digitalization and CSR is associated with superior legitimacy outcomes in transitional contexts. Country conditions shape this relationship: Ukraine’s crisis-driven digital expansion is tempered by disclosure volatility, whereas Kazakhstan’s steadier assurance environment favors platformized integration. The DCSI provides a transparent, replicable benchmark to guide managerial strategy and regulatory design in comparable financial systems.

Type of the article: Research Article

Abstract
Artificial intelligence is shaping digital governance, with global organizations emphasizing its opportunities and risks for public administration. The study aims to assess whether advancements in AI, measured by the AI Vibrancy Score (AIVS) and the Government AI Readiness Index (GAIRI), drive improvements in the E-Government Development Index (EGDI). Using panel data methods, the analysis draws on data from 36 countries for 2018–2022 (AIVS–EGDI) and 170 countries for 2020–2024 (GAIRI–EGDI), due to differing data availability and indicator coverage periods, applying fixed effects, random effects, and Mundlak specifications, combined with robust inference techniques. The results demonstrate that within-country improvements in AI readiness are positively and robustly associated with higher levels of e-government development, with the FE estimate for the Government AI Readiness Index equal to 0.17 (p < 0.001). RE models reveal stronger cross-country correlations, with coefficients of 2.55 (p < 0.001) for the AI Vibrancy Score and 0.35 (p < 0.001) for AI readiness. However, Mundlak (correlated RE) specifications indicate that the between-country components are statistically insignificant. Yet, the within-country effects remain significant, suggesting that dynamic national reforms and policy-driven progress outweigh inherited structural advantages. Time effects are pronounced, with positive and significant shifts in 2020 (+7.02) and 2022 (+8.10) relative to the baseline year, reflecting the acceleration of digital public administration during the post-pandemic period. Country-specific effects exhibit substantial heterogeneity, ranging from strongly positive deviations (e.g., Denmark, Estonia, Korea) to persistently negative ones (e.g., India, South Africa), underscoring the uneven national trajectories. Robustness checks using clustered standard errors confirm the stability of all key coefficients.

Acknowledgment
This paper was prepared based on the results of a study funded by the Ministry of Education and Science of Ukraine entitled “Digitalization of the public-private partnership system as a driver of the state’s economic security in the war and post-war periods” (registration number: 0126U000543).