INTERNATIONAL EXPERIENCE IN THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN PUBLIC AND FINANCIAL INSTITUTIONS
DOI:
https://doi.org/10.35774/sf2026.02.242Keywords:
AI governance; algorithmic accountability; data governance; government audit; public finance; financial monitoring; operational risk; model risk; digital finance; artificial intelligence.Abstract
Introduction. The digital transformation of public finance is increasing the role of artificial intelligence (AI) in government auditing, financial monitoring, data management, and risk forecasting. At the same time, the active implementation of AI in public and financial institutions is accompanied not only by potential economic benefits, but also by new regulatory, operational, and algorithmic risks associated with data quality, cybersecurity, algorithmic bias, false positives, and transparency of automated decision-making. This creates the need for a comprehensive scientific analysis of international AI governance models and approaches to their adaptation in Ukraine under conditions of digital transformation and geopolitical instability.
The purpose of the article is to systematize international experience in the application of AI in public and financial institutions, conduct a comparative analysis of U.S. and EU approaches to AI governance, and substantiate approaches to the development of an adaptive AI regulatory model for Ukraine’s public finance system.
Results. The study demonstrates that AI is gradually being integrated into key public finance functions, including government auditing, financial monitoring, transaction analysis, budget data management, and risk forecasting. AI implementation contributes to faster financial data processing, automation of control procedures, and optimization of selected managerial decisions. At the same time, significant differences between the U.S. innovation-oriented model and the EU regulation-oriented model based on risk-oriented governance and algorithmic accountability principles are identified.
The study substantiates that alongside the positive effects of AI implementation, new systemic risks emerge, including operational risk, model risk, algorithmic bias, and false-positive blocking of legitimate financial operations in AI-driven financial monitoring systems. The concept of algorithmic financial disruption is proposed as a separate analytical direction for studying digital finance risks associated with the potential impact of algorithmic errors on public finance stability, budgetary flows, and national financial security.
Conclusions. The study substantiates the feasibility of developing a hybrid AI governance model combining innovation capacity with regulatory oversight, algorithmic accountability, and human oversight mechanisms. For Ukraine, strategic importance is attached to harmonization with EU approaches to AI regulation and virtual assets regulation, development of digital financial monitoring systems, and protection of critical public financial flows. The results may be used in shaping public policy in the fields of digital finance, AI governance, and financial security.
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