Optimizing the distribution of inter-budget transfers using digital analysis and artificial intelligence tools

the article is devoted to the study of the problems of insufficient targeting and transparency in the distribution of interbudgetary transfers in Russia. In the context of growing interregional disparities, the relevance of the topic is determined by the need to transition to digital and intelligent analytical methods that ensure a more justified and equitable allocation of budgetary support. Materials and Methods. The research materials included regulatory and legal acts governing interbudgetary relations in the Russian Federation, as well as official statistical data from the Ministry of Finance, Rosstat, and the Federal Tax Service for 2022-2025. The study employed methods of system and comparative analysis, economic and mathematical modeling, and scenario-based approaches, along with tools of digital analytics and explainable artificial intelligence. Results. The author examined the current state of the interbudgetary transfer system in the Russian Federation and identified key issues in its functioning. Based on statistical data, it was established that in 2023, the share of interbudgetary transfers in regional revenues decreased by 11%, while own tax and non-tax revenues grew by 16%; the volume of grants in 2024 declined by approximately 14%. As a solution, the use of digital analytics and artificial intelligence tools was proposed to develop predictive and optimization models for transfer distribution, ensuring transparency, accuracy, and stability of the interbudgetary system. Conclusion. The application of digital analytics and artificial intelligence in the distribution of interbudgetary transfers increases the accuracy and transparency of financial decisions, thereby improving equalization mechanisms and strengthening the stability of regional budgets.

interbudgetary transfers, digital analytics, artificial intelligence, distribution optimization, fiscal stability, financial equalization