Tax forecasting tools in the system of local budgets based on big data

Abstract

The relevance of this study lies in the growing role of digital technologies and Big Data analytical tools in shaping a modern public finance system. The conditions of decentralization and constant fluctuations in the tax base require local authorities to transition from traditional forecasting methods to intellectual and analytical systems capable of ensuring the reliability, efficiency and transparency of financial decisions. The purpose of this article is to scientifically substantiate the application of Big Data technologies to enhance the accuracy, adaptability, and analytical capacity of tax forecasting systems in Ukraine's local budgets. The research methodology is based on systematic, comparative, economic, and analytical approaches. Methods of structural and functional analysis, generalization of practical experience from Ukrainian communities, international budget planning models, and elements of analytical modeling were applied to assess the integration of Big Data into the forecasting of tax revenues. The study’s results aim to identify the primary organizational, technical, and regulatory barriers to implementing Big Data analytical tools in local finance practice. It has been proven that the lack of unified standards for formatting and data exchange, the shortage of analytical specialists, and weak coordination between tax and treasury structures significantly limit local governments' analytical capacity.. It is emphasized that integrating Big Data analytical platforms lays the groundwork for improving forecasting accuracy, timely identification of tax risks, and prompt responses to changes in the economic environment. However, assessments of the effectiveness of such solutions require further empirical substantiation. Conclusions. The development of an adaptive analytical system for tax forecasting is a strategic direction to enhance local communities' financial autonomy and reduce forecast errors. The proposed recommendations include: the formation of a unified economic and information circuit for data collection and processing, the introduction of intermunicipal coordination of forecast parameters, the deployment of public analytical dashboards to increase transparency, the creation of mechanisms for verifying forecasts, the normalization of forecasting standards and data exchange at the level of state financial policy. The implementation of such steps will allow transforming the forecasting process into a continuous revenue management cycle focused on performance and openness. Prospects for further research include developing economic and mathematical models to assess the impact of Big Data on the effectiveness of budget forecasting, determining the socio-economic effects of digitalizing finances, and exploring the concept of an «intelligent community budget» to enhance the sustainability of local economies.