INNOVATIVE ANALYSIS METHODS FOR ACCURATE TAX FORECASTING AND OPTIMAL PLANNING OF BUSINESS ACTIVITIES

Abstract

In the contemporary economic landscape, shaped by rapid digitalization and significant challenges, particularly the ongoing martial law in Ukraine, accurate tax forecasting and effective tax planning are crucial for national financial stability and business viability. The article investigates innovative analytical methods, specifically machine learning (ML) algorithms and Big Data analytics, to enhance the precision of tax forecasting and optimize business entities' activity planning. We argue for a systemic approach that integrates the analysis of economic processes, taxpayer behavioral aspects, and the dynamic legal environment amid Ukraine's transformations and wartime conditions. The study analyzes the application of ML models for predicting tax revenues, managing tax risks, and facilitating optimal tax planning strategies. We also explore the prospects for adapting and integrating these advanced methodologies within the unique Ukrainian context, considering the specific challenges of martial law and post-crisis economic recovery. A key focus is on the intricate interplay between economic dynamics, taxpayer psychology, and the legal framework, highlighting how state regulatory influence during wartime can induce psychological strain on businesses. This underscores the urgent need for sophisticated analytical tools to navigate such complexities. The research further identifies the critical roles of judicial precedent and the organizational-legal form of enterprises as pivotal legal aspects impacting tax planning. These elements, often under-examined in conventional analyses, require refinement and integration through innovative analytical approaches to ensure more precise forecasting and effective planning. The paper advocates for a shift from traditional, often intuitive, calculations to data-driven models, powered by AI and ML. This transformation is vital for mitigating tax risks, ensuring transparency in taxpayer-state relations, and ultimately contributing to more efficient tax administration. Our findings suggest that adopting ML in tax policy forecasting and planning is a promising direction, enhancing efficiency and necessitating investments in IT infrastructure, quality data, and skilled personnel capable of integrating ML into practical tax management.