This paper aims to analyze the impact of tax digitalization, focusing on artificial intelligence (AI), machine learning and blockchain technologies, on enhancing tax compliance behavior in various contexts. It seeks to understand how these emerging digital tools influence taxpayer behaviors and compliance levels and to assess their effectiveness in reducing tax evasion and avoidance practices.
Using a systematic review technique with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method, this study evaluates 62 papers collected from the Scopus database. The papers were analyzed through textometry of titles, abstracts and keywords to identify prevailing trends and insights.
The review reveals that digitalization, particularly through AI and blockchain, significantly enhances tax compliance and operational efficiency. However, challenges persist, especially in emerging economies, regarding the adoption and integration of these technologies in tax systems. The findings indicate a global trend toward digital Tax Administration 3.0, emphasizing the importance of regulatory frameworks, capacity building and simplification for small and medium enterprises (SMEs).
The findings provide guidance for policymakers and tax administrations, underscoring the necessity of strategic planning, regulatory backing and global cooperation to effectively use digital technologies in tax compliance. Emphasizing the need for tailored support for SMEs, the study also calls for expanded research in less represented areas and specific sectors, such as SMEs and developing economies, to deepen global insights into digital tax compliance.
This study has attempted to fill the gap in the literature on the comprehensive impact of fiscal digitalization, particularly AI-based, on tax compliance across different global contexts, adding to the discourse on digital taxation.
