Artificial Intelligence In Tax Compliance: A Systematic Literature Review
DOI:
https://doi.org/10.33751/jmp.v14i1.131Keywords:
Artificial Intelligence, Tax Compliance, Tax Administration, Machine Learning, Systematic Literature ReviewAbstract
ARTIFICIAL INTELLIGENCE IN TAX COMPLIANCE: A SYSTEMATIC LITERATURE REVIEW
This study examines the role of Artificial Intelligence (AI) in enhancing tax compliance using a Systematic Literature Review (SLR) approach. A total of 60 peer-reviewed articles published between 2021 and 2026 were systematically analyzed following PRISMA guidelines. The review focuses on key themes such as the application of AI in tax administration, fraud detection, risk assessment, and taxpayer behavior analysis. The findings indicate that AI technologies, including machine learning and data analytics, significantly improve the efficiency, accuracy, and effectiveness of tax compliance processes by enabling real-time monitoring and predictive analysis. However, the literature also highlights several challenges, including data privacy concerns, ethical issues, high implementation costs, and disparities in technological readiness across countries. This study contributes to the literature by providing a comprehensive synthesis of existing research and offers insights for policymakers and tax authorities in leveraging AI to strengthen tax compliance and modernize tax administration systems.
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