Artificial Intelligence in Tax Administration: Opportunities, Challenges, and Future Prospects

Abstract

Artificial Intelligence (AI) is reshaping public administration globally, with tax administration emerging as one of the most data-intensive and transformation-ready sectors. This paper critically examines the role of AI in tax administration, focusing on its opportunities, challenges, and future prospects. AI technologies such as machine learning, natural language processing, robotic process automation, and predictive analytics are increasingly being deployed to improve tax compliance, enhance fraud detection, streamline administrative processes, and optimize revenue collection. These technologies enable tax authorities to shift from traditional rule-based enforcement to data-driven, predictive governance models. However, despite its transformative potential, AI adoption in tax systems introduces significant challenges, including algorithmic bias, data privacy concerns, lack of transparency, cybersecurity risks, institutional resistance, and unequal technological capacity between developed and developing economies. This study employs a qualitative research methodology based on secondary data analysis from institutional reports, academic literature, and policy frameworks. The findings suggest that AI significantly improves efficiency, accuracy, and compliance in tax administration, but its success depends on robust governance structures, ethical safeguards, and institutional readiness. The paper concludes that AI will become central to future tax systems, but its implementation must be carefully balanced to ensure fairness, accountability, transparency, and public trust in digital tax governance.