The introduction of Intelligent Tax Systems (ITSs), driven by Artificial Intelligence (AI) and Machine Learning (ML), is transforming tax administration by automating routine tasks, enhancing audit processes, and improving overall revenue collection efficiency. Traditional tax audits, reliant on manual data checks and human intervention, are slow and prone to errors. ITS can analyze vast amounts of tax data in real time, detect discrepancies, predict potential fraud, and automate compliance processes. These advancements significantly improve the speed, accuracy, and transparency of tax audits. Additionally, ITS can optimize tax revenue collection by identifying high-risk cases and allocating resources more efficiently. Despite its many benefits, the implementation of ITS comes with challenges, such as data privacy concerns, system integration issues, and regulatory compliance. This paper explores the potential of Intelligent Tax Systems in automating tax audits and improving revenue efficiency, examines the current state of their application, and discusses the challenges faced during implementation. The paper highlights the future prospects of ITS and its role in modernizing tax administration.
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