Transforming Tax Compliance with Machine Learning: Reducing Fraud and Enhancing Revenue Collection

Abstract

The integration of machine learning (ML) in tax administration has the potential to revolutionize tax compliance, enhancing fraud detection and optimizing revenue collection. This literature review explores the application of ML in tax systems, emphasizing its transformative role in addressing the limitations of traditional, labor-intensive compliance methods. Justification for adopting a literature review approach is rooted in the need to consolidate diverse perspectives, address research gaps, and provide an informed synthesis of existing findings. The study highlights the criteria used for selecting case studies and research papers, ensuring a robust analysis of ML’s ability to automate detection processes, improve risk assessment, and enable predictive analytics for efficient tax administration. Despite its potential, ML adoption is challenged by data quality issues, privacy concerns, technical infrastructure demands, and ethical considerations, which must be systematically addressed. This paper also identifies literature gaps, particularly the lack of balanced discourse, and provides recommendations for overcoming barriers, including enhancing data management practices, adopting ethical frameworks, and fostering cross-border collaboration. By addressing these challenges, ML can equip tax authorities with tools for creating efficient, adaptive, and fair systems. This research underscores ML’s growing importance in transforming global tax compliance, setting the stage for a future of more responsive and effective revenue administration.