Artificial Intelligence and Corporate Tax Risk Management: A Systematic Literature Review

Abstract

This study presents a systematic literature review examining the transformative role of artificial intelligence (AI) in corporate tax risk management. As globalization, regulatory scrutiny, and digitalization increase corporate tax complexity, AI technologies including machine learning, predictive analytics, and automated compliance systems are reshaping how organizations identify, assess, and manage tax-related risks. The review synthesizes research across taxation, corporate governance, risk management, and algorithmic decision-making, exploring AI applications in enhancing tax compliance, fraud detection, tax planning, and governance frameworks. It reveals that AI offers significant potential to improve efficiency and effectiveness in tax risk management, yet successful implementation requires robust governance structures, ethical organizational cultures, and supportive regulatory environments. Critically, the study examines challenges associated with algorithmic decision-making, including transparency, fairness, accountability, and trust. It identifies persistent research gaps concerning developing economies, long-term organizational impacts, and unintended consequences of automated tax decisions. By consolidating fragmented literature streams, the paper provides a conceptual foundation for strategically integrating AI into corporate tax risk management and offers directions for future research and policy development in this evolving field.