The Impact of Artificial Intelligence on Corporate Tax Risk: A Comprehensive Literature Review

International Journal of Multidisciplinary Research and Innovation Studies (IJMRIS); Salman Farah; Lamine Farok

Authors

Salman Farah Author
Lamine Farok Author

Keywords:

Artificial Intelligence, Corporate Tax Risk, Tax Compliance, Tax Fraud Detection, Tax Automation, Machine Learning, Predictive Analytics, Algorithmic Bias

Abstract

The integration of Artificial Intelligence (AI) into corporate tax risk management has transformed traditional approaches, introducing both opportunities and challenges in tax compliance, avoidance, and fraud detection. This literature review examines the evolving role of AI in corporate tax risk, focusing on its potential to optimize tax strategies, enhance compliance, and mitigate taxation-related risks. AI technologies—including machine learning, natural language processing, and predictive analytics—have automated routine tasks, improved accuracy, and enabled real-time decision-making. AI-driven systems are particularly effective in detecting anomalies and patterns indicative of tax fraud, thereby enhancing fraud detection efficiency and reducing evasion.

AI also facilitates automated tax planning, allowing organizations to model various tax scenarios and optimize strategies while remaining within regulatory boundaries. However, the implementation of AI in tax risk management raises significant ethical, legal, and technical concerns, including data privacy, algorithmic bias, and accountability. This review synthesizes current research to identify gaps in the literature and suggests directions for future inquiry, particularly regarding the long-term implications, ethical considerations, and cross-jurisdictional deployment of AI in taxation.

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