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Tax Fraud Detection Using Artificial Intelligence-Based Technologies: Trends and Implications

Author

Listed:
  • Rida Belahouaoui

    (LAMIGEP Laboratory, Moroccan School of Engineering Sciences (EMSI), Marrakech 40000, Morocco)

  • James Alm

    (Department of Economics, Tulane University, New Orleans, LA 70118, USA)

Abstract

This study examines the role of artificial intelligence (AI) tools in enhancing tax fraud detection within the ambit of the OECD Tax Administration 3.0, focusing on how these technologies streamline the detection process through a new “Adaptive AI Tax Oversight” (AATO) framework. Through a textometric systematic review covering the period from 2014 to 2024, the integration of AI in tax fraud detection is explored. The methodology emphasizes the evaluation of AI’s predictive, analytical, and procedural benefits in identifying and combating tax fraud. The research underscores AI’s significant impact on increasing detection accuracy, predictive capabilities, and operational efficiency in tax administrations. Key findings reveal the ways by which the development and application of the AATO framework improves the tax fraud detection process. The implications highlight not only the governance benefits and ethical challenges that arise, but also provide practical guidance for tax authorities worldwide in leveraging AI to reduce compliance costs and strengthen regulatory frameworks. Finally, the study offers recommendations for future research, particularly in refining AI methodologies, differentiating policy implications across high-income and low- and middle-income countries, and addressing governance and ethical issues to ensure equitable and sustainable tax administration practices.

Suggested Citation

  • Rida Belahouaoui & James Alm, 2025. "Tax Fraud Detection Using Artificial Intelligence-Based Technologies: Trends and Implications," JRFM, MDPI, vol. 18(9), pages 1-25, September.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:9:p:502-:d:1746793
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    References listed on IDEAS

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    1. Alex Cobham & Petr Janský, 2018. "Global distribution of revenue loss from corporate tax avoidance: re†estimation and country results," Journal of International Development, John Wiley & Sons, Ltd., vol. 30(2), pages 206-232, March.
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    Cited by:

    1. Anis EL AMMARI, 2025. "Measuring the impact of digitalization on the effects of corruption leading to tax evasion," Journal of Academic Finance, RED research unit, university of Gabes, Tunisia, vol. 16(3), pages 66-89, December.

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