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Emerging Trends in Tax Fraud Detection Using Artificial Intelligence-Based Technologies

Author

Listed:
  • James Alm

    (Tulane University)

  • Rida Belahouaoui

    (Cadi Ayyad University)

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, we examine the integration of AI in tax fraud detection. 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, and the implications offer a roadmap for global tax authorities to utilize AI in bolstering detection efforts, potentially lowering compliance expenses and improving regulatory frameworks.

Suggested Citation

  • James Alm & Rida Belahouaoui, 2025. "Emerging Trends in Tax Fraud Detection Using Artificial Intelligence-Based Technologies," Working Papers 2511, Tulane University, Department of Economics.
    • Handle: RePEc:tul:wpaper:2511
    as

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    File URL: http://repec.tulane.edu/RePEc/pdf/tul2511.pdf
    File Function: First Version, November 2025
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    References listed on IDEAS

    as
    1. James Alm, 2021. "Tax evasion, technology, and inequality," Economics of Governance, Springer, vol. 22(4), pages 321-343, December.
    2. Annette Alstadsaeter & Sarah Godar & Panayiotis Nicolaides & Gabriel Zucman, 2023. "Global Tax Evasion Report 2024," Working Papers halshs-04563948, HAL.
    3. 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.
    4. Francisco J. Delgado & Elena Fernández-Rodríguez & Roberto García-Fernández & Manuel Landajo & Antonio Martínez-Arias, 2023. "Tax avoidance and earnings management: a neural network approach for the largest European economies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    5. Rida Belahouaoui & El Houssain Attak, 2024. "Digital taxation, artificial intelligence and Tax Administration 3.0: improving tax compliance behavior – a systematic literature review using textometry (2016–2023)," Accounting Research Journal, Emerald Group Publishing Limited, vol. 37(2), pages 172-191, March.
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    Cited by:

    1. David R. Agrawal & William F. Fox, 2026. "The Smarter State? Artificial Intelligence and Modern State and Local Public Finance," CESifo Working Paper Series 12716, CESifo.
    2. 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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    Keywords

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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • H26 - Public Economics - - Taxation, Subsidies, and Revenue - - - Tax Evasion and Avoidance

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