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

James Alm () and Rida Belahouaoui ()
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Rida Belahouaoui: Cadi Ayyad University

No 2511, Working Papers from Tulane University, Department of Economics

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.

Keywords: Artificial intelligence; tax fraud; AATO framework; blockchain; neural networks; data mining (search for similar items in EconPapers)
JEL-codes: C45 H26 (search for similar items in EconPapers)
Date: 2025-11
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http://repec.tulane.edu/RePEc/pdf/tul2511.pdf First Version, November 2025 (application/pdf)

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