AI Surge: Intrinsically Transfiguring Tax Evasion

Suhaib B. Bani Kinana () and Omar Bani Kinana ()
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Suhaib B. Bani Kinana: Hashemite University
Omar Bani Kinana: University of East London

A chapter in Innovative Law and Business in the Digital Era, 2025, pp 169-177 from Springer

Abstract: Abstract The intersection of artificial intelligence and tax evasion became a focal point of interest. Analyzing the implications, challenges, and opportunities that artificial intelligence brought forth is considered an important breakthrough, which can provide valuable insights into the evolving landscape of tax compliance and enforcement. As AI technology revolutionized the way data is processed, providing unparalleled capabilities to identify patterns and anomalies within vast sets of data, it emerged as the touchstone, which, once developed, can be easily applied to the realm of tax evasion, holding promise for the development of detection mechanisms, uncovering intricate schemes, and minimizing false positives, thereby bolstering the tax enforcement effectiveness. AI technology can also handle the complex and multifaceted challenges, characterized by tax evasion’s dynamic nature. Being powerful tools that keep pace with the evolving tactics utilized by non-compliant entities, AI advanced approaches can combat tax evasion, introducing a paradigm shift that enables authorities to adapt to the perplexity and burstiness inherent in taxation schemes. Leveraging AI for enhanced detection and harnessing the power of AI-driven algorithms can support companies towards sifting through massive volumes of financial data, identifying irregularities and potential instances with unprecedented speed. The AI technology’s ability to recognize nuanced patterns and correlations empowers tax agencies to conduct targeted investigations, thereby amplifying their capacity to tackle tax evasion while minimizing false accusations.

Keywords: Artificial intelligence; Tax evasion; Tax compliance; AI integration in taxation practices; Tax fraud detection techniques (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-981-96-5773-5_17

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