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February 5, 20260 citationsOpen Access

Artificial intelligence and taxation

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MXMin Xu

Key Points

  • This examination focuses on how artificial intelligence revolutionizes interactions between taxpayers and tax authorities, enhancing efficiency.
  • Identified routine tasks automation through AI technology
  • Evaluated AI's role in data analysis for tax compliance
  • Analyzed legal and ethical implications of AI in taxation
  • AI significantly reduces time for data handling and compliance tasks
  • Tax authorities improve fraud detection using predictive analytics
  • Businesses that adopt AI gain a competitive advantage in tax calculation

Abstract

The examination of artificial intelligence and taxation is critical, as technology is fundamentally transforming the relationship between taxpayers and tax authorities, as well as compliance processes. Artificial intelligence can automate routine tasks (such as data entry and preparing returns), which can reduce errors and increase efficiency. Tax teams currently spend a significant amount of their time collecting and manipulating data, which artificial intelligence can do in a fraction of the time. Automation allows professionals to focus on higher value-added analytical and advisory tasks. Tax authorities actively use artificial intelligence for predictive analytics and network research to detect suspected tax evasion and fraud. Artificial intelligence helps analyse large amounts of financial data and identify suspicious patterns, making audits faster and more accurate. The introduction of a global minimum tax is an extremely complex and data-intensive process, for which multinational companies use artificial intelligence-based software to automate calculations and predict tax burdens. Companies that do not integrate artificial intelligence may find themselves at a significant competitive disadvantage. It is important to examine the legal and ethical framework for the use of artificial intelligence in taxation, ensuring transparency, data protection and algorithmic impartiality to avoid discriminatory bias. Examining this topic is essential for preparing future-proof tax systems and tax professionals in a rapidly changing, technology-driven world.

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Cite This Study

Min Xu (2025) studied this question.

synapsesocial.com/papers/6984349af1d9ada3c1fb2f3ehttps://doi.org/10.5281/zenodo.18454006
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