Vol. 4 No. 1 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

AI and Machine Learning in Tax Strategy: Predictive Analytics for Corporate Tax Optimization

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  • Piyushkumar Patel
Piyushkumar Patel
Accounting Consultant at Steelbro International Co., Inc, USA
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Published 29-02-2024

Keywords

  • Artificial Intelligence,
  • Machine Learning
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Piyushkumar Patel, “AI and Machine Learning in Tax Strategy: Predictive Analytics for Corporate Tax Optimization”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 1, pp. 439–457, Feb. 2024, Accessed: Dec. 28, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/229

Abstract

As businesses navigate the increasingly intricate landscape of global tax regulations, integrating artificial intelligence (AI) & machine learning (ML) in tax strategy is becoming more essential. Predictive analytics, driven by AI and ML technologies, enables companies to proactively optimize their tax positions by forecasting potential liabilities, uncovering opportunities for tax savings, and ensuring compliance with varying regulations across different jurisdictions. By analyzing vast amounts of data and identifying patterns, these technologies allow businesses to make more informed decisions, streamline tax processes, and reduce the risk of errors or missed opportunities. This transformation is significant for corporate tax departments, which traditionally relied on manual methods and reactive approaches. Today, AI and ML are reshaping these practices by automating routine tasks, providing real-time insights, and enhancing overall efficiency. Predictive analytics, for instance, can help tax departments identify trends and foresee issues such as changes in tax rates or evolving compliance requirements before they become pressing concerns. However, despite its advantages, adopting AI and ML in tax strategy comes with challenges. Predictors' accuracy depends on the quality of the data fed into these systems, which means businesses must ensure the integrity and completeness of their data. Additionally, as AI becomes more ingrained in tax strategies, data privacy and security concerns grow. Organizations must balance the power of predictive tools with protecting sensitive financial information. Moreover, the evolving role of tax professionals must also be considered, as these technologies reshape their responsibilities from manual data processing to interpreting insights and strategic planning. As AI continues to influence the corporate tax landscape, tax departments must adapt and harness the potential of these tools while being mindful of their limitations & the ongoing need for human expertise. This article delves into these aspects, offering practical insights, exploring case studies, and looking at future trends to understand how AI and ML drive the future of tax optimization.

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