AI-Powered Transfer Pricing: The Future of Tax Compliance and Regulatory Challenges

Abstract

In the digital age, artificial intelligence (AI) has become a transformative tool in numerous sectors, including taxation. One of the most challenging areas of tax compliance is transfer pricing—the rules and methods used by multinational enterprises (MNEs) to allocate income and expenses among their subsidiaries. Historically, transfer pricing has been an intricate process, often requiring extensive manual calculations and expertise. However, the advent of AI has the potential to revolutionize this domain. This paper delves into the role of AI in enhancing transfer pricing mechanisms, focusing on its implications for tax compliance and the regulatory hurdles that need to be addressed. By automating complex data analyses and identifying optimal pricing strategies, AI can streamline operations, reduce risks of non-compliance, and help in real-time tax audits. Nonetheless, the regulatory challenges surrounding AI’s integration into tax processes remain substantial, with issues such as transparency, data privacy, and the potential for tax avoidance posing significant concerns. This research explores the dual aspects of AI's contribution to transfer pricing efficiency and the hurdles in its adoption.