Abstract This research investigates the application of artificial intelligence (AI) in optimizing transfer pricing (TP) dispute resolution, with a focus on India and comparative insights from the Global South, under the Income Tax Act, 1961, and Organisation for Economic Co-operation and Development (OECD) Base Erosion and Profit Shifting (BEPS) guidelines. The central question is: How can AI-driven analytics enhance TP dispute resolution efficiency and fairness, and what ethical safeguards are needed to address biases and privacy concerns? Utilizing a mixed-methods approach, the study combines legal analysis of Sections 92–92F, case studies of landmark disputes (e.g., GlaxoSmithKline, Vodafone), and advanced AI techniques such as machine learning regression models for litigation outcome prediction and blockchain for transparent pricing audits. Secondary data from OECD reports, Indian tax statistics (2020–2025), and emerging AI literature in Brazil and South Africa inform the analysis. Quantitative findings reveal a 10–15% annual increase in TP disputes, with Mutual Agreement Procedures (MAPs) reducing resolution times by 30%, while AI simulations achieve 85–90% accuracy in predicting audit outcomes and a 20–30% reduction in compliance costs through automated arm’s-length pricing. The study identifies opportunities in AI-powered analytics for Assessing Officers and multinational enterprises (MNEs), alongside ethical challenges including algorithmic bias (e.g., sector-specific disparities mitigated by diverse training datasets) and data privacy risks under India’s Digital Personal Data Protection Act, 2023, and similar global frameworks. Comparative analysis highlights tailored AI adoption strategies across the Global South, addressing local legal nuances. This research’s originality lies in its integration of machine learning, blockchain, and ethical governance in TP analytics for emerging markets, filling a gap in literature dominated by Western contexts. Its significance offers policymakers actionable guidelines for AI deployment, MNEs compliant strategies, and a model for transparent tax administration, fostering economic growth and equity in India and beyond.
Mamta Bhawanishanker Khandelwal (Wed,) studied this question.