Author(s)

JiaYing Chen^1*, Pan Li², Huijie Fan³ 

Affiliation(s)

¹Cornell University, Ithaca 14850, New York, USA.

²University of Hull, Hull HU6 7RX, East Riding of Yorkshire, UK. 

³Hunan Agricultural University, Changsha 410128, Hunan, China.

Corresponding Author

JiaYing Chen

ABSTRACT

Transfer pricing (TP) has become increasingly complex in the era of globalization, requiring multinational enterprises (MNEs) to establish arm's length prices for intercompany transactions across jurisdictions. Traditional transfer pricing methodologies, while established through decades of regulatory practice, face significant challenges in addressing the complexity and volume of modern cross-border transactions. The emergence of predictive analytics (PA) and machine learning (ML) techniques offers transformative potential for enhancing transfer pricing determination, documentation, and compliance. This review examines the application of predictive analytics in transfer pricing contexts, exploring how artificial intelligence (AI), big data analytics (BDA), and advanced statistical methods are reshaping both corporate tax planning strategies and regulatory enforcement mechanisms. The regulatory implications of these technological advances are profound, raising questions about data transparency, algorithmic accountability, and the evolution of arm's length principle (ALP) interpretation. This paper synthesizes current research on predictive modeling approaches including neural networks (NN), random forests (RF), gradient boosting machines (GBM), and support vector machines (SVM) applied to comparable company selection, profit allocation, and risk assessment. We examine how tax authorities worldwide are deploying similar technologies for audit selection and compliance monitoring, creating both opportunities and challenges for MNEs navigating increasingly data-driven regulatory environments. The review addresses critical implementation considerations including data quality requirements, model interpretability standards, and the alignment of predictive systems with existing legal frameworks under Organisation for Economic Co-operation and Development (OECD) guidelines and local regulations. Findings indicate that while predictive analytics significantly improves accuracy and efficiency in transfer pricing processes, successful implementation requires careful attention to regulatory acceptability, documentation standards, and cross-functional integration between tax, finance, and data science teams.

KEYWORDS

Transfer pricing; Predictive analytics; Machine learning; Tax compliance; Regulatory implications; Arm's length principle; Multinational enterprises; Artificial intelligence; OECD guidelines; BEPS

CITE THIS PAPER

JiaYing Chen, Pan Li, Huijie Fan. Predictive analytics for transfer pricing and its regulatory implications. Journal of Trends in Financial and Economics. 2025, 2(4): 49-59. DOI: https://doi.org/10.61784/jtfe3065.

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