Artificial Intelligence and Transfer Pricing: A Multilayer Network Model for Compliance and Risk Mitigation

This paper investigates how artificial intelligence (AI), when integrated with multilayer network analysis, enhances compliance in transfer pricing (TP) for multinational enterprises (MNEs). Using a simulation-based model aligned with OECD 2022 Guidelines, we assess whether AI improves pricing alignment, reduces audit risk, and mitigates profit allocation deviations across jurisdictions. The model structures intercompany transactions across goods, services, and intangibles into distinct network layers, highlighting how AI-enabled diagnostics affect compliance outcomes. Results suggest that AI substantially improves compliance accuracy, especially in entities with high network centrality. This framework offers policymakers and tax professionals a scalable, regulation-aligned approach for real-time benchmarking and risk monitoring in an increasingly digital tax environment. Furthermore, the findings offer actionable insights for international policymakers aiming to design adaptive tax governance frameworks that incorporate algorithmic oversight and digital audit tools in response to evolving cross-border economic activity.