Istituto di Economia Internazionale - Artificial Intelligence and Transfer Pricing: A Multilayer Network Model for Compliance and Risk Mitigation

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Corresponding Author:
Roberto Moro-Visconti, Department of Economics and Business Management Sciences, Università Cattolica del Sacro Cuore, Milan - Italy

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

November 13, 2025
JEL classification: C63; F23; H25; K34; L86
Keywords: Intercompany Transactions; Algorithmic Tax Compliance; Predictive Simulation; OECD Profit Shifting; Automated Benchmarking; Audit Exposure

Abstract

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.

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