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

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

Economia Internazionale / International Economics, 2026, vol. 79, issue 1, 51-90

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.

Intelligenza artificiale e transfer pricing: un modello di network multistrato per la conformità e mitigazione del rischio

Questo studio esamina l’impatto dell’intelligenza artificiale (IA), integrata con l’analisi dei network multistrato, sulla conformità nei prezzi di trasferimento per le imprese multinazionali. Utilizzando un modello basato su simulazione allineato alle Linee Guida OCSE 2022, ci si chiede se l’IA migliori l’allineamento dei prezzi, riduca il rischio di audit e mitighi le deviazioni nell’allocazione dei profitti tra giurisdizioni. Il modello struttura le transazioni interaziendali riguardanti beni, servizi e beni immateriali in distinti strati di rete, evidenziando come i modelli diagnostici abilitati dall’IA influenzino i risultati di conformità. I risultati suggeriscono che l’IA migliora sostanzialmente l’accuratezza e la conformità dei confronti, soprattutto in centri direzionali centrali (hub) nell’ambito del network. Questo quadro offre un approccio scalabile, allineato alla normativa per il benchmarking in tempo reale e il monitoraggio del rischio in un ambiente fiscale sempre più digitalizzato.

Keywords: Intercompany Transactions; Algorithmic Tax Compliance; Predictive Simulation; OECD Profit Shifting; Automated Benchmarking; Audit Exposure (search for similar items in EconPapers)
JEL-codes: C63 F23 H25 K34 L86 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:ris:ecoint:022211

DOI: 10.65644/EIIE.079.01.0051

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