Money laundering risks and AML supervision in the luxury goods market: a comparative study of four jurisdictions

Journal of Money Laundering ControlJun 25, 2026

Money laundering risks and AML supervision in the luxury goods market: a comparative study of four jurisdictions

Abstract

Purpose Money laundering (ML) risks in the fast-growing luxury goods market fall largely outside the Financial Action Task Force (FATF) framework, which imposes anti-money laundering (AML) obligations on dealers in precious metals and stones but not to dealers in other high-value items. Focusing on cash transactions for portable luxury goods, this study aims to ask how four major jurisdictions – China, the EU, the UK and the USA – address these risks under domestic AML frameworks. Design/methodology/approach The study uses a theory-informed comparative legal methodology drawing on three analytical frameworks – situational crime prevention theory, regulatory theory and the theory of transnational legal order – operationalized through comparative legal analysis of primary sources and a quantitative review of FATF Mutual Evaluation (ME) data. Findings Significant inconsistencies emerge between jurisdictional approaches. All four jurisdictions formally comply with FATF requirements for precious metals and stones dealers but treat dealers of other high-value goods very differently, from comprehensive coverage to near-complete exclusion. These yield four supervisory models varying in regulatory breadth, reporting depth, enforcement feasibility and vulnerability to abuse. FATF ME ratings suggest that technical compliance does not consistently align with ML risk mitigation effectiveness. Originality/value The study addresses a research gap by examining ML risks across the luxury goods sector, beyond the well-studied precious metals and stones segment, in four jurisdictions representing the largest share of the global market. It develops an original typology illustrating how divergent approaches emerge under common FATF standards and suggests that technical compliance can obscure underlying weaknesses in sector-specific ML risk mitigation.

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