Magic Mirror in My Hand…. How Trade Mirror Statistics Can Help Us Detect Illegal Financial Flows

Abstract

Criminals worldwide typically use misreporting tricks of different sorts to exploit the transfer of goods between different countries for money laundering purposes. The main international anti-money laundering organisations started paying attention to this phenomenon, known as trade-based money laundering, or TBML, a long time ago, but the absence of suitable analytical tools has reportedly impeded preventive action. Nonetheless, the literature has consistently shown that the analysis of discrepancies in mirrored bilateral trade data could provide some help. Based on previous studies, this work builds a model factoring in the main structural determinants of discrepancies between mirrored data concerning Italy’s external trade in the period 2010-13, considered at a highly detailed (6-digit) level of goods classification for each partner country. Point estimates of freight costs are used to net the cif-fob discrepancy. The regression estimates are then used to compute TBML risk indicators at country and at (4-digit) product level. Based on these indicators, rankings of countries and product lines can be compiled and used to detect potential illegal commercial transactions.