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The widespread use of non-cash payments in Russia is the result of a complex bundle of factors: tax evasion, established networks of enterprises and policy-induced causes. By use of non-monetary payments, agents have relaxed their liquidity constraints, but they still lack the financial resources needed to undertake investment and restructuring. Banks' credit provision is based on criteria other than the profitability of the recipient of the funds. The interest rate, coupled with the restructuring of the banking sector, could operate as an effective lever that may lead to a more economically consistent management of liquidity and could also reduce capital flight.

Misreporting tricks of different sorts applied to the transfer of goods between different countries are typically exploited by criminals worldwide for money laundering ends. The main international anti‐money laundering organisations started paying attention to this phenomenon, dubbed “trade‐based money laundering” (TBML), a long time ago, but the failure to develop appropriate analytical tools has reportedly dogged preventive actions. Nonetheless, literature has widely advocated the possibility that the analysis of inconsistencies in mirrored bilateral trade data could provide some help. By building on previous contributions in the field, this work sets up a model factoring in the main structural determinants of discrepancies between mirrored data concerning Italy's 2010 to 2013 external trade at a highly detailed (6‐digit) level of goods classification for each partner country. Point estimates of freight costs are used to net each observation of the corresponding cif/fob discrepancy. The regression estimates are then deployed in order to compute TBML risk indicators at a country/4‐digit product level. Based on the indicators, rankings of countries and product lines can be compiled, which may be used for a risk‐driven search of potential illegal commercial transactions.

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