Objective: Empirical knowledge on how organized crime groups (henceforth OCGs) launder their illicit proceeds is still scarce. The standard economic approach assumes that profit maximization is their main driver, even when dealing with illicit proceeds. Criminologists criticized this unilateral approach and called for behavioral interpretations stemming from empirical research. Consequently, we aim to expand knowledge on drivers and constraints of money laundering.
Data/method: An in-depth quantitative content analysis (QCA) of 15 money laundering case studies based on open-source data and text mining is performed. We implement an innovative data gathering approach to identify patterns and schemes of OCGs’ money laundering activities - encompassing different groups, predicate offences, and countries - by analyzing judicial and police documents, supplemented with other sources (e.g. media reports).
Results: OCGs tend to employ unsophisticated ML strategies and invest in tangible assets, also limiting the number of third parties involved. Given the risky conditions in which OCGs operate, risk minimization seems to orient their decisions while economic returns are found to be secondary.
Conclusions/implications: Acknowledging the multi-faceted nature of OCGs is essential to identify ML risks correctly and design effective policies based on grounded evidence, thus also fostering the dialogue between AML practitioners and scholars