A Semantic Network Analysis of Laundering Drug Money

Martin Neumann, Nicholas Sartor


This article presents a case study of a money-laundering process. A database of police interrogations for a number of interrelated cases shows the enormous complexity of this process, exceeding the capacities of manual reconstruction. For this reason, semantic networks were reconstructed from the textual data, using the natural language processing techniques of artificial intelligence. These enabled the semantic field of this particular case to be dissected. The results reveal highly professional worldwide financial transactions. Criminal activity benefited from the infrastructure of offshore centres of the legal financial economy and permeated legal business, and the borders between legal and illegal activities became blurred. In fact, the money-laundering activity was only uncovered after the network broke down. Before the group had become known following an outbreak of internal conflict, the concealment of illegal sources of money had not been detected by law enforcement agencies. A case study does not allow for generalization. In particular, this case is not representative because the actors had access to significant resources beyond the reach of petty criminals. However, the findings from this case suggest that, in principle, professional money launderers are able to evade money-laundering regulations.

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