A Semantic Network Analysis of Laundering Drug Money

Martin Neumann, Nicholas Sartor

Abstract


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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References


Aggarwal, C., & Zhai, C. X., Eds (2012). Mining Text Data. New York: Springer.

Baker, W., & Faulkner, R. (1993). The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry. American Sociological Review, 58, 837-860.

Bardini, T. (2000). Bootstrapping: Douglas Engelbart, Coevolution, and the Origin of Personal Computing. Stanford, CA: Stanford University Press.

Bourdieu, P. (1984). Distinction. A Critique of the Judgement of Taste. Cambridge, MA: Harvard University Press.

Brumer, C. (2010). Why soft law dominates international finance – and not trade. Journal of International Economic Law, 13(3), 623-643.

Callon, M. (1987). Society in the making: The study of technology as a tool for sociological analysis. In W. Bijker, T. Hughes, & T. Pinch (Eds), The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology (pp.83-103). Cambridge, MA: MIT Press.

Coats, N., & Rafferty, M. (2007). Offshore financial centers, hot money and hedge funds: A network analysis of international capital flows. In L. Assassi, A. Nesvetailova, & D. Wigan (Eds), Global Finance in the New Century (pp.38-54). Basingstoke: Palgrave MacMillan.

Diesner, J., & Carley. K. (2005). Revealing social structure from texts: Meta-matrix text analysis as a novel method for network analysis. In V. Narayanan, & D. Armstrong (Eds), Causal Mapping for Research in Information Technology (pp.81-108). Hershey, PA: IDEA Group Publishing.

Diesner, J., & Carley, K. (2010). Extraktion relationaler Daten aus Texten. In C. Stegbauer, & R. Häussling (Eds), Handbuch Netzwerkforschung (pp.507-521). Wiesbaden: VS Verlag.

Diesner, J., Carley, K. M., & Tambayong, L. (2012). Extracting socio-cultural networks of the Sudan from open-source, large-scale text data. Computational and Mathematical Organization Theory, 18(3), 328-339.

Diesner J., Frantz T., & Carley K. (2005). Communication networks from the Enron email corpus: “It’s always about the people. Enron is no different”. Computational and Mathematical Organization Theory, 11(3), 201-228.

Duijn, P., Kashirin, V., & Slot, P. (2014). The relative ineffectiveness of criminal network disruption. Nature.com Scientific Reports, 4, Art. No. 4238. doi:10.1038/srep04238.

Gilmour, N. (2014). Understanding money laundering: A crime script approach. The European Review of Organized Crime, 1(2), 35-56.

Hanset. O., & Monteiro, E. (1998). Understanding Information Infrastructure (online manuscript). Retrieved from http://heim.ifi.uio.no/~oleha/Publications/bok.html.

Harnischmacher, R. (2009). Geldwaesche. Die Kriminalpolizei: Zeitschrift der Gewerkschaft der Polizei. Retrieved from http://www.kriminalpolizei.de/ausgaben/2009/ detailansicht-2009/artikel/geldwaesche-money-laundering-1.html.

Klerks, P. (2001). The network paradigm applied to criminal organizations. Connections, 24, 53-65.

Krebs, V. (2002). Mapping networks of terrorist cells. Connections, 24, 43-52.

Latour, B. (2005). Reassembling the Social: An Introduction to Actor Network Theory. Oxford: Oxford University Press.

Levi, M., & Reuter, P. (2006). Money laundering. Crime and Justice, 34(1), 289-375.

McCallum, A. (2005). Information extraction: Distilling structured data from unstructured text. ACM Queue, 3(9), 48-57.

Neumann, M., & Lotzmann, U. (forthcoming). Eine Herrschaft des Terrors: Gewalteskalation in illegalen Organisationen. In A. Groenemeyer, C. Equit, & H. Schmidt (Eds), Situationen der Gewalt. Weinheim: Beltz.

Quirk, P. (1997). Macroeconomic implications of money laundering. Trends in Organized Crime, 2(3), 10-14.

Sartor, N. (2015). Eine Netzwerkanalyse der Organisationsstrukturen im Bereich der Geldwäsche. Master’s thesis, Universität Koblenz-Landau, Koblenz, Germany.

Schneider, F., Dreer, E., & Riegler, W. (2006). Geldwaesche: Formen, Akteure, Groessenordnungen – und warum die Politik machtlos ist. Wiesbaden: Gabler.

Sparrow. M. (1991). The application of network analysis to criminal intelligence: A transnational organized crime threat assessment. Social Networks, 13, 251-274.

Steinko, A.F. (2012). Financial channels of money laundering in Spain. British Journal of Criminology, 52, 908-931.

Turner, N. (2015). The financial action task force: International regulatory convergence through soft law. New York Law School Review, 59, 548-559.

Van Duyne, P., & Levi M. (2005). Drugs and Money: Managing the Drug Trade and Crime – Money in Europe. London: Routledge.

Voinea, C., & Schatten, M. (2015). Recovering the past: Eastern European web mining platforms for reconstructing political attitudes. European Journal of Political Attitudes and Mentalities, 4(1), 22-39.

Wassermann, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.


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