Management Strategy, Vertical Integration, and Geographical Concentration of Money Laundering in Drug-Consuming Countries
The article by Aili Malm and Gisela Bichler, “Using Friends for Money: The Positional Importance of Money-Launderers in Organized Crime” (2013), offers an analysis within the specific context of the illicit drug market. Although published over a decade ago, this study remains frequently cited due to its methodological rigour and its exemplary use of criminal intelligence data 1 to model criminal activity, thereby supporting investigative services.
Malm and Bichler establish two complementary money laundering (ML) models 2 alongside the long-standing paradigm of professional money launderers. Their major contribution lies in the relative positioning of these models and the socio-economic mechanisms that guide their selection.
The authors classify money launderers into three distinct categories:
1. Professional Money Launderers (8% of cases) These individuals possess technical expertise in financial, legal, or real estate matters—such as lawyers, accountants, brokers, or real estate agents. They typically lack pre-existing personal ties to drug traffickers. Their role hinges on the technical sophistication of their operations, including the ability to exploit legal loopholes, bypass surveillance mechanisms, and ensure apparent legitimacy. This category aligns with the traditional model of professionalised ML, often operating with the support of uncooperative or minimally cooperative states, as well as financial institutions or their subsidiaries in these jurisdictions (Financial Action Task Force FATF-GAFI, 2018). In this model, the supply of ML services exceeds demand. Professional launderers act as profit multipliers for trafficking activities, facilitating speculation in real estate, art market inflation, and synergies with other illicit trades, such as cultural artefacts.
2. Opportunistic Money Launderers (12% of cases) While initially lacking specialised skills, these individuals leverage familial or friendly connections with traffickers to engage in ML activities.
3. Self-Launderers (80% of cases) These individuals combine drug trafficking with the laundering of their own profits, often through small-scale commercial enterprises (e.g., restaurants, beauty salons). For them, ML is an extension of the trafficking value chain.
Three key observations must be added:
A. Structural Similarities Between Opportunistic and Self-Launderers The models associated with opportunistic and self-launderers are structurally similar. Their primary objective is the rapid enjoyment of profits. Due to amateurism, unawareness of threats (such as competition among criminal groups or the effectiveness of anti-money laundering efforts), or the actual low level of such threats, they employ unsophisticated layering techniques. They see no need to engage professional launderers.
B. Weak Connectivity in ML Networks ML networks exhibit low connectivity. No professional or opportunistic launderer has more than four connections, and only 25% of self-launderers exceed five connections—a density explained by trafficking activities rather than ML. Consequently:
- The arrest of one actor is unlikely to trigger a domino effect.
- The hypothesis of vast ML networks, even among professional launderers, is weakened.
- The ML market accessible to traffickers is fragmented, with an abundance of professional launderers, reinforcing the hypothesis of an oversupply relative to demand.
C. The Phenomenon of “Non-Laundering” The category of self-launderers includes instances of “non-laundering,” where traffickers directly spend cash proceeds. Apart from the temporary storage of large sums, this practice is most common among low-level traffickers, whose relative poverty is famously illustrated in Levitt and Dubner’s chapter, “Why do drug dealers still live with their moms?” (2009).
What lessons can be drawn from this?
Admittedly, this study is limited to the laundering of drug trafficking proceeds, within a specific consumer region and over a defined period. Nevertheless, its implications for public policy and investigative practice are significant.
What the authors describe is an illegal economic fabric composed of individuals and small-scale businesses (micro and small enterprises) engaging in unsophisticated money laundering. It is dwarfed in size by the legal economy within which it is embedded, and thus remains largely unaffected by current anti-money laundering regulations. It is the oldest accounting and sound management standards that offer the best opportunities for detection.
The primary challenge for detection and tracing lies not in the sophistication of concealment techniques, but rather in the overwhelming volume of data that must be processed, within which only a minute – and often indirect – fraction may expose ML activities. At this juncture, tools such as those developed through the ENSEMBLE project, designed to analyse financial transactions and criminal value chains, could enhance these approaches by pinpointing anomalies even within poorly structured operations. For instance, the automated identification of suspicious transactions or tampered commercial records could effectively isolate activities that are frequently obscured amid the vast expanse of legitimate transactions.
Continuing this line of reasoning, ML – within the confines of this study – is a secondary activity to trafficking. The self-laundering trafficker, representing 80% of observed cases, adopts a cost-centre approach (as opposed to a profit-centre approach). The vertical integration they practise by incorporating money laundering aims solely to limit costs and quickly benefit from the proceeds of their trafficking. Not only are the layering techniques simple, but they are also geographically confined.
Finally – and this is perhaps the most important point – the managerial perspective, specifically decision-making theories, prevails. Attention is focused on the trafficking value chain, which is also the primary area of expertise and, consequently, the main decision-making chain (Ocasio, 1997; Ocasio et al., 2018). For operations to function, contacts must be easy and frequent, which promotes the geographical concentration of actors. Thus, opportunistic launderers must already be close associates of traffickers, while professional launderers remain on the periphery.
Written by Paul Labic. Laboratory for theoretical and applied economics (BETA), CNRS UMR 7522. Associate researcher at the research lab of the French police academy (ENSP)
REFERENCES
Financial Action Task Force FATF-GAFI. (2018). Professional money laundering through trade and money services businesses (Issue July). https://www.fatf-gafi.org/en/publications/Methodsandtrends/Professional-money-laundering.html
Levitt, S. D. ., & Dubner, S. J. . (2009). Freakonomics : a rogue economist explores the hidden side of everything. Harper Perennial.
Malm, A., & Bichler, G. (2013). Using friends for money: the positional importance of money-launderers in organized crime. Trends in Organized Crime, 16(4), 365–381. https://doi.org/10.1007/s12117-013-9205-5
Ocasio, W. (1997). Towards an attention-based view of the firm. Strategic Management Journal, 18(Summer Special Issue), 187–206.
Ocasio, W., Laamanen, T., & Vaara, E. (2018). Communication and attention dynamics: An attention-based view of strategic change. Strategic Management Journal, 39(1), 155–167. https://doi.org/10.1002/smj.2702
NOTES
- Data for this article were sourced from the Royal Canadian Mounted Police’s 2007 threat assessment report, involving approximately 40 intelligence officers. The dataset covered organised crime in British Columbia from 2004–2006, focusing on drug trafficking and involving 129 criminal groups (2,197 individuals). ↩︎
- The general ML model comprises three stages: (1) placement, complicated by cash payments in certain crimes, including drug trafficking; (2) layering, which may involve real estate speculation, luxury goods trading, artefacts, crypto-assets, etc.; and (3) integration (or sanctification), where laundered funds are made available. The latter two stages can be iterative, as static assets may arouse suspicion. ↩︎
