Name Your Price: Exploring the Costs of Professional Money Laundering Services in Large-Scale Cases in the United States

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Despite debate over its effectiveness, empirical evidence on the pricing of money laundering services is limited, primarily due to the dearth of data and information on money laundering. To address this knowledge gap, this study examines the pricing of professional money laundering services in the United States, drawing on an explorative sample of 90 large-scale criminal cases from January 2007 to December 2024. The findings reveal a median commission fee of 9% of the total amount to be laundered, with notable price variations across different types of predicate offences. Cybercrime stands out as the most expensive, with a median commission fee of 25%, followed by drug trafficking and fraud, both at 7%. Implications of the findings for both research and policy are discussed. Figures Figure 1 Figure 2 1. Introduction Money laundering is the process aimed at disguising the origin of crime proceeds and is a key issue for most profit-oriented offenders. The Financial Action Task Force (henceforth FATF) – the global standard-setter on money laundering – claims that the global anti-money laundering regime has made it harder for criminals to launder their illicit proceeds over the decades, thus pushing them to increasingly rely on professional money launderers (PMLs) (FATF, 2018 ). Offenders reasonably believe that they can lower their risk of detection and arrest by hiring third-party experts who offer their specialized knowledge and technical skills in return for a commission fee determined through a market-based exchange (Kramer et al., 2023 ). However, it is surprising to note that we still “know little about the supply side of money laundering services” (Levi & Soudijn, 2020 , p. 29). The cost of money laundering is a key component in microeconomic models of criminal behavior (Imanpour et al., 2019 ; Masciandaro, 1998 , 2007 ; Masciandaro & Barone, 2008 ; McCarthy et al., 2015 ). Anti-money laundering policy aims at raising this cost. Like in other illicit markets (Bright & Ritter, 2010 ; Gathmann, 2008 ; Manski et al., 2001 ), the amount of money that PMLs charge for their services can be used to evaluate the effectiveness of national and international policies aimed at curbing this illicit market. From a theoretical point of view, stricter enforcement should increase the risk of detection and arrest by adding an extra cost for the suppliers of money laundering services who, assuming a somewhat efficient market, should raise their commission fees to compensate for the increased risks they face. In turn, predicate offenders should reduce their engagement in criminal activities because of the lower net returns after the more expensive money laundering process. Empirical evidence supports this mechanism; for example, in the case of the drug trafficking, higher drug prices frequently are often associated with increased risks of detection for traffickers (see, for example, Aziani et al., 2021 ; Boivin, 2014 ; Caulkins & Reuter, 2010 ). As a result, PMLs’ commission fees can be used as “a performance indicator as to how well the control system works” (Levi & Reuter, 2006 , p. 320). However, despite the debate on the effectiveness of the anti-money laundering regime (see for a review see Levi, 2020 ), information on the prices of money laundering services has been historically episodic and anecdotal (Levi & Reuter, 2006 ; Reuter & Truman, 2005 )—a reality that persists today. For example, the recent Europol’s “European Financial and Economic Crime Threat Assessment” (2023) reported that PMLs charge commission fees ranging from 5–20% of the sums laundered. However, the report did not provide empirical data to substantiate these figures. In fact, comprehensive data on the pricing of money laundering services —and on money laundering more broadly—remains largely unavailable. This study seeks to fill this knowledge gap by creating a unique dataset on money laundering service prices to answer the following research question: What are the prices of money laundering services in large-scale cases? To address this, it analyzes the commission fees charged by PMLs in 90 large-scale money laundering cases investigated in the United States between January 2007 and December 2024. The study focuses exclusively on large-scale cases—defined as those involving illicit proceeds exceeding $ 100,000—in order to ensure analytical consistency and to concentrate on operations that are most likely to involve professional money laundering services. Existing literature identifies the scale of illicit proceeds as a primary determinant in the use of such specialized services, as higher amounts typically “create a need for formal money laundering” (Caulkins & Reuter, 2022 , p. 5). In contrast, low-value cases are less likely to warrant or afford professional laundering, often relying on informal or unsophisticated methods (Levi & Soudijn, 2020 ). Limiting the analysis to high-value cases thus enhances comparability and ensures the findings are relevant to the study of structured, professionalized money laundering networks. The present study is structured as follows. The next section illustrates the theoretical framework and the rationale behind adopting a market-oriented approach for the analysis of money laundering activities. The subsequent sections review previous empirical literature on the price of money laundering services. Then the paper details the data and methodology used to answer the research question. Subsequently, the main findings of the analysis are presented and discussed along with limitations, policy implications, and potential directions for future research. 2. The impact of law enforcement on the price of money laundering services Prices of goods and services in a market are the result of the interaction between supply and demand. When it comes to illegal transactions, price dynamics are strongly influenced by the level of law enforcement action. The baseline theoretical framework for modelling this relationship is the risks and price framework elaborated by Reuter and Kleiman ( 1986 ) for the illicit drug market, which builds on Williamson’s transaction cost economics (Williamson, 1973 ). Based on this theoretical framework, the economic returns for drug traffickers at each market level is the overall amount of illicit proceeds generated from the sales of drugs less three main cost components: (a) the costs of purchasing the drugs or raw materials (e.g., chemical precursors); (b) the conventional business costs (e.g., transportation and storage of drugs); (c) the non-monetary costs (e.g., risks of product loss, risks of arrest, risks of violence). As conventional business costs are negligible in drug markets, the non-monetary costs are the ones responsible of the mark-up in the price charged at both the wholesale and retail level by the traffickers, compared to the cost of obtaining drugs (Reuter & Kleiman, 1986 ). The scrutiny of law enforcement agencies (LEAs) increases the costs of drug production and distribution and ultimately the prices charged at the retail level because criminals, like legitimate entrepreneurs, pass their costs to the final customers. The risks and price framework can also be applied to the money laundering market. Like other criminals, PMLs face three main cost components: (a) the costs of designing and setting up the money laundering scheme (e.g., bribes for employees of anti-money laundering obliged entities); (b) the conventional business costs (e.g., commissions in legitimate financial transactions, tax payments); (c) the non-monetary costs (e.g., risks of arrest, risks of confiscation of illicit proceeds, risk of violence by competitors or clients themselves). Because the time to set up a money laundering scheme, as well as the commissions involved in most legitimate financial transactions, are negligible (Levi & Reuter, 2006 ), the price charged to the clients should almost fully reflect the compensation for the risks that PMLs are exposed to. There are three main ways in which LEAs can influence the price of money laundering services by imposing penalties on both customers and the suppliers of the services. First, the anti-money laundering controls implemented by obliged entities (e.g., banks) make the process of moving illicit proceeds risky and costly. Second, LEAs’ action may temporarily disrupt the supply side of the market by arresting current providers of money laundering services and discouraging the ones willing to enter the business, thus reducing the availability, and increasing the price of such services. Third, enforcement may also intervene on the demand side of the money laundering market. Offenders routinely choose between self-laundering and employing a PML. Stricter anti-money laundering controls (e.g., higher probability of detection, longer sentences in case of conviction) may encourage offenders to choose PMLs over self-laundering to lower their risks of detection and arrest. Such a change of preference may increase the demand for the services and consequently their prices in the market. In general, the cost of money laundering services tends to correlate with the level of scrutiny imposed by authorities on participants in the illicit market. The degree to which this external factor impacts prices is not static; rather, it evolves dynamically in response to changes in legislation and enforcement measures that vary over time. Consequently, stringent and effectively enforced anti-money laundering controls are likely to lead to higher prices for money laundering services. The resulting mark-up reflects the risk premium of PMLs whose activities have become riskier. Following the same reasoning, if anti-money laundering controls become more or less stringent over time, prices of money laundering services should respectively increase or decrease. 3. Previous empirical research on the market for money laundering Policy evaluation is a key component of public policy. Scholars of money laundering have also long focused on examining the premises, mechanisms, and outcomes of the global anti-money laundering regime to assess the extent to which they are effective and cost-effective (Ferwerda, 2018 ; Ferwerda & Reuter, 2019 ; Gerbrands et al., 2022 ; Halliday et al., 2020 ). Although its positive welfare impact is often taken for granted (Levi et al., 2018 ), several studies have highlighted notable shortcomings — from its limited effectiveness in preventing serious profit-driven crimes to its potential unintended consequences (Halliday et al., 2019 ; Levi et al., 2018 ; Pol, 2020 ). However, measuring the effectiveness of anti-money laundering policies is inherently difficult because identifying their goals is not straightforward and specifying the scale of money laundering in their absence is impossible (Ferwerda, 2018 ; Sharman, 2008 ). Overall, efforts in this domain have been frustrated by the lack of hard evidence on this phenomenon. Nevertheless, collecting and sharing more and higher quality money laundering data has not been a priority for international and national bodies (Levi et al., 2018 ). In the absence of suitable success measures, claims of success have been based on compliance with anti-money laundering standard themselves (Halliday et al., 2019 ). In particular, the FATF has endorsed a view that considers high numbers of arrests and confiscations of proceeds of crime as appropriate indicators of success of the anti-money laundering regime. However, this approach is inherently flawed as also the opposite works: if anti-money laundering policies are truly effective in deterring offenders from laundering their illicit proceeds, then small numbers of arrests and confiscations should occur over time. Conversely, a more nuanced approach has emerged, suggesting “to measure the cost of laundering money to criminals, in the same way that the street price of illegal drugs is used to measure the impact of drug control strategies” (Sharman, 2008 , p. 15). Despite the promise of this perspective, the price dynamics of the money laundering market remain largely unexplored (Levi & Soudijn, 2020 ). Even the FATF report on professional money laundering lacks systematic data on service prices, although it acknowledges that “the main characteristic that makes PML unique is the provision of ML services in exchange for a commission, fee or other type of profit” (FATF, 2018 , p. 10). As already highlighted by Levi and Reuter ( 2006 ), there are two main reasons underpinning a similar lack of attention. First, information on the magnitude of the fees received by PMLs is often lacking in most criminal investigations. LEAs do not systematically collect and record this information when investigating a money laundering case because it is not considered necessary to obtain a conviction. Second, from an analytical point of view, price is an ambiguous concept: it may indicate either the fraction of funds received by PMLs, including what they must pay to additional providers involved in their schemes, or the total share of the illicit proceeds that do not return to the offenders’ control. The latter includes a variety of additional cost components such as the tax payments that are due, for example, when laundering illicit proceeds through a legitimate business or the costs associated with running a front business for money laundering intentionally designed to incur losses. To the best of our knowledge, despite the growing academic interest in PMLs (see, for example, Kramer et al., 2023 ; Levi, 2021 , 2022 ; Soudijn, 2024 ), only two studies – to a different extent - have provided information on commission fees related to money laundering services to date (Reuter & Truman, 2004 ; Soudijn & Reuter, 2016 ). When discussing about the market for money laundering services, Reuter and Truman ( 2004 ) provided information on eight cases between 1993 and 1995 involving PMLs worldwide, finding an average commission fee of 9% for their services. However, the authors themselves cautioned that “the data are merely illustrative and so sparse that no inferences about price trends can be drawn” (Reuter & Truman, 2004 , p. 36). Soudijn and Reuter ( 2016 ) analyzed six cases of bulk cash smuggling used by Colombian drug traffickers to move illicit proceeds from the Netherlands back to Colombia. Their results showed that such services are quite expensive, with cash smugglers charging between 10% and 17% of the total amounts. In addition to the above-mentioned studies, fragmented information on money laundering prices also emerges from other academic studies where the focus was not on professional money laundering services. Soudijn ( 2012 ) recounts a criminal case involving a Dutch cashier in Amsterdam who charged a British drug trafficker a 2% commission fee for exchanging his pounds into euros. Kruisbergen et al. ( 2019 ) analyzed 30 cases of organized crime to investigate the role of new technologies in money laundering. In particular, a case featured a PML who charged a commission fee of 7% to convert Bitcoins in cash – a much higher fee compared to the ones requested by legitimate providers. Farfán-Méndez ( 2019 ) explored how Mexican drug organizations launder illicit proceeds. In one of the three case studies examined, PMLs laundered drug proceeds by buying jewels and gold, reselling the high-value goods later to refineries mostly located in Florida and then smuggling the resulting cash to Mexico. It is worth noting that this entire process cost the drug traffickers between 2% and 4% of the total amount of illicit proceeds they handed over to the PMLs. Lastly, Benson ( 2018 ) analyzed 20 cases involving solicitors or chartered accountants convicted of money laundering in the United Kingdom. Notably, she found that 80% of them (16) were remunerated with fees that did not exceed the typical amount of money they would have received if the transaction had involved legitimate clients and funds. Overall, information on the prices of money laundering services remains minimal and ambiguous, typically drawn from broader studies on money laundering that do not explicitly focus on commission fees or systematically examine the cost structures of professional laundering services. As a result, existing insights are often anecdotal, fragmented, or inferred indirectly, highlighting a significant gap in the literature regarding the economics of professional money laundering. Addressing this gap is important for two key reasons. First, empirical data on the prices charged by PMLs can shed light on their financial incentives and the actual returns generated through their involvement in money laundering activities—an area that has received limited attention despite growing recognition of their role. Second, from a policy perspective, effective anti-money laundering (AML) measures should raise the cost of laundering illicit proceeds, thereby reducing the profitability of crime and disincentivizing predicate offences. Empirical evidence on laundering fees thus provides a critical foundation for evaluating whether current AML strategies are effectively targeting key pressure points in the laundering process or whether a recalibration of policy focus is warranted. 4. Data and methodology Data availability poses a significant challenge in money laundering research, particularly when studying the behaviors of money launderers (van Duyne et al., 2018). As van Duyne (2003) suggests, understanding these actors requires focusing on their observable conduct, specifically how they handle illicit proceeds. Given the limited access to detailed police investigation files or court documents—resources typically employed in criminological research (Roks et al., 2022)—this study adopted a novel approach by relying on publicly available law enforcement press releases. The aim was to build a dataset of large-scale criminal cases involving PMLs, with a particular focus on the fees charged for their services. To this end, we systematically analyzed press releases issued by the Offices of the United States Attorneys in the United States from January 2007 to December 2024. While these press releases lack the granular detail of internal police files, they allow for large-scale data collection, a crucial factor in understanding the economic dimensions of professional money laundering. The United States was selected as the case study for three main reasons. First, U.S. authorities have emphasized the importance of tracking the pricing of money laundering services since the 2002 National Money Laundering Strategy (U.S. Treasury, 2002), making the country particularly relevant for collecting evidence on professional money laundering services. Second, the openness of the U.S. judicial system provides unique access to case information, a level of transparency often unavailable in other jurisdictions (Roks et al., 2022). Third, the United States has played a dominant role in shaping global anti-money laundering frameworks and has been at the forefront of investigating and prosecuting money laundering cases. Additionally, focusing on a single country enhances comparability across cases by ensuring consistency within the legal and geographical context. While variations in enforcement priorities within U.S. law enforcement agencies are acknowledged, this approach minimizes the variability caused by differences in legal definitions and anti-money laundering frameworks across jurisdictions (Matanky-Becker & Cockbain, 2021). Data Collection Process To construct the dataset, we conducted a systematic analysis of press releases issued by the Offices of the United States Attorneys between January 2007 and December 2024. 1 These press releases are typically published when criminal cases result in arrests, indictments, convictions, or sentences and often include relevant information, such as the fees charged for professional money laundering services. Data collection involved retrieving and classifying information from the press releases published on the official website, including titles, content, and links, while adhering to ethical guidelines. The Offices of the United States Attorneys has published a total of 253,497 press releases spanning the study period. To identify potentially relevant cases, we conducted a keyword search for the term “money laundering” within the title or body of each release. This yielded 19,809 press releases (approximately 8% of the total). Each of these was manually read and reviewed to determine its eligibility for inclusion in the analysis. A case was included if it explicitly involved at least one PML—defined as individuals or groups offering money laundering services for a commission fee while knowingly handling illicit proceeds (Kramer et al., 2023; Malm & Bichler, 2013). PMLs were distinguished from self-launderers or informal facilitators, who may launder money as a favor for social contacts rather than as a professional service (Caulkins & Reuter, 2022). To ensure analytical consistency and focus on structured, large-scale operations, we applied a minimum threshold of $100,000 in total illicit proceeds associated with the broader criminal investigation. While this threshold is inherently somewhat arbitrary, it was informed by the distribution of case values in the broader dataset: a large share of the relevant cases involved proceeds well above $100,000. As a result, the cutoff did not lead to the exclusion of a significant number of potentially relevant cases. Instead, it allowed us to retain a sufficiently large and analytically meaningful sample. Additionally, cases were included only if they provided a clearly stated fee amount for the laundering service, rather than vague or general references to the existence of a payment. The manual review process identified 143 eligible press releases. Since multiple press releases can relate to the same case—particularly when arrests, indictments, and convictions occur at different stages—these were consolidated into single entries to avoid duplication, resulting in 125 unique cases. Of these, 30 cases (24%) were excluded due to missing fee information, and an additional 12 cases (10%) were excluded due to missing information on the total proceeds connected to the case or because the proceeds were below $100,000. This resulted in a final sample of 90 large-scale professional money laundering cases investigated between 2007 and 2024. To enhance data quality, key case details—such as defendant names and specific circumstances—were triangulated with other open-source materials. Where available, additional court documents, including complaints, indictments, and judgments, were retrieved from platforms like Court Listener. 2 Supplementary information from media reports and other online sources enriched 54% of the cases. Method of analysis The resulting dataset was analyzed using a mixed-methods approach. A quantitative content analysis was performed to extract structured information, including the amount of illicit proceeds, predicate offenses, the number of PMLs involved, money laundering methods, and the fees charged for services. This quantitative analysis was complemented by a qualitative exploration of selected cases to gain a deeper understanding of the techniques used by PMLs. Court documents, where available, provided direct evidence, such as quotes from wiretaps, affidavits, and other case materials, offering richer insights into PML operations. 5. Results Key characteristics of professional money laundering large-scale cases in our sample Table 1 provides an overview of the key characteristics of the cases included in the sample. The total amount of illegal proceeds associated with the investigations of our 90 large-scale cases exceeds $ 1.7 billion. Among the 90 large-scale cases, 49% involve amounts ranging from $ 100,000 to $ 1 million, 29% from over $ 1 million to $ 10 million, and 22% exceeding $ 10 million. We chose to focus on large-scale cases, applying an exclusion criterion for cases below $ 100,000, to reduce variability and inconsistencies that could dilute the overall findings. Narrowing the sample helps maintain a clear focus on patterns relevant to large-scale operations. It is often not possible to definitively link reported commission fees to specific laundered amounts. For instance, a press release may state that a criminal group laundered $ 10 million and separately mention a 9% fee. However, such a fee may apply only to an individual transaction, and assuming it reflects the entire amount would be speculative—and potentially misleading. Accordingly, we refrain from calculating precise revenue shares or inferring discount patterns (e.g., lower commission fees for higher volumes) based on these figures. The cases cover several predicate offences. The largest share (34%) involved the laundering of illicit proceeds originating from drug trafficking, followed by fraud (e.g., investment frauds) (22%), cybercrime (10%), and other predicate offences (18%), such as human trafficking, corruption and counterfeiting. Of note, PMLs handled illicit proceeds originating from multiple predicate offences only in 10% of the cases. This result seems to suggest that PMLs may specialize in handling illicit proceeds stemming from just a single predicate offence, probably to take full advantage of situational factors that may facilitate their activities, such as the ownership of cash-intensive legitimate businesses that well suit cash proceeds. In the remaining cases (6%), it was not possible to identify the underlying predicate offence from the available case materials. The number of PMLs involved in each case varies between 1 and 24, with a mean of 2.6. The PMLs appear to primarily work alone or in pairs, with larger professional money laundering networks being relatively rare. Of note, in 47% of the cases (42), PMLs held a legitimate job. Contrary to what previous literature suggests, the results indicate that PMLs do not necessarily require a legitimate occupation to facilitate money laundering; instead, they may leverage skills and expertise in specific financial niches (e.g., peer-to-peer cryptocurrency exchange). However, having a legitimate occupation certainly acts as a facilitator, as PMLs can more easily conceal illicit proceeds behind the legitimacy provided by their businesses or professions. Results showed that, out of the 42 cases involving PMLs with a legitimate occupation, 29% involved business owners, followed by lawyers (21%), check cashiers (14%) and other professionals (36%), such as bankers, accountants and real estate agents. We want to highlight that 44% of the cases in our sample involved an undercover agent acting as an offender attempting to engage a money launderer. Sting operations are designed to build criminal charges against money launderers. As a result, it is important to consider potential overestimations in prices compared to typical criminal transactions, as undercover agents may “not exhibit the same opportunistic bargaining behavior as regular customers” (Moeller, 2012 , p. 37). In other words, undercover agents are more likely to accept the first commission fee proposed by the PML, as their primary objective is not to negotiate the best price but to complete the transactions in order to gather evidence for prosecution in court. Table 1. Key characteristics of professional money laundering cases, aggregated 2007–2024 Source: Authors’ elaboration Figure 1 highlights that this trend is only noticeable in the case of commissions ranging from 11–15%, but these cases represent a minority of our sample (20%). Overall, the data suggests that undercover operations are more likely to result in lower-to-moderate fees, in contrast to real criminal transactions, which exhibit a wider range of fees, with offenders more frequently engaging in higher-fee transactions. This suggests that there is no major issue with the fee distribution in undercover operations. Commission fees of money laundering services Overall, the 90 cases in the sample entail a median commission fee of 9% of the total amount to be laundered (mean commission fee = 12%). 3 Most of the cases (68%) involve a commission fee lower or equal to 10%. Figure 1 shows that commission fees charged for money laundering services vary across predicate offences. Cybercrime is the costliest predicate offence with 25% as median commission fee (mean = 30%), followed by drug trafficking (median = 7%, mean = 8%) and fraud (median = 7%, mean = 10%). A Kruskal-Wallis test confirmed that there is a statistically significant difference between the three predicate offense groups (H = 15.83, p < 0.01). 4 This means the fees across these offense types are not distributed similarly. Mann-Whitney U tests to determine which specific groups differ from each other: cybercrime has significantly higher fees compared to both drug trafficking (z = -3.89, p < 0.01) and fraud (z = -3.21, p < 0.01). It is important to note that determining whether the commission fee covers a full-service money laundering operation—i.e., managing illicit proceeds from placement to integration—can be difficult. Some PMLs, however, appear to offer more targeted services. For example, in case 15, a PML charged a fee ranging from 45–62% of the total amount laundered for providing “cash-out services” to members of a criminal organization involved in stealing bank and credit card accounts, exchanging the illicit proceeds for cash. In another case, the U.S. Department of Justice highlighted in the press release that a lawyer received a 5% fee, despite not offering actual money laundering services to his client. Instead, the lawyer merely facilitated the transfer of illicit proceeds from a Hong Kong bank account to several accounts in New York. From an analytical standpoint, these activities do not qualify as money laundering, as they do not obscure the illicit origin or integrate the proceeds into the legitimate financial system. 6. Discussion The present study is based on the exploratory analysis of 90 criminal investigations involving PMLs who were investigated in the United States from 2007 to 2024. Overall, the results showed that PMLs in the United States charge customers a median fee of 9% of the total amount to be laundered (mean 12%) for processing their illicit proceeds. A significant difference exists in the prices charged across different predicate offences. These findings merit further discussion, as they may offer valuable insights into the effectiveness of the current anti-money laundering regime. The impact of national and international controls on money laundering activities seems to emerge from the case materials. PMLs in the sample appear to be aware of potential detection and arrest risks they encounter while conducting their illicit activities and, as a result, they demand high fees for their services. This clearly stems from case 29 where a PML offered an undercover agent to launder $ 100,000 of alleged drug proceeds in exchange for a 9% commission fee. The undercover agent tried to negotiate the fee, but the PML promptly answered: “ at some point it becomes, you know, the risk starts to overtake, the, umm reward, so to speak ”. Likewise, in case 31, two financial advisors at an offshore investment firm openly admitted to routinely charging higher fees to clients seeking money laundering services compared to those involved in tax evasion, citing the greater risks associated with laundering criminal proceeds. Additionally, the structure of the criminal market appears to influence the commission rates charged by PMLs. For example, the indictment of case 80 includes a wiretapped conversation in which offenders discussed the 20% fee requested by a PML: “ She says everybody brokering right now is paying 15. That’s what she thinks. That everybody pays cash brokers 15. […] She says the market is saturated right know ”. In case 69, a PML remarked: “ They should not mind to pay 15%, which is the going rate. That’s how much it costs ”. These examples suggest that commission rates are not only influenced by risk, but also by market dynamics, including competition and supply-demand conditions within the illicit financial market. The potential effectiveness of anti-money laundering controls may also explain both the limited scope and complexity of the money laundering schemes set up by the PMLs. PMLs may prefer only providing specialized services in one stage of the money laundering process without overseeing and managing the entire scheme to limit their exposure and facilitate the successful outcome of their activities. As a result, offenders may have to rely on multiple PMLs to complete the laundering process – thus paying multiple fees and bearing a higher overall cost – or take the risk by spending proceeds that are not fully laundered. By the same reasoning, variations in commission fees across predicate offences may also be attributed to differences in enforcement scrutiny. This idea is not new; Levi and Reuter ( 2006 ) already suggested the existence of multiple markets for money laundering services depending on the predicate offences, with some of them involving higher-percentage payments, as PMLs would face a greater risk of investigation from law enforcement and more serious penalties. The perception of different enforcement risks linked with various predicate offences seems evident in the case materials. For example, the PML in case 34 charged a 10% commission fee to cash healthcare fraud and mortgage fraud checks but a 30% commission fee for cashing checks related to identity theft tax refund fraud. Finally, in case 17, when introducing an offender to a PML, a co-conspirator advised him to claim that the money was from corruption rather than drug trafficking, as a strategy to reduce money laundering fees. The relatively high commission fees for cybercrime proceeds may reflect the need to eliminate the digital traces that inevitably link the proceeds to their illicit origin. These proceeds are often in digital form – either in bank accounts or cryptocurrencies - and, potentially, may require more complex schemes and technical skills compared to other predicate offences. Simultaneously, cybercrimes have emerged as a significant threat over the last decade, driven by the substantial economic and emotional toll they impose on their victims. The fight against cybercrime has swiftly risen to the top of political agendas worldwide, attracting increased scrutiny and resource allocation (Caneppele & da Silva, 2022 ). However, it is important to note that cybercrime cases account for only about 10% of the sample, a relatively small proportion compared to offence types such as drug-related cases, which represent approximately 34%. This difference in representation may contribute to greater variability in the average commission fees observed for cybercrime proceeds, and could, to some extent, affect the robustness of conclusions drawn for this category. Nevertheless, the consistently higher fees identified in these cases align with the expectation that laundering digital assets may require more technically sophisticated methods and greater risk exposure. While further research with larger samples is needed to confirm these patterns, the findings suggest that cybercrime-related laundering may indeed operate under distinct market conditions. Overall, caution is required when drawing conclusions about the effectiveness of the anti-money laundering system. This study did not attempt a temporal analysis, primarily due to the limited sample size over an extended period, which makes it difficult to reliably track changes or trends in pricing of money laundering services over time. Additionally, the only prior reference for money laundering service prices in the United States is a study reported in the 2002 U.S. National Money Laundering Strategy, which indicated that fees for laundering services ranged from 4–8%, with a high of 12%. However, this figure is based on anecdotal evidence, as neither the number nor the type of cases underlying the estimate are publicly available. As such, it is not possible to conclude that there has been an increase or decrease in the price of money laundering services in the United States. Limitations The present paper has some limitations that warrant further discussion. First, anti-money laundering enforcement targets both the demand and supply sides (Levi & Reuter, 2006 ). While supply-side efforts directed at PMLs should raise the price of money laundering services, demand-side efforts against customers have the opposite effect by reducing demand. Both enforcement actions aim to decrease the volume of laundering and the net returns from crime. However, it is worth noting that prices can only be interpreted along with estimates of quantity of money laundering services which are difficult to obtain. Second, the study suffers from limited external validity because it uses a non-exhaustive sample of professional money laundering large-case cases in the United States. There is no centralized database in which all professional money laundering cases in the United States are stored and from which a random sample could be drawn. Rather, cases were identified through a purposive sampling as they were selected based on their availability and the economic value of the illicit proceeds of the entire investigation. The non-probability selection of the sample means that these cases are not representative of all large-scale professional money laundering cases in the United States. Despite efforts to collect as many cases as possible to mitigate the negative consequences of this sampling approach, it is important to acknowledge this limitation when interpreting the results. Third, the use of law enforcement data further limits the scope of the analysis. Although the investigative methods used by LEAs provide an exclusive glimpse into criminal activities, this type of data only includes criminal cases that matched the scope and resources of the LEAs and, not secondary, were targeted in successful investigations (Roks et al., 2022 ). Additionally, it is still not clear to what extent detected money laundering cases are representative of the population (Levi & Soudijn, 2020 ). For example, one could reasonably argue that “failed” PMLs—those who were caught—differ significantly from undetected ones, precisely because they were detected and arrested. A key implication of this perspective is that PMLs who have been arrested might display lower levels of sophistication and impose lower commission fees compared to their more elusive counterparts, simply because they were not skilled enough to avoid capture. Fourth, the information included in the cases may also be incomplete as law enforcement data is not naturally meant for research purposes. For example, out of 125 professional money laundering cases identified, 90 (72%) cases reported the information on the fee charged and were included in the sample for the analysis. Furthermore, it is worth noting that 26% of the cases (23) were not final convictions or sentences. On-going criminal proceedings are a good trade-off between the solidity of the evidence and the topicality of the case for research purposes (Roks et al., 2022 ). However, it is important to note that the charges in these cases are merely allegations, and the defendants are presumed innocent unless and until proven guilty. Finally, 44% of the cases (40) involved undercover police officers purchasing money laundering services. The primary objective of sting operations is to gather sufficient evidence to prosecute money launderers. Undercover agents, however, may not negotiate fees as aggressively as typical clients because their focus is on completing the transaction to build a solid case, rather than securing the best deal. In our sample, this potential issue of inflated fees is mostly limited to a few cases with relatively high fees, which account for only a minor share of the total cases analyzed (Fig. 1 ). 7. Conclusions, policy implications and directions for future research Despite the above-mentioned limitations, the present study is the first exploratory analysis on an under-researched topic, and it has relevant research and policy implications. First, measuring this cost component is a necessary step in developing a reliable estimate of the overall cost that offenders pay to launder their illicit proceeds. Given the lack of hard data on money laundering, collecting price information may represent a promising avenue for measuring the effectiveness of anti-money laundering policies. However, more granular and systematic data are needed for this purpose. To date, PMLs can still be overlooked during a criminal investigations when, for different reasons (e.g., budget constraints), LEAs do not prioritize money laundering and rather focus on the predicate offence (Levi, 2018 ; Levi & Soudijn, 2020 ; Soudijn, 2014 ). Conversely, LEAs need to adopt a financial approach from the very beginning of a criminal investigation and pay more attention to the financial side of the criminal activities they are investigating (Kramer et al., 2023 ; Roks et al., 2022 ; Soudijn, 2014 ). Such an effort would not only be relevant from a research point of view but also have practical implications for prosecutions. For example, assessing if legitimate professionals (e.g., lawyers) involved in a criminal investigation have charged a premium commission fee to their clients could help demonstrate, beyond reasonable doubt, their knowledge of the illicit origin of the funds and their involvement in money laundering services (Levi & Soudijn, 2020 ). In addition, scholars should strive to explore other data sources to overcome the above-mentioned limitations associated with law enforcement data. For example, interviewing PMLs may provide detailed – and unfiltered – insights on price dynamics and how anti-money laundering controls impact their activities. Despite the significant challenges in convincing offenders to discuss their money laundering activities (see, for a review, Levi & Soudijn, 2020 ), insights from their perspective on which controls effectively hinder their activities could be invaluable for identifying both effective measures and areas needing improvement (see, for example, Berry et al., 2023 ). Another promising research avenue is the analysis of supply-demand interactions related to money laundering services in online environments (e.g., darknet marketplaces and forums), which would allow for large-scale price data collection and support more advanced statistical analyses (Kruisbergen et al., 2019 ). More broadly, future academic research should focus on analyzing the key characteristics of the market for money laundering services. To date, most empirical efforts have focused on assessing the prevalence of PMLs in money laundering activities (see for example Malm & Bichler, 2013 ; Soudijn, 2014 ). While the demand for facilitators may logically vary depending on the scale and nature of the crime proceeds (see, for a review Levi, 2021 ), there is still minimal evidence on the characteristics of the supply side of money laundering services. Important questions remain unanswered, such as what drives variation in laundering fees, how PMLs build and maintain trust with clients, and how disputes or defaults are managed in the absence of legal enforcement mechanisms. Investigating these aspects would not only deepen our understanding of how PMLs operate but also help design more targeted and disruptive AML interventions. Ultimately, recognizing and analyzing professional money laundering as a service market—with its own economic logic, pricing structures, and organizational forms—could mark a significant shift in both academic research and policy effectiveness. Declarations Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used ChatGPT in order to edit the language of the manuscript. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. Funding No funding was received to assist with the preparation of this manuscript. Author Contribution M.N. developed the initial conceptual framework and conducted the literature review. The framework was further refined in collaboration with S.F. M.N. collected and analyzed the data, with analytical input from S.F. M.N. produced the results. The manuscript was written by M.N., with support from S.F., who also reviewed and edited the full draft. Both authors revised the manuscript based on reviewer feedback. Acknowledgement The authors would like to thank Alberto Aziani for his valuable comments on an early draft of this manuscript. They also wish to express their sincere appreciation to the participants of the 79th Annual Conference of the American Society of Criminology (ASC), held in San Francisco in November 2024, for their insightful feedback and suggestions. Data Availability The data that support the findings of this study are available from the corresponding author upon request. References Aziani, A., Berlusconi, G., & Giommoni, L. (2021). A Quantitative Application of Enterprise and Social Embeddedness Theories to the Transnational Trafficking of Cocaine in Europe. Deviant Behavior , 42 (2), 245–267. https://doi.org/10.1080/01639625.2019.1666606 Benson, K. (2018). Money Laundering, Anti-Money Laundering and the Legal Profession. In C. King, C. Walker, & J. Gurulé (Eds.), The Palgrave Handbook of Criminal and Terrorism Financing Law (pp. 109–133). Springer International Publishing. https://doi.org/10.1007/978-3-319-64498-1_6 Berry, M., Salinas, M., & Gundur, R. V. (2023). Financial risk management strategies of small to medium illicit drug enterprises: Considering low-level money laundering. Trends in Organized Crime . https://doi.org/10.1007/s12117-023-09501-5 Boivin, R. (2014). Risks, prices, and positions: A social network analysis of illegal drug trafficking in the world-economy. International Journal of Drug Policy , 25 (2), 235–243. https://doi.org/10.1016/j.drugpo.2013.12.004 Bright, D. A., & Ritter, A. (2010). Retail price as an outcome measure for the effectiveness of drug law enforcement. International Journal of Drug Policy , 21 (5), 359–363. https://doi.org/10.1016/j.drugpo.2009.12.001 Caneppele, S., & da Silva, A. (2022). Cybercrime. In D. Nelken & C. Hamilton (Eds.), Research Handbook of Comparative Criminal Justice . Edward Elgar Publishing. https://doi.org/10.4337/9781839106385 Caulkins, J. P., & Reuter, P. (2010). How Drug Enforcement Affects Drug Prices. Crime and Justice , 39 (1), 213–271. https://doi.org/10.1086/652386 Caulkins, J. P., & Reuter, P. (2022). How much demand for money laundering services does drug selling create? Identifying the key parameters. International Journal of Drug Policy , 103 , 103652. https://doi.org/10.1016/j.drugpo.2022.103652 Europol. (2023). European Financial and Economic Crime Threat Assessment 2023—The Other Side of the Coin: An Analysis of Financial and Economic Crime . Publications Office of the European Union. Farfán-Méndez, C. (2019). The Structure of Drug Trafficking Organizations and Money Laundering Practices: A Risk Appetite Hypothesis. Journal of Illicit Economies and Development , 1 (3), 294–311. https://doi.org/10.31389/jied.1 FATF. (2018). Professional Money Laundering . FATF. https://www.fatf-gafi.org/en/publications/Methodsandtrends/Professional-money-laundering.html Ferwerda, J. (2018). The Effectiveness of Anti-Money Laundering Policy: A Cost-Benefit Perspective. In C. King, C. Walker, & J. Gurulé (Eds.), The Palgrave Handbook of Criminal and Terrorism Financing Law (pp. 317–344). Springer International Publishing. https://doi.org/10.1007/978-3-319-64498-1_14 Ferwerda, J., & Reuter, P. (2019). Learning from Money Laundering National Risk Assessments: The Case of Italy and Switzerland. European Journal on Criminal Policy and Research , 25 (1), 5–20. https://doi.org/10.1007/s10610-018-9395-0 Gathmann, C. (2008). Effects of enforcement on illegal markets: Evidence from migrant smuggling along the southwestern border. Journal of Public Economics , 92 (10–11), 1926–1941. https://doi.org/10.1016/j.jpubeco.2008.04.006 Gerbrands, P., Unger, B., Getzner, M., & Ferwerda, J. (2022). The effect of anti-money laundering policies: An empirical network analysis. EPJ Data Science , 11 (1), 15. https://doi.org/10.1140/epjds/s13688-022-00328-8 Halliday, T., Levi, M., & Reuter, P. (2019). Anti-Money Laundering: An Inquiry into a Disciplinary Transnational Legal Order. UC Irvine Journal of International, Transnational, and Comparative Law , 4 (1). Halliday, T., Levi, M., & Reuter, P. (2020). Why Do Transnational Legal Orders Persist?: The Curious Case of Money-Laundering Controls. In G. Shaffer & E. Aaronson (Eds.), Transnational Legal Ordering of Criminal Justice (1st ed., pp. 51–83). Cambridge University Press. https://doi.org/10.1017/9781108873994.002 Imanpour, M., Rosenkranz, S., Westbrock, B., Unger, B., & Ferwerda, J. (2019). A microeconomic foundation for optimal money laundering policies. International Review of Law and Economics , 60 , 105856. https://doi.org/10.1016/j.irle.2019.105856 Kramer, J.-A., Blokland, A. A. J., Kleemans, E. R., & Soudijn, M. R. J. (2023). Money laundering as a service: Investigating business-like behavior in money laundering networks in the Netherlands. Trends in Organized Crime . https://doi.org/10.1007/s12117-022-09475-w Kruisbergen, E. W., Leukfeldt, E. R., Kleemans, E. R., & Roks, R. A. (2019). Money talks money laundering choices of organized crime offenders in a digital age. Journal of Crime and Justice , 42 (5), 569–581. https://doi.org/10.1080/0735648X.2019.1692420 Levi, M. (2018). Reflections on Proceeds of Crime: A New Code for Confiscation? In J. Child & A. Duff (Eds.), Criminal Law Reform Now . Hart. Levi, M. (2020). Evaluating the Control of Money Laundering and Its Underlying Offences: The Search for Meaningful Data. Asian Journal of Criminology , 15 (4), 301–320. https://doi.org/10.1007/s11417-020-09319-y Levi, M. (2021). Making sense of professional enablers’ involvement in laundering organized crime proceeds and of their regulation. Trends in Organized Crime , 24 (1), 96–110. https://doi.org/10.1007/s12117-020-09401-y Levi, M. (2022). Lawyers as money laundering enablers? An evolving and contentious relationship. Global Crime , 23 (2), 126–147. https://doi.org/10.1080/17440572.2022.2089122 Levi, M., & Reuter, P. (2006). Money Laundering. Crime and Justice , 34 , 289–375. https://doi.org/10.1086/501508 Levi, M., Reuter, P., & Halliday, T. (2018). Can the AML system be evaluated without better data? Crime, Law and Social Change , 69 (2), 307–328. https://doi.org/10.1007/s10611-017-9757-4 Levi, M., & Soudijn, M. (2020). Understanding the Laundering of Organized Crime Money. Crime and Justice , 49 , 579–631. https://doi.org/10.1086/708047 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 Manski, C. F., Pepper, J., & Petrie, C. (2001). Informing America’s policy on illegal drugs: What we don’t know keeps hurting us (National Research Council (U.S.) & National Research Council (U.S.), Eds.). National Academy Press. Masciandaro, D. (1998). Money Laundering Regulation: The Micro Economics. Journal of Money Laundering Control , 2 (1), 49–58. https://doi.org/10.1108/eb027170 Masciandaro, D. (2007). Economics of Money Laundering: A Primer. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.970184 Masciandaro, D., & Barone, R. (2008). Worldwide Anti-Money Laundering Regulation: Estimating Costs and Benefits. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.1136107 Matanky-Becker, R., & Cockbain, E. (2021). Behind the criminal economy: Using UK tax fraud investigations to understand money laundering myths and models. Crime, Law and Social Change . https://doi.org/10.1007/s10611-021-09997-4 McCarthy, K. J., van Santen, P., & Fiedler, I. (2015). Modeling the money launderer: Microtheoretical arguments on anti-money laundering policy. International Review of Law and Economics , 43 , 148–155. https://doi.org/10.1016/j.irle.2014.04.006 Moeller, K. (2012). Costs and revenues in street-level cannabis dealing. Trends in Organized Crime , 15 (1), 31–46. https://doi.org/10.1007/s12117-011-9146-9 Pol, R. F. (2020). Anti-money laundering: The world’s least effective policy experiment? Together, we can fix it. Policy Design and Practice , 3 (1), 73–94. https://doi.org/10.1080/25741292.2020.1725366 Reuter, P., & Kleiman, M. A. R. (1986). Risks and Prices: An Economic Analysis of Drug Enforcement. Crime and Justice , 7 , 289–340. Reuter, P., & Truman, E. (2004). Chasing dirty money: The fight against money laundering (Vol. 381). Institute for International Economics. Reuter, P., & Truman, E. (2005). Anti-Money Laundering Overkill? It’s time to ask how well the system is working. The International Economy , 19 (1), 56–60. Roks, R. A., Kruisbergen, E. W., & Kleemans, E. R. (2022). Walls of silence and organized crime: A theoretical and empirical exploration into the shielding of criminal activities from authorities. Trends in Organized Crime . https://doi.org/10.1007/s12117-022-09447-0 Sharman, J. C. (2008). Power and Discourse in Policy Diffusion: Anti-Money Laundering in Developing States. International Studies Quarterly , 52 (3), 635–656. JSTOR. Soudijn, M. (2012). Removing excuses in money laundering. Trends in Organized Crime , 15 (2–3), 146–163. https://doi.org/10.1007/s12117-012-9161-5 Soudijn, M. (2014). Using strangers for money: A discussion on money-launderers in organized crime. Trends in Organized Crime , 17 (3), 199–217. https://doi.org/10.1007/s12117-014-9217-9 Soudijn, M. (2024). Encounters with Professional Money Launderers; An Analysis of Financial Transactions as Reported by Gatekeepers. European Journal on Criminal Policy and Research . https://doi.org/10.1007/s10610-024-09588-8 Soudijn, M., & Reuter, P. (2016). Cash and carry: The high cost of currency smuggling in the drug trade. Crime, Law and Social Change , 66 (3), 271–290. https://doi.org/10.1007/s10611-016-9626-6 U.S. Treasury. (2002). 2002 National Money Laundering Strategy . https://home.treasury.gov/system/files/136/archive-documents/monlaund.pdf van Duyne, P. C. (2003). Organizing cigarette smuggling and policy making, ending up in smoke. Springer , 39 , 285–317. van Duyne, P. C., Harvey, J. H., & Gelemerova, L. Y. (2018). The Critical Handbook of Money Laundering: Policy, analysis and myths . Palgrave Macmillan. Williamson, O. E. (1973). Markets and Hierarchies: Some Elementary Considerations. American Economic Review , 63 (2), 316–325. Footnotes Press releases published before 2013 can be accessed at the following link: https://www.justice.gov/archive/usao/ . The Court Listener is a repository developed by the Free Law Project, a U.S.-registered non-profit organization. It includes free-accessible court documents that users have downloaded by the Public Access to Court Electronic Records (PACER), the electronic records system maintained by the Administrative Office of the U.S. Courts (AOUSC) and made available to the wider community. In the case of a variable commission fee, the mean value of all the prices reported in the case materials was considered. The Kruskal-Wallis test can be used as a non-parametric alternative to ANOVA, making it suitable for cases where the data does not follow a normal distribution. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Jan, 2026 Read the published version in Trends in Organized Crime → Version 1 posted Editorial decision: Revision requested 01 Sep, 2025 Reviews received at journal 30 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 14 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor assigned by journal 11 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 05 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6828794","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471351982,"identity":"4330f57d-8e07-418a-a619-85809081fdb6","order_by":0,"name":"Mirko Nazzari","email":"","orcid":"","institution":"University of Sassari","correspondingAuthor":false,"prefix":"","firstName":"Mirko","middleName":"","lastName":"Nazzari","suffix":""},{"id":471351983,"identity":"f3ed14b9-9cf4-4272-ad8d-9fbf10431ecb","order_by":1,"name":"Serena Favarin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBADfjCZwGDDwMAMEeEhpEWyAaIlDaLlQAIDDyE9EC0MDIchFFALTmvk23sfPi6oYZDgn5H87MPDHecTt7MzP/z88QeDjD0OLQZnjhsbzzjGICFxI814RuKZ24k7m9mMJfA5zEAijU2ah42hjuHMAWOGxLbbiRsO8zDg1SI//xn7b55/DBLyZ45/Bmo5B9LC/AOv92+wsTHztjFIGBzvAdlyAKSFDb/DzqQxS/P2SUgYHu8pBmpJNt5wmM3M4kyaBA/PAVwhdozxM883Gwm5w+ybGX+22cluOH/48Y0KGxt79gZcLgMDCSJERsEoGAWjYBQQDwB9YlKJOYfBjAAAAABJRU5ErkJggg==","orcid":"","institution":"Catholic University of the Sacred Heart","correspondingAuthor":true,"prefix":"","firstName":"Serena","middleName":"","lastName":"Favarin","suffix":""}],"badges":[],"createdAt":"2025-06-05 11:39:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6828794/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6828794/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12117-025-09587-z","type":"published","date":"2026-01-19T15:57:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84809264,"identity":"ec4892eb-e086-4f68-ab71-bd1ddaa5b56e","added_by":"auto","created_at":"2025-06-17 14:41:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Commission Fees in Undercover Operations vs. Criminal Market Transactions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Percentages are calculated based on the total number of police operations for each type. The sample includes 40 sting operations and 50 non-sting operations\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: Authors’ elaboration\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6828794/v1/e0612ad9b6f5311421988cd6.png"},{"id":84809266,"identity":"e5258ceb-26b4-4b03-a804-453655896eb6","added_by":"auto","created_at":"2025-06-17 14:41:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplot of commission fees charged for money laundering services, grouped by predicate offence (N = 60)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: Authors’ elaboration\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6828794/v1/82b1d92ee2ae8d77929ddbc4.png"},{"id":101152450,"identity":"61f4cca8-f3d6-4464-a27f-08e2ecd50bb4","added_by":"auto","created_at":"2026-01-26 16:11:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":914965,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6828794/v1/47385277-4947-4423-8ccc-c2548d1ec5ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Name Your Price: Exploring the Costs of Professional Money Laundering Services in Large-Scale Cases in the United States","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMoney laundering is the process aimed at disguising the origin of crime proceeds and is a key issue for most profit-oriented offenders. The Financial Action Task Force (henceforth FATF) \u0026ndash; the global standard-setter on money laundering \u0026ndash; claims that the global anti-money laundering regime has made it harder for criminals to launder their illicit proceeds over the decades, thus pushing them to increasingly rely on professional money launderers (PMLs) (FATF, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Offenders reasonably believe that they can lower their risk of detection and arrest by hiring third-party experts who offer their specialized knowledge and technical skills in return for a commission fee determined through a market-based exchange (Kramer et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, it is surprising to note that we still \u0026ldquo;know little about the supply side of money laundering services\u0026rdquo; (Levi \u0026amp; Soudijn, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, p. 29).\u003c/p\u003e \u003cp\u003eThe cost of money laundering is a key component in microeconomic models of criminal behavior (Imanpour et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Masciandaro, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Masciandaro \u0026amp; Barone, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; McCarthy et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Anti-money laundering policy aims at raising this cost. Like in other illicit markets (Bright \u0026amp; Ritter, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gathmann, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Manski et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), the amount of money that PMLs charge for their services can be used to evaluate the effectiveness of national and international policies aimed at curbing this illicit market. From a theoretical point of view, stricter enforcement should increase the risk of detection and arrest by adding an extra cost for the suppliers of money laundering services who, assuming a somewhat efficient market, should raise their commission fees to compensate for the increased risks they face. In turn, predicate offenders should reduce their engagement in criminal activities because of the lower net returns after the more expensive money laundering process. Empirical evidence supports this mechanism; for example, in the case of the drug trafficking, higher drug prices frequently are often associated with increased risks of detection for traffickers (see, for example, Aziani et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Boivin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Caulkins \u0026amp; Reuter, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a result, PMLs\u0026rsquo; commission fees can be used as \u0026ldquo;a performance indicator as to how well the control system works\u0026rdquo; (Levi \u0026amp; Reuter, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, p. 320). However, despite the debate on the effectiveness of the anti-money laundering regime (see for a review see Levi, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), information on the prices of money laundering services has been historically episodic and anecdotal (Levi \u0026amp; Reuter, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Reuter \u0026amp; Truman, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u0026mdash;a reality that persists today. For example, the recent Europol\u0026rsquo;s \u0026ldquo;European Financial and Economic Crime Threat Assessment\u0026rdquo; (2023) reported that PMLs charge commission fees ranging from 5\u0026ndash;20% of the sums laundered. However, the report did not provide empirical data to substantiate these figures. In fact, comprehensive data on the pricing of money laundering services \u0026mdash;and on money laundering more broadly\u0026mdash;remains largely unavailable. This study seeks to fill this knowledge gap by creating a unique dataset on money laundering service prices to answer the following research question: \u003cem\u003eWhat are the prices of money laundering services in large-scale cases?\u003c/em\u003e To address this, it analyzes the commission fees charged by PMLs in 90 large-scale money laundering cases investigated in the United States between January 2007 and December 2024.\u003c/p\u003e \u003cp\u003e The study focuses exclusively on large-scale cases\u0026mdash;defined as those involving illicit proceeds exceeding \u003cspan\u003e$\u003c/span\u003e100,000\u0026mdash;in order to ensure analytical consistency and to concentrate on operations that are most likely to involve professional money laundering services. Existing literature identifies the scale of illicit proceeds as a primary determinant in the use of such specialized services, as higher amounts typically \u0026ldquo;create a need for formal money laundering\u0026rdquo; (Caulkins \u0026amp; Reuter, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e , p. 5). In contrast, low-value cases are less likely to warrant or afford professional laundering, often relying on informal or unsophisticated methods (Levi \u0026amp; Soudijn, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e ). Limiting the analysis to high-value cases thus enhances comparability and ensures the findings are relevant to the study of structured, professionalized money laundering networks. \u003c/p\u003e \u003cp\u003eThe present study is structured as follows. The next section illustrates the theoretical framework and the rationale behind adopting a market-oriented approach for the analysis of money laundering activities. The subsequent sections review previous empirical literature on the price of money laundering services. Then the paper details the data and methodology used to answer the research question. Subsequently, the main findings of the analysis are presented and discussed along with limitations, policy implications, and potential directions for future research.\u003c/p\u003e"},{"header":"2. The impact of law enforcement on the price of money laundering services","content":"\u003cp\u003ePrices of goods and services in a market are the result of the interaction between supply and demand. When it comes to illegal transactions, price dynamics are strongly influenced by the level of law enforcement action. The baseline theoretical framework for modelling this relationship is the risks and price framework elaborated by Reuter and Kleiman (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) for the illicit drug market, which builds on Williamson\u0026rsquo;s transaction cost economics (Williamson, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). Based on this theoretical framework, the economic returns for drug traffickers at each market level is the overall amount of illicit proceeds generated from the sales of drugs less three main cost components: (a) the costs of purchasing the drugs or raw materials (e.g., chemical precursors); (b) the conventional business costs (e.g., transportation and storage of drugs); (c) the non-monetary costs (e.g., risks of product loss, risks of arrest, risks of violence). As conventional business costs are negligible in drug markets, the non-monetary costs are the ones responsible of the mark-up in the price charged at both the wholesale and retail level by the traffickers, compared to the cost of obtaining drugs (Reuter \u0026amp; Kleiman, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The scrutiny of law enforcement agencies (LEAs) increases the costs of drug production and distribution and ultimately the prices charged at the retail level because criminals, like legitimate entrepreneurs, pass their costs to the final customers.\u003c/p\u003e \u003cp\u003eThe risks and price framework can also be applied to the money laundering market. Like other criminals, PMLs face three main cost components: (a) the costs of designing and setting up the money laundering scheme (e.g., bribes for employees of anti-money laundering obliged entities); (b) the conventional business costs (e.g., commissions in legitimate financial transactions, tax payments); (c) the non-monetary costs (e.g., risks of arrest, risks of confiscation of illicit proceeds, risk of violence by competitors or clients themselves). Because the time to set up a money laundering scheme, as well as the commissions involved in most legitimate financial transactions, are negligible (Levi \u0026amp; Reuter, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the price charged to the clients should almost fully reflect the compensation for the risks that PMLs are exposed to.\u003c/p\u003e \u003cp\u003eThere are three main ways in which LEAs can influence the price of money laundering services by imposing penalties on both customers and the suppliers of the services. First, the anti-money laundering controls implemented by obliged entities (e.g., banks) make the process of moving illicit proceeds risky and costly. Second, LEAs\u0026rsquo; action may temporarily disrupt the supply side of the market by arresting current providers of money laundering services and discouraging the ones willing to enter the business, thus reducing the availability, and increasing the price of such services. Third, enforcement may also intervene on the demand side of the money laundering market. Offenders routinely choose between self-laundering and employing a PML. Stricter anti-money laundering controls (e.g., higher probability of detection, longer sentences in case of conviction) may encourage offenders to choose PMLs over self-laundering to lower their risks of detection and arrest. Such a change of preference may increase the demand for the services and consequently their prices in the market.\u003c/p\u003e \u003cp\u003eIn general, the cost of money laundering services tends to correlate with the level of scrutiny imposed by authorities on participants in the illicit market. The degree to which this external factor impacts prices is not static; rather, it evolves dynamically in response to changes in legislation and enforcement measures that vary over time. Consequently, stringent and effectively enforced anti-money laundering controls are likely to lead to higher prices for money laundering services. The resulting mark-up reflects the risk premium of PMLs whose activities have become riskier. Following the same reasoning, if anti-money laundering controls become more or less stringent over time, prices of money laundering services should respectively increase or decrease.\u003c/p\u003e"},{"header":"3. Previous empirical research on the market for money laundering","content":"\u003cp\u003ePolicy evaluation is a key component of public policy. Scholars of money laundering have also long focused on examining the premises, mechanisms, and outcomes of the global anti-money laundering regime to assess the extent to which they are effective and cost-effective (Ferwerda, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ferwerda \u0026amp; Reuter, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gerbrands et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Halliday et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although its positive welfare impact is often taken for granted (Levi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), several studies have highlighted notable shortcomings \u0026mdash; from its limited effectiveness in preventing serious profit-driven crimes to its potential unintended consequences (Halliday et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Levi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pol, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, measuring the effectiveness of anti-money laundering policies is inherently difficult because identifying their goals is not straightforward and specifying the scale of money laundering in their absence is impossible (Ferwerda, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharman, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, efforts in this domain have been frustrated by the lack of hard evidence on this phenomenon. Nevertheless, collecting and sharing more and higher quality money laundering data has not been a priority for international and national bodies (Levi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the absence of suitable success measures, claims of success have been based on compliance with anti-money laundering standard themselves (Halliday et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In particular, the FATF has endorsed a view that considers high numbers of arrests and confiscations of proceeds of crime as appropriate indicators of success of the anti-money laundering regime. However, this approach is inherently flawed as also the opposite works: if anti-money laundering policies are truly effective in deterring offenders from laundering their illicit proceeds, then small numbers of arrests and confiscations should occur over time.\u003c/p\u003e \u003cp\u003eConversely, a more nuanced approach has emerged, suggesting \u0026ldquo;to measure the cost of laundering money to criminals, in the same way that the street price of illegal drugs is used to measure the impact of drug control strategies\u0026rdquo; (Sharman, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, p. 15). Despite the promise of this perspective, the price dynamics of the money laundering market remain largely unexplored (Levi \u0026amp; Soudijn, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Even the FATF report on professional money laundering lacks systematic data on service prices, although it acknowledges that \u0026ldquo;the main characteristic that makes PML unique is the provision of ML services in exchange for a commission, fee or other type of profit\u0026rdquo; (FATF, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, p. 10).\u003c/p\u003e \u003cp\u003eAs already highlighted by Levi and Reuter (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), there are two main reasons underpinning a similar lack of attention. First, information on the magnitude of the fees received by PMLs is often lacking in most criminal investigations. LEAs do not systematically collect and record this information when investigating a money laundering case because it is not considered necessary to obtain a conviction. Second, from an analytical point of view, price is an ambiguous concept: it may indicate either the fraction of funds received by PMLs, including what they must pay to additional providers involved in their schemes, or the total share of the illicit proceeds that do not return to the offenders\u0026rsquo; control. The latter includes a variety of additional cost components such as the tax payments that are due, for example, when laundering illicit proceeds through a legitimate business or the costs associated with running a front business for money laundering intentionally designed to incur losses.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, despite the growing academic interest in PMLs (see, for example, Kramer et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Levi, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Soudijn, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), only two studies \u0026ndash; to a different extent - have provided information on commission fees related to money laundering services to date (Reuter \u0026amp; Truman, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Soudijn \u0026amp; Reuter, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). When discussing about the market for money laundering services, Reuter and Truman (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) provided information on eight cases between 1993 and 1995 involving PMLs worldwide, finding an average commission fee of 9% for their services. However, the authors themselves cautioned that \u0026ldquo;the data are merely illustrative and so sparse that no inferences about price trends can be drawn\u0026rdquo; (Reuter \u0026amp; Truman, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, p. 36). Soudijn and Reuter (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) analyzed six cases of bulk cash smuggling used by Colombian drug traffickers to move illicit proceeds from the Netherlands back to Colombia. Their results showed that such services are quite expensive, with cash smugglers charging between 10% and 17% of the total amounts.\u003c/p\u003e \u003cp\u003eIn addition to the above-mentioned studies, fragmented information on money laundering prices also emerges from other academic studies where the focus was not on professional money laundering services. Soudijn (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) recounts a criminal case involving a Dutch cashier in Amsterdam who charged a British drug trafficker a 2% commission fee for exchanging his pounds into euros. Kruisbergen et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) analyzed 30 cases of organized crime to investigate the role of new technologies in money laundering. In particular, a case featured a PML who charged a commission fee of 7% to convert Bitcoins in cash \u0026ndash; a much higher fee compared to the ones requested by legitimate providers. Farf\u0026aacute;n-M\u0026eacute;ndez (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) explored how Mexican drug organizations launder illicit proceeds. In one of the three case studies examined, PMLs laundered drug proceeds by buying jewels and gold, reselling the high-value goods later to refineries mostly located in Florida and then smuggling the resulting cash to Mexico. It is worth noting that this entire process cost the drug traffickers between 2% and 4% of the total amount of illicit proceeds they handed over to the PMLs. Lastly, Benson (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) analyzed 20 cases involving solicitors or chartered accountants convicted of money laundering in the United Kingdom. Notably, she found that 80% of them (16) were remunerated with fees that did not exceed the typical amount of money they would have received if the transaction had involved legitimate clients and funds.\u003c/p\u003e \u003cp\u003eOverall, information on the prices of money laundering services remains minimal and ambiguous, typically drawn from broader studies on money laundering that do not explicitly focus on commission fees or systematically examine the cost structures of professional laundering services. As a result, existing insights are often anecdotal, fragmented, or inferred indirectly, highlighting a significant gap in the literature regarding the economics of professional money laundering. Addressing this gap is important for two key reasons. First, empirical data on the prices charged by PMLs can shed light on their financial incentives and the actual returns generated through their involvement in money laundering activities\u0026mdash;an area that has received limited attention despite growing recognition of their role. Second, from a policy perspective, effective anti-money laundering (AML) measures should raise the cost of laundering illicit proceeds, thereby reducing the profitability of crime and disincentivizing predicate offences. Empirical evidence on laundering fees thus provides a critical foundation for evaluating whether current AML strategies are effectively targeting key pressure points in the laundering process or whether a recalibration of policy focus is warranted.\u003c/p\u003e"},{"header":"4. Data and methodology","content":"\u003cp\u003eData availability poses a significant challenge in money laundering research, particularly when studying the behaviors of money launderers (van Duyne et al., 2018). As van Duyne (2003) suggests, understanding these actors requires focusing on their observable conduct, specifically how they handle illicit proceeds. Given the limited access to detailed police investigation files or court documents\u0026mdash;resources typically employed in criminological research (Roks et al., 2022)\u0026mdash;this study adopted a novel approach by relying on publicly available law enforcement press releases. The aim was to build a dataset of large-scale criminal cases involving PMLs, with a particular focus on the fees charged for their services. To this end, we systematically analyzed press releases issued by the Offices of the United States Attorneys in the United States from January 2007 to December 2024. While these press releases lack the granular detail of internal police files, they allow for large-scale data collection, a crucial factor in understanding the economic dimensions of professional money laundering.\u003c/p\u003e\n\u003cp\u003eThe United States was selected as the case study for three main reasons. First, U.S. authorities have emphasized the importance of tracking the pricing of money laundering services since the 2002 National Money Laundering Strategy (U.S. Treasury, 2002), making the country particularly relevant for collecting evidence on professional money laundering services. Second, the openness of the U.S. judicial system provides unique access to case information, a level of transparency often unavailable in other jurisdictions (Roks et al., 2022). Third, the United States has played a dominant role in shaping global anti-money laundering frameworks and has been at the forefront of investigating and prosecuting money laundering cases. Additionally, focusing on a single country enhances comparability across cases by ensuring consistency within the legal and geographical context. While variations in enforcement priorities within U.S. law enforcement agencies are acknowledged, this approach minimizes the variability caused by differences in legal definitions and anti-money laundering frameworks across jurisdictions (Matanky-Becker \u0026amp; Cockbain, 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Collection Process\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo construct the dataset, we conducted a systematic analysis of press releases issued by the Offices of the United States Attorneys between January 2007 and December 2024.\u003csup\u003e1\u003c/sup\u003e These press releases are typically published when criminal cases result in arrests, indictments, convictions, or sentences and often include relevant information, such as the fees charged for professional money laundering services. Data collection involved retrieving and classifying information from the press releases published on the official website, including titles, content, and links, while adhering to ethical guidelines.\u003c/p\u003e\n\u003cp\u003eThe Offices of the United States Attorneys has published a total of 253,497 press releases spanning the study period. To identify potentially relevant cases, we conducted a keyword search for the term \u0026ldquo;money laundering\u0026rdquo; within the title or body of each release. This yielded 19,809 press releases (approximately 8% of the total). Each of these was manually read and reviewed to determine its eligibility for inclusion in the analysis.\u003c/p\u003e\n\u003cp\u003eA case was included if it explicitly involved at least one PML\u0026mdash;defined as individuals or groups offering money laundering services for a commission fee while knowingly handling illicit proceeds (Kramer et al., 2023; Malm \u0026amp; Bichler, 2013). PMLs were distinguished from self-launderers or informal facilitators, who may launder money as a favor for social contacts rather than as a professional service (Caulkins \u0026amp; Reuter, 2022). To ensure analytical consistency and focus on structured, large-scale operations, we applied a minimum threshold of $100,000 in total illicit proceeds associated with the broader criminal investigation. While this threshold is inherently somewhat arbitrary, it was informed by the distribution of case values in the broader dataset: a large share of the relevant cases involved proceeds well above $100,000. As a result, the cutoff did not lead to the exclusion of a significant number of potentially relevant cases. Instead, it allowed us to retain a sufficiently large and analytically meaningful sample. Additionally, cases were included only if they provided a clearly stated fee amount for the laundering service, rather than vague or general references to the existence of a payment.\u003c/p\u003e\n\u003cp\u003eThe manual review process identified 143 eligible press releases. Since multiple press releases can relate to the same case\u0026mdash;particularly when arrests, indictments, and convictions occur at different stages\u0026mdash;these were consolidated into single entries to avoid duplication, resulting in 125 unique cases. Of these, 30 cases (24%) were excluded due to missing fee information, and an additional 12 cases (10%) were excluded due to missing information on the total proceeds connected to the case or because the proceeds were below $100,000. This resulted in a final sample of 90 large-scale professional money laundering cases investigated between 2007 and 2024.\u003c/p\u003e\n\u003cp\u003eTo enhance data quality, key case details\u0026mdash;such as defendant names and specific circumstances\u0026mdash;were triangulated with other open-source materials. Where available, additional court documents, including complaints, indictments, and judgments, were retrieved from platforms like Court Listener.\u003csup\u003e2\u003c/sup\u003e Supplementary information from media reports and other online sources enriched 54% of the cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMethod of analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe resulting dataset was analyzed using a mixed-methods approach. A quantitative content analysis was performed to extract structured information, including the amount of illicit proceeds, predicate offenses, the number of PMLs involved, money laundering methods, and the fees charged for services. This quantitative analysis was complemented by a qualitative exploration of selected cases to gain a deeper understanding of the techniques used by PMLs. Court documents, where available, provided direct evidence, such as quotes from wiretaps, affidavits, and other case materials, offering richer insights into PML operations.\u003c/p\u003e"},{"header":"5. Results","content":"\u003cp\u003e \u003cb\u003eKey characteristics of professional money laundering large-scale cases in our sample\u003c/b\u003e \u003c/p\u003e \u003cp\u003e Table\u0026nbsp;1 provides an overview of the key characteristics of the cases included in the sample. The total amount of illegal proceeds associated with the investigations of our 90 large-scale cases exceeds \u003cspan\u003e$\u003c/span\u003e1.7\u0026nbsp;billion. Among the 90 large-scale cases, 49% involve amounts ranging from \u003cspan\u003e$\u003c/span\u003e100,000 to \u003cspan\u003e$\u003c/span\u003e1\u0026nbsp;million, 29% from over \u003cspan\u003e$\u003c/span\u003e1\u0026nbsp;million to \u003cspan\u003e$\u003c/span\u003e10\u0026nbsp;million, and 22% exceeding \u003cspan\u003e$\u003c/span\u003e10\u0026nbsp;million. We chose to focus on large-scale cases, applying an exclusion criterion for cases below \u003cspan\u003e$\u003c/span\u003e100,000, to reduce variability and inconsistencies that could dilute the overall findings. Narrowing the sample helps maintain a clear focus on patterns relevant to large-scale operations. It is often not possible to definitively link reported commission fees to specific laundered amounts. For instance, a press release may state that a criminal group laundered \u003cspan\u003e$\u003c/span\u003e10\u0026nbsp;million and separately mention a 9% fee. However, such a fee may apply only to an individual transaction, and assuming it reflects the entire amount would be speculative\u0026mdash;and potentially misleading. Accordingly, we refrain from calculating precise revenue shares or inferring discount patterns (e.g., lower commission fees for higher volumes) based on these figures. \u003c/p\u003e \u003cp\u003eThe cases cover several predicate offences. The largest share (34%) involved the laundering of illicit proceeds originating from drug trafficking, followed by fraud (e.g., investment frauds) (22%), cybercrime (10%), and other predicate offences (18%), such as human trafficking, corruption and counterfeiting. Of note, PMLs handled illicit proceeds originating from multiple predicate offences only in 10% of the cases. This result seems to suggest that PMLs may specialize in handling illicit proceeds stemming from just a single predicate offence, probably to take full advantage of situational factors that may facilitate their activities, such as the ownership of cash-intensive legitimate businesses that well suit cash proceeds. In the remaining cases (6%), it was not possible to identify the underlying predicate offence from the available case materials.\u003c/p\u003e \u003cp\u003eThe number of PMLs involved in each case varies between 1 and 24, with a mean of 2.6. The PMLs appear to primarily work alone or in pairs, with larger professional money laundering networks being relatively rare. Of note, in 47% of the cases (42), PMLs held a legitimate job. Contrary to what previous literature suggests, the results indicate that PMLs do not necessarily require a legitimate occupation to facilitate money laundering; instead, they may leverage skills and expertise in specific financial niches (e.g., peer-to-peer cryptocurrency exchange). However, having a legitimate occupation certainly acts as a facilitator, as PMLs can more easily conceal illicit proceeds behind the legitimacy provided by their businesses or professions. Results showed that, out of the 42 cases involving PMLs with a legitimate occupation, 29% involved business owners, followed by lawyers (21%), check cashiers (14%) and other professionals (36%), such as bankers, accountants and real estate agents.\u003c/p\u003e \u003cp\u003eWe want to highlight that 44% of the cases in our sample involved an undercover agent acting as an offender attempting to engage a money launderer. Sting operations are designed to build criminal charges against money launderers. As a result, it is important to consider potential overestimations in prices compared to typical criminal transactions, as undercover agents may \u0026ldquo;not exhibit the same opportunistic bargaining behavior as regular customers\u0026rdquo; (Moeller, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, p. 37). In other words, undercover agents are more likely to accept the first commission fee proposed by the PML, as their primary objective is not to negotiate the best price but to complete the transactions in order to gather evidence for prosecution in court.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable 1. Key characteristics of professional money laundering cases, aggregated 2007\u0026ndash;2024\u003c/b\u003e \u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"616\" height=\"446\"\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: Authors\u0026rsquo; elaboration\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e highlights that this trend is only noticeable in the case of commissions ranging from 11\u0026ndash;15%, but these cases represent a minority of our sample (20%). Overall, the data suggests that undercover operations are more likely to result in lower-to-moderate fees, in contrast to real criminal transactions, which exhibit a wider range of fees, with offenders more frequently engaging in higher-fee transactions. This suggests that there is no major issue with the fee distribution in undercover operations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCommission fees of money laundering services\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOverall, the 90 cases in the sample entail a median commission fee of 9% of the total amount to be laundered (mean commission fee\u0026thinsp;=\u0026thinsp;12%).\u003csup\u003e3\u003c/sup\u003e Most of the cases (68%) involve a commission fee lower or equal to 10%. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that commission fees charged for money laundering services vary across predicate offences. Cybercrime is the costliest predicate offence with 25% as median commission fee (mean\u0026thinsp;=\u0026thinsp;30%), followed by drug trafficking (median\u0026thinsp;=\u0026thinsp;7%, mean\u0026thinsp;=\u0026thinsp;8%) and fraud (median\u0026thinsp;=\u0026thinsp;7%, mean\u0026thinsp;=\u0026thinsp;10%). A Kruskal-Wallis test confirmed that there is a statistically significant difference between the three predicate offense groups (H\u0026thinsp;=\u0026thinsp;15.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003csup\u003e4\u003c/sup\u003e This means the fees across these offense types are not distributed similarly. Mann-Whitney U tests to determine which specific groups differ from each other: cybercrime has significantly higher fees compared to both drug trafficking (z = -3.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and fraud (z = -3.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eIt is important to note that determining whether the commission fee covers a full-service money laundering operation\u0026mdash;i.e., managing illicit proceeds from placement to integration\u0026mdash;can be difficult. Some PMLs, however, appear to offer more targeted services. For example, in case 15, a PML charged a fee ranging from 45\u0026ndash;62% of the total amount laundered for providing \u0026ldquo;cash-out services\u0026rdquo; to members of a criminal organization involved in stealing bank and credit card accounts, exchanging the illicit proceeds for cash. In another case, the U.S. Department of Justice highlighted in the press release that a lawyer received a 5% fee, despite not offering actual money laundering services to his client. Instead, the lawyer merely facilitated the transfer of illicit proceeds from a Hong Kong bank account to several accounts in New York. From an analytical standpoint, these activities do not qualify as money laundering, as they do not obscure the illicit origin or integrate the proceeds into the legitimate financial system.\u003c/p\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThe present study is based on the exploratory analysis of 90 criminal investigations involving PMLs who were investigated in the United States from 2007 to 2024. Overall, the results showed that PMLs in the United States charge customers a median fee of 9% of the total amount to be laundered (mean 12%) for processing their illicit proceeds. A significant difference exists in the prices charged across different predicate offences. These findings merit further discussion, as they may offer valuable insights into the effectiveness of the current anti-money laundering regime.\u003c/p\u003e \u003cp\u003e The impact of national and international controls on money laundering activities seems to emerge from the case materials. PMLs in the sample appear to be aware of potential detection and arrest risks they encounter while conducting their illicit activities and, as a result, they demand high fees for their services. This clearly stems from case 29 where a PML offered an undercover agent to launder \u003cspan\u003e$\u003c/span\u003e100,000 of alleged drug proceeds in exchange for a 9% commission fee. The undercover agent tried to negotiate the fee, but the PML promptly answered: \u0026ldquo; \u003cem\u003eat some point it becomes, you know, the risk starts to overtake, the, umm reward, so to speak\u003c/em\u003e \u0026rdquo;. Likewise, in case 31, two financial advisors at an offshore investment firm openly admitted to routinely charging higher fees to clients seeking money laundering services compared to those involved in tax evasion, citing the greater risks associated with laundering criminal proceeds. \u003c/p\u003e \u003cp\u003eAdditionally, the structure of the criminal market appears to influence the commission rates charged by PMLs. For example, the indictment of case 80 includes a wiretapped conversation in which offenders discussed the 20% fee requested by a PML: \u0026ldquo;\u003cem\u003eShe says everybody brokering right now is paying 15. That\u0026rsquo;s what she thinks. That everybody pays cash brokers 15. [\u0026hellip;] She says the market is saturated right know\u003c/em\u003e\u0026rdquo;. In case 69, a PML remarked: \u0026ldquo;\u003cem\u003eThey should not mind to pay 15%, which is the going rate. That\u0026rsquo;s how much it costs\u003c/em\u003e\u0026rdquo;. These examples suggest that commission rates are not only influenced by risk, but also by market dynamics, including competition and supply-demand conditions within the illicit financial market.\u003c/p\u003e \u003cp\u003eThe potential effectiveness of anti-money laundering controls may also explain both the limited scope and complexity of the money laundering schemes set up by the PMLs. PMLs may prefer only providing specialized services in one stage of the money laundering process without overseeing and managing the entire scheme to limit their exposure and facilitate the successful outcome of their activities. As a result, offenders may have to rely on multiple PMLs to complete the laundering process \u0026ndash; thus paying multiple fees and bearing a higher overall cost \u0026ndash; or take the risk by spending proceeds that are not fully laundered.\u003c/p\u003e \u003cp\u003eBy the same reasoning, variations in commission fees across predicate offences may also be attributed to differences in enforcement scrutiny. This idea is not new; Levi and Reuter (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) already suggested the existence of multiple markets for money laundering services depending on the predicate offences, with some of them involving higher-percentage payments, as PMLs would face a greater risk of investigation from law enforcement and more serious penalties. The perception of different enforcement risks linked with various predicate offences seems evident in the case materials. For example, the PML in case 34 charged a 10% commission fee to cash healthcare fraud and mortgage fraud checks but a 30% commission fee for cashing checks related to identity theft tax refund fraud. Finally, in case 17, when introducing an offender to a PML, a co-conspirator advised him to claim that the money was from corruption rather than drug trafficking, as a strategy to reduce money laundering fees.\u003c/p\u003e \u003cp\u003eThe relatively high commission fees for cybercrime proceeds may reflect the need to eliminate the digital traces that inevitably link the proceeds to their illicit origin. These proceeds are often in digital form \u0026ndash; either in bank accounts or cryptocurrencies - and, potentially, may require more complex schemes and technical skills compared to other predicate offences. Simultaneously, cybercrimes have emerged as a significant threat over the last decade, driven by the substantial economic and emotional toll they impose on their victims. The fight against cybercrime has swiftly risen to the top of political agendas worldwide, attracting increased scrutiny and resource allocation (Caneppele \u0026amp; da Silva, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, it is important to note that cybercrime cases account for only about 10% of the sample, a relatively small proportion compared to offence types such as drug-related cases, which represent approximately 34%. This difference in representation may contribute to greater variability in the average commission fees observed for cybercrime proceeds, and could, to some extent, affect the robustness of conclusions drawn for this category. Nevertheless, the consistently higher fees identified in these cases align with the expectation that laundering digital assets may require more technically sophisticated methods and greater risk exposure. While further research with larger samples is needed to confirm these patterns, the findings suggest that cybercrime-related laundering may indeed operate under distinct market conditions.\u003c/p\u003e \u003cp\u003eOverall, caution is required when drawing conclusions about the effectiveness of the anti-money laundering system. This study did not attempt a temporal analysis, primarily due to the limited sample size over an extended period, which makes it difficult to reliably track changes or trends in pricing of money laundering services over time. Additionally, the only prior reference for money laundering service prices in the United States is a study reported in the 2002 U.S. National Money Laundering Strategy, which indicated that fees for laundering services ranged from 4\u0026ndash;8%, with a high of 12%. However, this figure is based on anecdotal evidence, as neither the number nor the type of cases underlying the estimate are publicly available. As such, it is not possible to conclude that there has been an increase or decrease in the price of money laundering services in the United States.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe present paper has some limitations that warrant further discussion. First, anti-money laundering enforcement targets both the demand and supply sides (Levi \u0026amp; Reuter, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). While supply-side efforts directed at PMLs should raise the price of money laundering services, demand-side efforts against customers have the opposite effect by reducing demand. Both enforcement actions aim to decrease the volume of laundering and the net returns from crime. However, it is worth noting that prices can only be interpreted along with estimates of quantity of money laundering services which are difficult to obtain.\u003c/p\u003e \u003cp\u003eSecond, the study suffers from limited external validity because it uses a non-exhaustive sample of professional money laundering large-case cases in the United States. There is no centralized database in which all professional money laundering cases in the United States are stored and from which a random sample could be drawn. Rather, cases were identified through a purposive sampling as they were selected based on their availability and the economic value of the illicit proceeds of the entire investigation. The non-probability selection of the sample means that these cases are not representative of all large-scale professional money laundering cases in the United States. Despite efforts to collect as many cases as possible to mitigate the negative consequences of this sampling approach, it is important to acknowledge this limitation when interpreting the results.\u003c/p\u003e \u003cp\u003eThird, the use of law enforcement data further limits the scope of the analysis. Although the investigative methods used by LEAs provide an exclusive glimpse into criminal activities, this type of data only includes criminal cases that matched the scope and resources of the LEAs and, not secondary, were targeted in successful investigations (Roks et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, it is still not clear to what extent detected money laundering cases are representative of the population (Levi \u0026amp; Soudijn, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, one could reasonably argue that \u0026ldquo;failed\u0026rdquo; PMLs\u0026mdash;those who were caught\u0026mdash;differ significantly from undetected ones, precisely because they were detected and arrested. A key implication of this perspective is that PMLs who have been arrested might display lower levels of sophistication and impose lower commission fees compared to their more elusive counterparts, simply because they were not skilled enough to avoid capture.\u003c/p\u003e \u003cp\u003eFourth, the information included in the cases may also be incomplete as law enforcement data is not naturally meant for research purposes. For example, out of 125 professional money laundering cases identified, 90 (72%) cases reported the information on the fee charged and were included in the sample for the analysis. Furthermore, it is worth noting that 26% of the cases (23) were not final convictions or sentences. On-going criminal proceedings are a good trade-off between the solidity of the evidence and the topicality of the case for research purposes (Roks et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, it is important to note that the charges in these cases are merely allegations, and the defendants are presumed innocent unless and until proven guilty.\u003c/p\u003e \u003cp\u003eFinally, 44% of the cases (40) involved undercover police officers purchasing money laundering services. The primary objective of sting operations is to gather sufficient evidence to prosecute money launderers. Undercover agents, however, may not negotiate fees as aggressively as typical clients because their focus is on completing the transaction to build a solid case, rather than securing the best deal. In our sample, this potential issue of inflated fees is mostly limited to a few cases with relatively high fees, which account for only a minor share of the total cases analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"7. Conclusions, policy implications and directions for future research","content":"\u003cp\u003eDespite the above-mentioned limitations, the present study is the first exploratory analysis on an under-researched topic, and it has relevant research and policy implications. First, measuring this cost component is a necessary step in developing a reliable estimate of the overall cost that offenders pay to launder their illicit proceeds. Given the lack of hard data on money laundering, collecting price information may represent a promising avenue for measuring the effectiveness of anti-money laundering policies. However, more granular and systematic data are needed for this purpose. To date, PMLs can still be overlooked during a criminal investigations when, for different reasons (e.g., budget constraints), LEAs do not prioritize money laundering and rather focus on the predicate offence (Levi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Levi \u0026amp; Soudijn, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Soudijn, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Conversely, LEAs need to adopt a financial approach from the very beginning of a criminal investigation and pay more attention to the financial side of the criminal activities they are investigating (Kramer et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Roks et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Soudijn, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Such an effort would not only be relevant from a research point of view but also have practical implications for prosecutions. For example, assessing if legitimate professionals (e.g., lawyers) involved in a criminal investigation have charged a premium commission fee to their clients could help demonstrate, beyond reasonable doubt, their knowledge of the illicit origin of the funds and their involvement in money laundering services (Levi \u0026amp; Soudijn, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, scholars should strive to explore other data sources to overcome the above-mentioned limitations associated with law enforcement data. For example, interviewing PMLs may provide detailed \u0026ndash; and unfiltered \u0026ndash; insights on price dynamics and how anti-money laundering controls impact their activities. Despite the significant challenges in convincing offenders to discuss their money laundering activities (see, for a review, Levi \u0026amp; Soudijn, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), insights from their perspective on which controls effectively hinder their activities could be invaluable for identifying both effective measures and areas needing improvement (see, for example, Berry et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another promising research avenue is the analysis of supply-demand interactions related to money laundering services in online environments (e.g., darknet marketplaces and forums), which would allow for large-scale price data collection and support more advanced statistical analyses (Kruisbergen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore broadly, future academic research should focus on analyzing the key characteristics of the market for money laundering services. To date, most empirical efforts have focused on assessing the prevalence of PMLs in money laundering activities (see for example Malm \u0026amp; Bichler, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Soudijn, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While the demand for facilitators may logically vary depending on the scale and nature of the crime proceeds (see, for a review Levi, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), there is still minimal evidence on the characteristics of the supply side of money laundering services. Important questions remain unanswered, such as what drives variation in laundering fees, how PMLs build and maintain trust with clients, and how disputes or defaults are managed in the absence of legal enforcement mechanisms. Investigating these aspects would not only deepen our understanding of how PMLs operate but also help design more targeted and disruptive AML interventions. Ultimately, recognizing and analyzing professional money laundering as a service market\u0026mdash;with its own economic logic, pricing structures, and organizational forms\u0026mdash;could mark a significant shift in both academic research and policy effectiveness.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used ChatGPT in order to edit the language of the manuscript. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.N. developed the initial conceptual framework and conducted the literature review. The framework was further refined in collaboration with S.F. M.N. collected and analyzed the data, with analytical input from S.F. M.N. produced the results. The manuscript was written by M.N., with support from S.F., who also reviewed and edited the full draft. Both authors revised the manuscript based on reviewer feedback.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Alberto Aziani for his valuable comments on an early draft of this manuscript. They also wish to express their sincere appreciation to the participants of the 79th Annual Conference of the American Society of Criminology (ASC), held in San Francisco in November 2024, for their insightful feedback and suggestions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAziani, A., Berlusconi, G., \u0026amp; Giommoni, L. (2021). A Quantitative Application of Enterprise and Social Embeddedness Theories to the Transnational Trafficking of Cocaine in Europe. \u003cem\u003eDeviant Behavior\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(2), 245\u0026ndash;267. https://doi.org/10.1080/01639625.2019.1666606\u003c/li\u003e\n\u003cli\u003eBenson, K. (2018). Money Laundering, Anti-Money Laundering and the Legal Profession. In C. King, C. Walker, \u0026amp; J. Gurul\u0026eacute; (Eds.), \u003cem\u003eThe Palgrave Handbook of Criminal and Terrorism Financing Law\u003c/em\u003e (pp. 109\u0026ndash;133). Springer International Publishing. https://doi.org/10.1007/978-3-319-64498-1_6\u003c/li\u003e\n\u003cli\u003eBerry, M., Salinas, M., \u0026amp; Gundur, R. V. (2023). Financial risk management strategies of small to medium illicit drug enterprises: Considering low-level money laundering. \u003cem\u003eTrends in Organized Crime\u003c/em\u003e. https://doi.org/10.1007/s12117-023-09501-5\u003c/li\u003e\n\u003cli\u003eBoivin, R. (2014). Risks, prices, and positions: A social network analysis of illegal drug trafficking in the world-economy. \u003cem\u003eInternational Journal of Drug Policy\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(2), 235\u0026ndash;243. https://doi.org/10.1016/j.drugpo.2013.12.004\u003c/li\u003e\n\u003cli\u003eBright, D. A., \u0026amp; Ritter, A. (2010). Retail price as an outcome measure for the effectiveness of drug law enforcement. \u003cem\u003eInternational Journal of Drug Policy\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(5), 359\u0026ndash;363. https://doi.org/10.1016/j.drugpo.2009.12.001\u003c/li\u003e\n\u003cli\u003eCaneppele, S., \u0026amp; da Silva, A. (2022). Cybercrime. In D. Nelken \u0026amp; C. Hamilton (Eds.), \u003cem\u003eResearch Handbook of Comparative Criminal Justice\u003c/em\u003e. Edward Elgar Publishing. https://doi.org/10.4337/9781839106385\u003c/li\u003e\n\u003cli\u003eCaulkins, J. P., \u0026amp; Reuter, P. (2010). How Drug Enforcement Affects Drug Prices. \u003cem\u003eCrime and Justice\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(1), 213\u0026ndash;271. https://doi.org/10.1086/652386\u003c/li\u003e\n\u003cli\u003eCaulkins, J. P., \u0026amp; Reuter, P. (2022). How much demand for money laundering services does drug selling create? Identifying the key parameters. \u003cem\u003eInternational Journal of Drug Policy\u003c/em\u003e, \u003cem\u003e103\u003c/em\u003e, 103652. https://doi.org/10.1016/j.drugpo.2022.103652\u003c/li\u003e\n\u003cli\u003eEuropol. (2023). \u003cem\u003eEuropean Financial and Economic Crime Threat Assessment 2023\u0026mdash;The Other Side of the Coin: An Analysis of Financial and Economic Crime\u003c/em\u003e. Publications Office of the European Union.\u003c/li\u003e\n\u003cli\u003eFarf\u0026aacute;n-M\u0026eacute;ndez, C. (2019). The Structure of Drug Trafficking Organizations and Money Laundering Practices: A Risk Appetite Hypothesis. \u003cem\u003eJournal of Illicit Economies and Development\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(3), 294\u0026ndash;311. https://doi.org/10.31389/jied.1\u003c/li\u003e\n\u003cli\u003eFATF. (2018). \u003cem\u003eProfessional Money Laundering\u003c/em\u003e. FATF. https://www.fatf-gafi.org/en/publications/Methodsandtrends/Professional-money-laundering.html\u003c/li\u003e\n\u003cli\u003eFerwerda, J. (2018). The Effectiveness of Anti-Money Laundering Policy: A Cost-Benefit Perspective. In C. King, C. Walker, \u0026amp; J. Gurul\u0026eacute; (Eds.), \u003cem\u003eThe Palgrave Handbook of Criminal and Terrorism Financing Law\u003c/em\u003e (pp. 317\u0026ndash;344). Springer International Publishing. https://doi.org/10.1007/978-3-319-64498-1_14\u003c/li\u003e\n\u003cli\u003eFerwerda, J., \u0026amp; Reuter, P. (2019). Learning from Money Laundering National Risk Assessments: The Case of Italy and Switzerland. \u003cem\u003eEuropean Journal on Criminal Policy and Research\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 5\u0026ndash;20. https://doi.org/10.1007/s10610-018-9395-0\u003c/li\u003e\n\u003cli\u003eGathmann, C. (2008). Effects of enforcement on illegal markets: Evidence from migrant smuggling along the southwestern border. \u003cem\u003eJournal of Public Economics\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e(10\u0026ndash;11), 1926\u0026ndash;1941. https://doi.org/10.1016/j.jpubeco.2008.04.006\u003c/li\u003e\n\u003cli\u003eGerbrands, P., Unger, B., Getzner, M., \u0026amp; Ferwerda, J. (2022). The effect of anti-money laundering policies: An empirical network analysis. \u003cem\u003eEPJ Data Science\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 15. https://doi.org/10.1140/epjds/s13688-022-00328-8\u003c/li\u003e\n\u003cli\u003eHalliday, T., Levi, M., \u0026amp; Reuter, P. (2019). Anti-Money Laundering: An Inquiry into a Disciplinary Transnational Legal Order. \u003cem\u003eUC Irvine Journal of International, Transnational, and Comparative Law\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1).\u003c/li\u003e\n\u003cli\u003eHalliday, T., Levi, M., \u0026amp; Reuter, P. (2020). Why Do Transnational Legal Orders Persist?: The Curious Case of Money-Laundering Controls. In G. Shaffer \u0026amp; E. Aaronson (Eds.), \u003cem\u003eTransnational Legal Ordering of Criminal Justice\u003c/em\u003e (1st ed., pp. 51\u0026ndash;83). Cambridge University Press. https://doi.org/10.1017/9781108873994.002\u003c/li\u003e\n\u003cli\u003eImanpour, M., Rosenkranz, S., Westbrock, B., Unger, B., \u0026amp; Ferwerda, J. (2019). A microeconomic foundation for optimal money laundering policies. \u003cem\u003eInternational Review of Law and Economics\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e, 105856. https://doi.org/10.1016/j.irle.2019.105856\u003c/li\u003e\n\u003cli\u003eKramer, J.-A., Blokland, A. A. J., Kleemans, E. R., \u0026amp; Soudijn, M. R. J. (2023). Money laundering as a service: Investigating business-like behavior in money laundering networks in the Netherlands. \u003cem\u003eTrends in Organized Crime\u003c/em\u003e. https://doi.org/10.1007/s12117-022-09475-w\u003c/li\u003e\n\u003cli\u003eKruisbergen, E. W., Leukfeldt, E. R., Kleemans, E. R., \u0026amp; Roks, R. A. (2019). Money talks money laundering choices of organized crime offenders in a digital age. \u003cem\u003eJournal of Crime and Justice\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(5), 569\u0026ndash;581. https://doi.org/10.1080/0735648X.2019.1692420\u003c/li\u003e\n\u003cli\u003eLevi, M. (2018). Reflections on Proceeds of Crime: A New Code for Confiscation? In J. Child \u0026amp; A. Duff (Eds.), \u003cem\u003eCriminal Law Reform Now\u003c/em\u003e. Hart.\u003c/li\u003e\n\u003cli\u003eLevi, M. (2020). Evaluating the Control of Money Laundering and Its Underlying Offences: The Search for Meaningful Data. \u003cem\u003eAsian Journal of Criminology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 301\u0026ndash;320. https://doi.org/10.1007/s11417-020-09319-y\u003c/li\u003e\n\u003cli\u003eLevi, M. (2021). Making sense of professional enablers\u0026rsquo; involvement in laundering organized crime proceeds and of their regulation. \u003cem\u003eTrends in Organized Crime\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 96\u0026ndash;110. https://doi.org/10.1007/s12117-020-09401-y\u003c/li\u003e\n\u003cli\u003eLevi, M. (2022). Lawyers as money laundering enablers? An evolving and contentious relationship. \u003cem\u003eGlobal Crime\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(2), 126\u0026ndash;147. https://doi.org/10.1080/17440572.2022.2089122\u003c/li\u003e\n\u003cli\u003eLevi, M., \u0026amp; Reuter, P. (2006). Money Laundering. \u003cem\u003eCrime and Justice\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e, 289\u0026ndash;375. https://doi.org/10.1086/501508\u003c/li\u003e\n\u003cli\u003eLevi, M., Reuter, P., \u0026amp; Halliday, T. (2018). Can the AML system be evaluated without better data? \u003cem\u003eCrime, Law and Social Change\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e(2), 307\u0026ndash;328. https://doi.org/10.1007/s10611-017-9757-4\u003c/li\u003e\n\u003cli\u003eLevi, M., \u0026amp; Soudijn, M. (2020). Understanding the Laundering of Organized Crime Money. \u003cem\u003eCrime and Justice\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 579\u0026ndash;631. https://doi.org/10.1086/708047\u003c/li\u003e\n\u003cli\u003eMalm, A., \u0026amp; Bichler, G. (2013). Using friends for money: The positional importance of money-launderers in organized crime. \u003cem\u003eTrends in Organized Crime\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4), 365\u0026ndash;381. https://doi.org/10.1007/s12117-013-9205-5\u003c/li\u003e\n\u003cli\u003eManski, C. F., Pepper, J., \u0026amp; Petrie, C. (2001). \u003cem\u003eInforming America\u0026rsquo;s policy on illegal drugs: What we don\u0026rsquo;t know keeps hurting us\u003c/em\u003e (National Research Council (U.S.) \u0026amp; National Research Council (U.S.), Eds.). National Academy Press.\u003c/li\u003e\n\u003cli\u003eMasciandaro, D. (1998). Money Laundering Regulation: The Micro Economics. \u003cem\u003eJournal of Money Laundering Control\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 49\u0026ndash;58. https://doi.org/10.1108/eb027170\u003c/li\u003e\n\u003cli\u003eMasciandaro, D. (2007). Economics of Money Laundering: A Primer. \u003cem\u003eSSRN Electronic Journal\u003c/em\u003e. https://doi.org/10.2139/ssrn.970184\u003c/li\u003e\n\u003cli\u003eMasciandaro, D., \u0026amp; Barone, R. (2008). Worldwide Anti-Money Laundering Regulation: Estimating Costs and Benefits. \u003cem\u003eSSRN Electronic Journal\u003c/em\u003e. https://doi.org/10.2139/ssrn.1136107\u003c/li\u003e\n\u003cli\u003eMatanky-Becker, R., \u0026amp; Cockbain, E. (2021). Behind the criminal economy: Using UK tax fraud investigations to understand money laundering myths and models. \u003cem\u003eCrime, Law and Social Change\u003c/em\u003e. https://doi.org/10.1007/s10611-021-09997-4\u003c/li\u003e\n\u003cli\u003eMcCarthy, K. J., van Santen, P., \u0026amp; Fiedler, I. (2015). Modeling the money launderer: Microtheoretical arguments on anti-money laundering policy. \u003cem\u003eInternational Review of Law and Economics\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e, 148\u0026ndash;155. https://doi.org/10.1016/j.irle.2014.04.006\u003c/li\u003e\n\u003cli\u003eMoeller, K. (2012). Costs and revenues in street-level cannabis dealing. \u003cem\u003eTrends in Organized Crime\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 31\u0026ndash;46. https://doi.org/10.1007/s12117-011-9146-9\u003c/li\u003e\n\u003cli\u003ePol, R. F. (2020). Anti-money laundering: The world\u0026rsquo;s least effective policy experiment? Together, we can fix it. \u003cem\u003ePolicy Design and Practice\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 73\u0026ndash;94. https://doi.org/10.1080/25741292.2020.1725366\u003c/li\u003e\n\u003cli\u003eReuter, P., \u0026amp; Kleiman, M. A. R. (1986). Risks and Prices: An Economic Analysis of Drug Enforcement. \u003cem\u003eCrime and Justice\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 289\u0026ndash;340.\u003c/li\u003e\n\u003cli\u003eReuter, P., \u0026amp; Truman, E. (2004). \u003cem\u003eChasing dirty money: The fight against money laundering\u003c/em\u003e (Vol. 381). Institute for International Economics.\u003c/li\u003e\n\u003cli\u003eReuter, P., \u0026amp; Truman, E. (2005). Anti-Money Laundering Overkill? It\u0026rsquo;s time to ask how well the system is working. \u003cem\u003eThe International Economy\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 56\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eRoks, R. A., Kruisbergen, E. W., \u0026amp; Kleemans, E. R. (2022). Walls of silence and organized crime: A theoretical and empirical exploration into the shielding of criminal activities from authorities. \u003cem\u003eTrends in Organized Crime\u003c/em\u003e. https://doi.org/10.1007/s12117-022-09447-0\u003c/li\u003e\n\u003cli\u003eSharman, J. C. (2008). Power and Discourse in Policy Diffusion: Anti-Money Laundering in Developing States. \u003cem\u003eInternational Studies Quarterly\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(3), 635\u0026ndash;656. JSTOR.\u003c/li\u003e\n\u003cli\u003eSoudijn, M. (2012). Removing excuses in money laundering. \u003cem\u003eTrends in Organized Crime\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2\u0026ndash;3), 146\u0026ndash;163. https://doi.org/10.1007/s12117-012-9161-5\u003c/li\u003e\n\u003cli\u003eSoudijn, M. (2014). Using strangers for money: A discussion on money-launderers in organized crime. \u003cem\u003eTrends in Organized Crime\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(3), 199\u0026ndash;217. https://doi.org/10.1007/s12117-014-9217-9\u003c/li\u003e\n\u003cli\u003eSoudijn, M. (2024). Encounters with Professional Money Launderers; An Analysis of Financial Transactions as Reported by Gatekeepers. \u003cem\u003eEuropean Journal on Criminal Policy and Research\u003c/em\u003e. https://doi.org/10.1007/s10610-024-09588-8\u003c/li\u003e\n\u003cli\u003eSoudijn, M., \u0026amp; Reuter, P. (2016). Cash and carry: The high cost of currency smuggling in the drug trade. \u003cem\u003eCrime, Law and Social Change\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e(3), 271\u0026ndash;290. https://doi.org/10.1007/s10611-016-9626-6\u003c/li\u003e\n\u003cli\u003eU.S. Treasury. (2002). \u003cem\u003e2002 National Money Laundering Strategy\u003c/em\u003e. https://home.treasury.gov/system/files/136/archive-documents/monlaund.pdf\u003c/li\u003e\n\u003cli\u003evan Duyne, P. C. (2003). Organizing cigarette smuggling and policy making, ending up in smoke. \u003cem\u003eSpringer\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e, 285\u0026ndash;317.\u003c/li\u003e\n\u003cli\u003evan Duyne, P. C., Harvey, J. H., \u0026amp; Gelemerova, L. Y. (2018). \u003cem\u003eThe Critical Handbook of Money Laundering: Policy, analysis and myths\u003c/em\u003e. Palgrave Macmillan.\u003c/li\u003e\n\u003cli\u003eWilliamson, O. E. (1973). Markets and Hierarchies: Some Elementary Considerations. \u003cem\u003eAmerican Economic Review\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(2), 316\u0026ndash;325.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Press releases published before 2013 can be accessed at the following link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.justice.gov/archive/usao/\u003c/span\u003e\u003cspan address=\"https://www.justice.gov/archive/usao/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The Court Listener is a repository developed by the Free Law Project, a U.S.-registered non-profit organization. It includes free-accessible court documents that users have downloaded by the Public Access to Court Electronic Records (PACER), the electronic records system maintained by the Administrative Office of the U.S. Courts (AOUSC) and made available to the wider community.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e In the case of a variable commission fee, the mean value of all the prices reported in the case materials was considered.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The Kruskal-Wallis test can be used as a non-parametric alternative to ANOVA, making it suitable for cases where the data does not follow a normal distribution.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"trends-in-organized-crime","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tioc","sideBox":"Learn more about [Trends in Organized Crime](http://link.springer.com/journal/12117)","snPcode":"12117","submissionUrl":"https://submission.springernature.com/new-submission/12117/3","title":"Trends in Organized Crime","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6828794/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6828794/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe fight against money laundering has motivated the establishment of a far-reaching regime worldwide. Despite debate over its effectiveness, empirical evidence on the pricing of money laundering services is limited, primarily due to the dearth of data and information on money laundering. To address this knowledge gap, this study examines the pricing of professional money laundering services in the United States, drawing on an explorative sample of 90 large-scale criminal cases from January 2007 to December 2024. The findings reveal a median commission fee of 9% of the total amount to be laundered, with notable price variations across different types of predicate offences. Cybercrime stands out as the most expensive, with a median commission fee of 25%, followed by drug trafficking and fraud, both at 7%. Implications of the findings for both research and policy are discussed.\u003c/p\u003e","manuscriptTitle":"Name Your Price: Exploring the Costs of Professional Money Laundering Services in Large-Scale Cases in the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 14:41:42","doi":"10.21203/rs.3.rs-6828794/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-01T15:42:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-30T07:45:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227989149892321263152773912968946393383","date":"2025-06-16T08:45:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324393253471633376111649454872871906042","date":"2025-06-14T20:08:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-12T08:31:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-11T23:01:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-11T23:00:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Trends in Organized Crime","date":"2025-06-05T11:36:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"trends-in-organized-crime","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tioc","sideBox":"Learn more about [Trends in Organized Crime](http://link.springer.com/journal/12117)","snPcode":"12117","submissionUrl":"https://submission.springernature.com/new-submission/12117/3","title":"Trends in Organized Crime","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"335dcc16-d460-4cb1-a4ca-47b28331a429","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:07:05+00:00","versionOfRecord":{"articleIdentity":"rs-6828794","link":"https://doi.org/10.1007/s12117-025-09587-z","journal":{"identity":"trends-in-organized-crime","isVorOnly":false,"title":"Trends in Organized Crime"},"publishedOn":"2026-01-19 15:57:38","publishedOnDateReadable":"January 19th, 2026"},"versionCreatedAt":"2025-06-17 14:41:42","video":"","vorDoi":"10.1007/s12117-025-09587-z","vorDoiUrl":"https://doi.org/10.1007/s12117-025-09587-z","workflowStages":[]},"version":"v1","identity":"rs-6828794","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6828794","identity":"rs-6828794","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}