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How Advanced Technologies Can Fight Money Laundering by Drug Traffickers

Drug Trafficking Is Widespread

Drug trafficking is a global problem—and despite decades of research, regulations, and enforcement, it remains a $400 billion global business. Government ministries and agencies around the world continuously monitor and research illicit drug markets to gain a more comprehensive understanding of this nefarious industry—but the problem persists. 

One reason drug traffickers are hard to identify is that they are constantly adapting and evolving their tactics. For example, the shuttering of non-essential businesses due to COVID-19 has impacted a money-laundering system in North America known as the “black-market peso exchange.” With storefronts closed, drug trafficking groups are reverting to older, riskier ways of repatriating profits, like cash smuggling. Fortunately, last year, the U.S. Drug Enforcement Administration (DEA) made three seizures in Los Angeles’ fashion district―the exchange’s epicenter―that each netted more than $1 million in suspected drug proceeds that had piled up. 

Drug Trafficking Is Hard to Detect

Because drug trafficking is the top source of funds to be laundered (outpacing human trafficking, arms dealing, and the illegal wildlife trade), it is a prime focus within financial crime and compliance programs at financial institutions. The financial services industry has invested in innovative technology such as machine learning (ML), artificial intelligence (AI), and robotics process automation to combat the issue. However, despite tremendous dedication and resources, financial institutions continue to miss a significant amount of criminal activity related to drug trafficking. Why do gaps remain?

  • Lack of holistic network view: Transnational criminal organizations rely on intricate networks of specialized cells for drug distribution, transportation, consolidation of proceeds, and money laundering. These rings leverage tactics such as transferring funds to/from other countries or mirroring schemes, making it particularly challenging for financial institutions to gain a holistic view of the network.
  • International sprawl: The production, distribution, and consumption of illicit drugs spreads across the globe and involves deep-rooted criminal groups who often exploit international trade. Criminals can use illicit proceeds to buy goods for export; misrepresent the price, goods, or quality; and effectively launder the proceeds. The largely paper-based systems used in the trade finance business make this process challenging to detect.
  • Cash focus: Criminals use cash as an anonymous payment method, making it difficult to trace a specific sale, criminal activity, or method of laundering.
  • New payment methods: The global payments system is changing, and criminals are exploiting new technologies and networks, such as the darknet (an encrypted virtual network), that are altering the nature of the illicit drug trade and the types of players involved. For example, groups operating in virtual networks tend to have looser ties and to be organized in horizontal structures rather than the typical hierarchical structures. Studies have highlighted that smaller groups have become more significant.
  • Challenges with the synthetic drug market: Information on synthetic drug manufacturing is more limited than that available on plant-based drugs (cocaine, opiates, and cannabis). This is largely because synthetic drugs can be manufactured anywhere, as the process does not involve the extraction of active constituents from plants that have to be cultivated in certain conditions. The challenges in tracking synthetic drug production prevent accurate estimation of the volume of the corresponding market worldwide. Nevertheless, data on synthetic drug seizures and drug use suggest that the supply of synthetic drugs is expanding. 

Technology Can Transform the Detection of Money Laundering by Drug Traffickers

Graph analytics, combined with other technologies like machine learning and artificial intelligence, represents a new path forward in understanding the intricate patterns of money laundering associated with drug trafficking. These technologies can offer transformation on several fronts. Let’s use fentanyl production and distribution as an example.

  • Screening: All financial institutions screen prospects during the onboarding process. However, a typical single or limited attribute matching engine produces a high rate of false positives. A multi-dimension algorithm can increase the effectiveness of matching results by leveraging key entity information with external information such as media scans and UBO information. For example, while a specific drug trafficker may not appear on a sanctions list, that individual may be involved with a high-risk shell company or be the subject of negative news. This connection can be detected immediately, stopping the onboarding.
  • Client risk rating: Once a prospect is screened, all required information, including expected transaction volume, international activity, purpose, and source of income, is collected. This information helps determine the entity risk segment (high, medium, low) and anticipatory profile. If a prospect is expected to engage with high-risk countries associated with drug production, the financial institution may choose to ask for justification or to not onboard that person. Further, this information should be monitored on an ongoing basis and checked for discrepancies with the knowledge that was provided during onboarding.
  • Transaction filtering: Once the prospect is approved, transactions should be screened in real-time for activity with prohibited countries or counterparties. By leveraging a configurable screening methodology, financial institutions can identify potential drug traffickers based on embedded messages in the payment data or references on websites that mention illegal drug transactions using closed virtual currency (CVC) exchangers. Payments to any prohibited websites or CVC exchangers should be declined immediately. In the case of trade finance, financial institutions should screen the identified goods against prohibited, restricted, or dual-use goods to ensure the customer is not dealing in controlled substances (regulated under Controlled Substances Act) without a valid business justification.
  • Mass surveillance: A drug trafficking scheme is not a siloed activity—rather, it is a series of related activities that may appear unrelated. For example, in a typical fentanyl scheme, individuals frequently transfer funds using multiple Money Service Business (MSB) agent locations. Financial institutions should, therefore, move away from individual/red-flag monitoring toward mass surveillance―in other words, evolving from an alert/event-based investigation to a case-based investigation. Correlation of various events should happen based on shared information, such as telephone numbers, addresses, and more. This will allow analysts to see holistic entity activity instead of siloed events. Once comfortable with this concept, financial institutions should leverage graph analytics for event correlation, thereby uncovering hidden networks that might otherwise be missed. For example, drug trafficking rings will often recruit many people to engage in “micro structuring.” The hired people will make small ($500 - $1,000) cash deposits into ATMs in a region. Shortly thereafter, the money is withdrawn in another country. Graph analytics can help institutions find these accounts by identifying groups of similar patterns or identifying accounts that are funded only by small cash deposits. Graph analytics can also help find circular flows of funds, another typical design of money launderers.
  • Machine learning: Supervised ML using graph analytics can be a game-changer in increasing monitoring effectiveness. Detailed information gathering during onboarding is essential to identifying entities behaving outside “normal” activity. ML can be leveraged to uncover new patterns and schemes, identify instances of known criminal patters, and to score customer behaviors and non-behavioral attributes. For example, an individual transacting with a business in a high-risk country and making a high volume of virtual currency payments outside the “normal” behavior of its segment can be easily detected leveraging graph analytics and ML. Unsupervised machine learning should be applied to detect previously unmarked, unknown drug trafficking schemes.
  • Contextual investigation: Financial institutions can leverage powerful graph analytics to connect the dots between internal and external data, providing a holistic representation of networks that uncovers hidden patterns. Investigators can click through entities and their connections—represented as nodes on the graph model—to analyze networks and suspicious activities. For example, once detected by an ML model, investigators can expand the graph by bringing in external data, such as business registry information, to ensure that none of the ultimate beneficiary owners are designated drug traffickers or have negative news on them.
  • Collective intelligence learning: AI can be leveraged to enhance human expertise through recommendations and next-best actions while also helping analysts gain situational awareness and learn institutional best practices. Once detected by an ML model and designated by the investigator as a true positive, previously detected organized drug trafficking cases can be leveraged to make recommendations for new evidence in a graph. This way, institutions can be ensuring collective learning.
  • Natural language processing (NLP) narrative: Leveraging investigation outcomes, NLP can generate automatic case narratives, eliminating the manual component, reducing investigation times, and avoiding human errors. Case narratives are vital for the prosecution of suspect entities—and can help financial institutions improve investigation quality and reduce drug crime. NLP can be beneficial in ensuring the concluded drug-trafficking case has been well documented (in the case narrative) to assist law enforcement in prosecuting these highly sophisticated criminal rings.
  • Sentiment analysis: Sentiment analysis can help identify and extract opinions from suspicious activity reports, leveraging text across blogs, social media, media scans, and more. Sentiment analysis findings should feedback into the system to uncover unknown behaviors and provide case recommendations for quick decisioning.
  • Entity recognition and resolution: Graph matching can provide a holistic view of all the matched entities by various attributes, such as name, address, or email. This unifies data in real-time to create a single entity view across the enterprise. Entity resolution can be applied to resolve different entities based on shared addresses, phone numbers, tax IDs, or names—key indicators to identify a drug ring network. In many cases, criminals use aliases or pseudonyms, which can be resolved by bringing internal and external data together for a consistent entity definition. Specifically, this is useful when criminals are using different products and services to “layer” within one institution.

The Time to Act Is Now

The problem of money laundering by drug traffickers has continued to grow in recent years, with worldwide drug use increasing 30% between 2009 and 2018. Fortunately, recent advances in technology are giving financial institutions the tools they need to win this fight. As a result, they keep themselves, their customers, and our world safer.


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