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Forced labor

Lessons learned from the ACAMS/³ÉÈËVRÊÓÆµ Human Trafficking Initiative at the World Cup

Ingo Steinhaeuser  Senior Risk and Fraud Specialist / ³ÉÈËVRÊÓÆµ

· 5 minute read

Ingo Steinhaeuser  Senior Risk and Fraud Specialist / ³ÉÈËVRÊÓÆµ

· 5 minute read

Financial institutions can significantly strengthen the fight against human trafficking by using data, AI, and intelligence-sharing partnerships with NGOs, law enforcement, and regulators, a new initiative shows

Key insights:

      • Collaboration is the strongest enabler of detection — Financial institutions are most effective at identifying human trafficking when they work closely with NGOs, law enforcement, and regulators, combining financial intelligence with victim-centered and investigative insights.

      • Data, technology, and AI can uncover trafficking networks — By analyzing financial transactions alongside open-source intelligence, social media activity, public records, and specialized datasets, organizations can identify patterns, relationships, and high-risk accounts more efficiently.

      • Financial institutions have a critical role in disrupting trafficking — Because human trafficking depends on moving and laundering illicit profits, banks and other financial institutions can help stop it by detecting suspicious activity, filing targeted reports, and supporting law enforcement investigations.


Human trafficking is not only one of the most devastating financial crimes but also one of the most complex as it cuts across fraud, money laundering, and organized crime, with some crime rings use their existing drug trafficking networks for human trafficking-related crimes.

Financial institutions are in a unique position to help battle this scourge as they can see the financial flows generated from human trafficking and sexual exploitation. Without the ability to launder the proceeds, human trafficking as a crime would lose some of its appeal.

To understand this better, a multi-city initiative around the FIFA World Cup, co-led by ³ÉÈËVRÊÓÆµ and , brought in leaders from financial institutions, law enforcement, non-governmental organizations (NGOs), regulators, and corporate risk departments to address human trafficking from a financial crime perspective.

Indeed, as research shows, forced labor in the private economy generates as much as $236 billion in , according to the International Labour Organization. If financial institutions can identify the proceeds of traffickers and their patterns, however, they can close suspected accounts, file prioritized suspicious activity reports, and notify law enforcement to help put a quicker end to this terrible problem.

The use of data and technology

Unfortunately, financial institutions often lack the context and the data points to act with certainty. These data points often include the names of victims, their behaviors, and their relationships with traffickers and can provide important clues about the origins and methods of human trafficking, including locations and transportation patterns. NGOs can help in this area; and such NGOs as the and already are providing critical, victim-centered insight.

In addition, NGOs often build datasets and proprietary content on their own to uncover trafficking. , for example, maintains a large, proprietary dataset that’s built from network metadata and behavioral signals collected from publicly accessible online environments. This data is then analyzed into real‑time intelligence, such as risk scores and activity patterns, which helps law enforcement identify and prioritize suspected child exploitation offenders.


Traffickers use social media platforms, online ads, and messaging apps to recruit victims and to advertise illicit services, often leave a digital footprint that can be analyzed, which enables law enforcement and analysts to identify victims, map relationships between illicit actors, detect recruitment patterns, identify locations, and uncover entire trafficking networks.


Other relevant information sources include the Illicit Massage Business (IMB) database from ³ÉÈËVRÊÓÆµ Special Services, which includes business accounts, the location, and the owner of every massage parlor in the US, in which trafficking victims are forced to operate.

Because traffickers use social media platforms, online ads, and messaging apps to recruit victims and to advertise illicit services, they often leave a digital footprint that can be analyzed. This enables law enforcement and analysts to identify victims, map relationships between illicit actors, detect recruitment patterns, identify locations, and uncover entire trafficking networks. This information can then be enhanced by combining it with public records and data from the open web, deep web, and dark web.

Learning the lessons of collaboration

As we at the ACAMS–³ÉÈËVRÊÓÆµ Human Trafficking Initiative looked back at the lessons learned and reviewed best practices, we can see that any success in identifying illicit trafficking accounts is based on three factors: i) close cooperation with law enforcement and NGOs; ii) specialized investigative resources with human trafficking backgrounds; and iii) the use of data and open-source intelligence, either standalone or integrated into monitoring workflows.

Financial institutions understand their role and the need to obtain specialized data and expertise; and leveraging these capabilities typically results in the termination or de-risking of suspicious accounts.

Because collaboration with law enforcement is not consistent across financial institutions, particularly in the US, this means that overall, there’s a very uneven focus on human trafficking detection and prevention, depending on the availability of resources and the level of collaboration.

The role of regulators, like the U.S. Treasury Department’s , is crucial because these entities can leverage AI to act even more rapidly and connect information quicker, which can help disrupt human trafficking more effectively. Investigators are instructed to make a specific selection, field 38(h), when filing a report and include a specific reference to human trafficking. This will allow FinCEN to analyze and identify patterns, trends, and trafficking networks by linking these reports together.

In that context financial institutions have another reason to embrace AI within their customer data. By analyzing transactions and other patterns of risk using all available data sources and building agentic capabilities and workflows within their own customer data, financial institutions will be able to better identify high-risk accounts without carrying out labor-intensive investigations.

While this event series focused on the 2026 World Cup, human trafficking existed long before the tournament and will not stop once it concludes. However, if NGOs, authorities, and financial institutions can significantly improve their ability to detect and disrupt it, that would represent a major step forward.


You can find out more about how law enforcement and others are disrupting human trafficking networks here

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