• • • Collaboration is the strongest enabler of detection 鈥� 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 鈥� 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 鈥� 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 成人VR视频 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鈥檚 built from network metadata and behavioral signals collected from publicly accessible online environments. This data is then analyzed into real鈥憈ime 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 成人VR视频 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鈥摮扇薞R视频 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鈥檚 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鈥檚 , 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

Key takeaways:

• • • The deal isn’t dead, but it’s not settled either 鈥� USMCA stays in force for now, but the refusal by the US to confirm a 16-year extension triggers rolling annual reviews, with a hard expiration in 2036 if no resolution is reached.

    • Auto rules of origin are the central battleground 鈥� Washington is pushing to raise North American content requirements well above the current 75% threshold, with a specific push for more US-based final assembly and parts production.

    • Uncertainty itself is a cost 鈥� Automakers make multibillion-dollar, multi-decade plant and supply-chain investments, and not knowing what the rules will look like next year (let alone in 2036) makes those bets harder to justify.

On July 1 鈥� the sixth anniversary of the United State-Mexico-Canada Agreement听(USMCA), taking effect 鈥� the United States, Mexico, and Canada were required under the agreement’s to jointly decide whether to extend the pact for another 16 years. The U.S. Trade Representative, Jamieson Greer, announced that the Trump Administration would not agree to renew USMCA in its current form, citing persistent US trade deficits with both neighbors and what the administration considers 鈥渦nresolved shortcomings鈥� in the deal. The move by the US pushed the North American trade pact into a new period of annual reviews and extended negotiations over tariffs, market access, and manufacturing rules.

This isn’t a withdrawal; indeed, the agreement will remain in force for another decade, providing that none of the three countries exits the agreement. However, the lack of a clean renewal opens the door to years of contentious negotiations over the rules governing continent-wide supply chains. Both Canada and Mexico had favored a straightforward 16-year extension, but the US was unwilling to sign off without changes.

Why automakers are ground zero

No sector is more exposed to this outcome than automotive manufacturing. Vehicles and parts routinely cross the borders of the US, Mexico, and Canada multiple times before final assembly, a pattern built up since the North American Free Trade Agreement (NAFTA) first opened North American auto trade in 1994, and the sector alone accounts for roughly 18% of all trade among the three countries.

At the heart of the dispute is the 鈥� the share of a vehicle’s value that must originate in North America to qualify for duty-free treatment. USMCA currently sets that threshold at 75% for passenger vehicles and light trucks, up from 62.5% under the old NAFTA rules. The Trump administration is reportedly seeking to push that figure to 82%, with half of that value required to come specifically from the United States 鈥� a change aimed squarely at pulling more engine, transmission, and assembly work back across the border.

That shift likely would ripple through the industry unevenly. The annual from American University’s Kogod School of Business, which tracks US content in vehicles annually, found that only 109 models are estimated to hit 51% or more US content for its upcoming index, down from 123 the year before 鈥� a sign of how far the current supply chain sits from any tightened standard. As one researcher involved in that analysis explains, automakers will ultimately have to weigh absorbing new tariff costs against relocating engine, transmission, and component production 鈥� or even entire assembly plants 鈥� into the United States.

have largely tried to protect the status quo rather than push for disruption. General Motors, Ford, and Stellantis have publicly urged Washington to extend the existing agreement, arguing it’s essential to American production, even as they privately brace for the possibility of major changes. Stellantis has also warned regulators about a separate risk: If US rules don’t keep pace with vehicles imported from outside North America, American-built models will keep losing ground to Asian imports, to the detriment of US autoworkers.

Managing the uncertainty tax

Perhaps the most immediate effect isn’t a specific rule change 鈥� it’s the absence of a deadline forcing one. that the decision doesn’t immediately change the flow of goods and services across North America, but it could weigh on business planning, particularly in industries that depend on long-term capital commitments.

Scott Lincicome of the Cato Institute, for example, pointed to exactly this risk, telling that the resulting uncertainty could weigh on investment decisions, which matters enormously for an industry that plans plant investments, supplier contracts, and vehicle platforms on five- and ten-year horizons.

There’s also a geopolitical wrinkle shaping the negotiations reflected in a growing concern in Washington over Chinese-made components entering North American supply chains through Mexico. Lawmakers have already proposed legislation directing US trade officials to prioritize protecting USMCA from Chinese investment during the review, which could translate into rules disqualifying vehicles that use components tied to Chinese state actors.

What happens next with the USMCA?

Formal bilateral talks between the US and Mexico are continuing, while US-Canada negotiations have barely begun. The US and Mexico are set to meet again the week of July 20 for a third round of bilateral negotiations tied to the joint review. Barring a breakthrough, expect this to become a recurring headline 鈥� another review, another round of tariff and rules-of-origin brinkmanship, repeated annually until either a deal is struck or the clock runs out in 2036.

For automakers, the message is less about any single new rule and more about planning in an environment in which the ground can shift every year. That’s a very different operating reality than the one the industry built its North American footprint on over the past three decades.

You can find out more about the USMCA and the challenges it faces here

Key insights:

• • • Sect. 301 and IEEPA tariffs operate on fundamentally different legal foundations 鈥� The IEEPA tariffs flow from an executive emergency declaration that can be unwound overnight, while Sect. 301 findings are built on a formal evidentiary record that can survive numerous administrations.

    • Those manufacturers that diversified away from China now face compounded exposure 鈥� The countries to which many manufactured moved their trade operations 鈥� including Vietnam, India, Bangladesh, and Malaysia 鈥� are now named in the recent Sect. 301 action.

    • Managing this complexity without purpose-built tools is no longer realistic 鈥� The need for access to quality vendor data, tariff classifications, country-of-origin mapping, and duty layering requires systems that can be updated continuously, not spreadsheets that are reviewed quarterly.

Since early 2025, manufacturers have lived in a tariff environment defined by volatility that鈥檚 been dictated seemingly at the whim of the United States. Rates announced one week were paused the next, country-specific deals emerged from diplomatic calls, and 90-day exemptions became the operating rhythm. For supply chain teams, the rational response was to treat every new tariff as provisional 鈥� something to monitor, not necessarily something to plan around.

That logic does not apply to the of the U.S. Trade Representative (USTR), under听Section 301 of the Trade Act of 1974听that a list of 60 economies 鈥� comprising the largest US trading partners 鈥� had failed to enforce a ban on goods produced with forced labor听are therefore were听restrictive to US trade.

To understand why, it actually requires and how it compares to the International Emergency Economic Powers Act (IEEPA), which the Trump Administration had used as its authority behind the 2025 reciprocal tariffs until that was disallowed .

Unlike the IEEPA, Sect. 301 is not an executive power that turns on or off depending on when a national emergency is declared. Rather, it is a statutory framework that requires the USTR to conduct a formal investigation, gather evidence, hold public hearings, and build a record before making an actionability determination. In the June 2 action alone, the USTR received testimony from nearly 60 witnesses and almost 500 public comments before issuing its findings.

That record matters, because it is what makes tariffs issued in response to Sect. 301 findings structurally resistant to reversal. Unwinding them requires either a new formal determination, a negotiated bilateral resolution in which the trading partner actually changes its practices, or Congressional action. A new administration cannot simply issue a presidential order lifting them because the legal bar is categorically higher.

And this distinction is no longer theoretical. After the Supreme Court ruled his tariffs invalid, President Trump immediately pivoting to Section 122 of the Trade Act of 1974, which permits a temporary global surcharge of up to 15% for no more than 150 days. That took effect February 24, and is set to expire July 24, unless extended by Congress. Tariffs imposed because of the June 2 Sect. 301 findings were never exposed to the same legal vulnerability and is now the administration’s primary vehicle for building durable tariff authority.

There is also a political dimension that compounds the durability. The June 2 findings are grounded specifically the failure of the named economies to prohibit the importation of goods made with forced labor. That framing carries broad bipartisan support in Washington, and neither party is positioned to argue against forced labor prohibitions, which means the political incentive to reverse these tariffs is far weaker than it was for the IEEPA-based tariffs.

The compounded exposure problem

For manufacturers that spent 2024 and 2025 diversifying their supply chains away from the tariff-heavy China, the June 2 findings create a specific and uncomfortable problem. The most common destinations for that diversification 鈥� Vietnam, Bangladesh, India, Malaysia, Thailand, and Indonesia 鈥� are all named in USTR’s recent action. Proposed additional duties of 10% to 12.5% would layer on top of existing duties and any Sect. 122 tariffs still in place during the transition period.

In other words, the move that looked like risk mitigation then may now carry its own tariff exposure now 鈥� and unlike the situation in 2025, there is no obvious alternative jurisdiction.

That means vendor management systems that integrate tariff data in real time 鈥� pulling current duty rates by code, flagging country-of-origin changes, modeling landed cost across multiple sourcing scenarios 鈥� are no longer a competitive advantage. Now they are a baseline operational requirement. The same applies to supplier compliance documentation. As forced labor attestations become relevant to exclusion eligibility under Sect. 301, having those records organized, current, and accessible is not an audit-readiness question, rather, it鈥檚 a cost-of-goods question.

Then, the practical challenge for manufacturers becomes an operational one, not just a strategic one. Tracking tariff exposure across dozens of suppliers, multiple countries of origin, layered duty structures, and evolving classification rules is not a task that can be easily scaled with traditional tools. For example, in the 24 hours following the Supreme Court鈥檚 tariff ruling, the US terminated one tariff regime, enacted a replacement under a different statute, and announced the launch of multiple new Sect. 301 investigations. A manufacturer鈥檚 spreadsheet that鈥檚 updated monthly cannot keep pace with a regulatory environment moving at that speed.

The durable lesson

The IEEPA tariff experience trained supply chain teams to stay nimble 鈥� and then demonstrated exactly how fragile executive-action tariffs can be when the Supreme Court invalidated them. That instinct toward flexibility still has value, of course; however, the Sect. 301 framework requires a parallel capability that requires manufacturers to recognize when a tariff is structural, model its long-term cost impact, and adapt sourcing and vendor strategies accordingly.

These new Sect. 301-based tariffs are not a negotiating position waiting to be resolved. They are a legal determination, built on a formal record, grounded in a cause 鈥� the elimination of forced labor from global supply chains 鈥� that has strong consensus across the political spectrum.

Those manufacturers that plan around them as permanent while investing in the tools to manage that complexity in real time will be better positioned than those waiting for the next exemption announcement.

You can find out more about how tariffs continue to impact global trade here

Key insights:

• • • Unauthorized practice of law doctrine is a poor fit for regulating AI and legal technology 鈥� UPL remains important when people represent others in court or affect legal proceedings, but it does not translate cleanly to software. Extending UPL to software moves the doctrine away from its original purpose and toward regulating the existence of tools rather than the risk of harm.

    • Legal technology should be regulated through consumer protection principles, not tool-based restrictions 鈥� Rather than asking whether an AI product or software platform “practices law,” regulators should focus on whether the tool misleads users, performs as advertised, provides adequate transparency, or causes demonstrable consumer harm.

    • A harm-based framework can protect consumers while expanding access to justice 鈥� Because most legal needs remain unmet, AI and justice tech can provide meaningful support to people who would otherwise receive no legal help. Safe harbors, disclosures, and accountability for fraud, negligence, or false advertising would better balance innovation with consumer protection.

This is the second of a two-part blog series examining how regulators, the legal profession, and individual litigants are looking at the unauthorized practice of law (UPL). We first looked at the history of UPL, and now this installment suggests a consumer protection-based method of regulation to replace today鈥檚 supplier-based regulations.

The legal industry has reached an inflection point in which the unauthorized practice of law (UPL) doctrine is no longer the right framework for regulating modern legal services. As technology reshapes how legal help is delivered, the focus must shift from policing who can provide legal support to ensuring that consumers are protected from harm.

This does not mean dismantling the doctrine entirely. UPL continues to serve an important function in which individuals represent others in court or otherwise engage in conduct that affects the integrity of legal proceedings 鈥� because courts need enforceable boundaries around who may appear before them.

Those considerations, however, do not translate cleanly to today鈥檚 AI-driven advanced technology. Extending UPL to software may shift the doctrine away from its original purpose and toward regulating the existence of tools rather than the risk of harm. A more effective approach is to apply existing consumer protection principles 鈥� such as fraud, negligence, and false advertising 鈥� to evaluate whether legal technologies are delivering accurate, transparent, and reliable support for users.

UPL continues to serve an important function in which individuals represent others in court or otherwise engage in conduct that affects the integrity of legal proceedings 鈥� because courts need enforceable boundaries around who may appear before them.

Over the past several decades, we have bent UPL law to fit emerging models of legal assistance, but with AI we have reached the point at which we should not attempt to stretch it further. AI has expanded the capabilities of legal technology beyond document automation to include research, summarization, and guided analysis. These tools are now widely available and are being used by individuals who would otherwise navigate legal issues without assistance.

At the same time, familiar regulatory arguments are being applied to these new tools. Assertions that such systems constitute UPL rely on assumptions that are increasingly difficult to reconcile with how these technologies function and are used.

Users generally understand that these systems are not lawyers. Engagement with AI tools is driven by accessibility, cost, and convenience, particularly in a landscape in which a substantial portion of legal needs go unmet. The more relevant inquiry is whether these tools provide a meaningful improvement compared to having no assistance at all.

Shifting from UPL to consumer protection

When consumers turn to technology, outcomes will vary, as they do across all forms of legal support. When issues arise, existing legal frameworks already provide mechanisms for accountability. Questions of fraud, negligence, and misleading representations can be addressed through established consumer protection laws without relying on an expanded interpretation of UPL.

Focusing on demonstrable harm rather than the mere existence of a tool aligns regulatory efforts with their intended purpose. It also reduces the risk of limiting innovation that could address persistent gaps in access to legal services encountered by so many individuals today. When liability is tied to the mere provision of technology, rather than to harmful conduct, the result is often reduced investment and slower development of potentially beneficial solutions.

Some jurisdictions have begun to adopt approaches that reflect this distinction. Non-prosecution policies, disclosure requirements, and clearly defined safe harbors for AI-driven tools can provide a framework in which innovation can proceed alongside appropriate safeguards. Indeed, these models emphasize transparency and consumer awareness while allowing for continued experimentation and improvement.

Legal doctrine in transition

Whether state regulators drive the change or watch from the sidelines, there are broader legal considerations on the horizon. On the federal level, the U.S. Federal Trade Commission (FTC) has already signaled that a change towards harm-based regulation as opposed to tool-centered regulation is coming.

In his concurrence in , which had billed itself to consumers as 鈥渢he world鈥檚 first robot lawyer,鈥� :

鈥淢y vote should not be taken as support for the State Bar of California鈥檚 claim that DoNotPay was engaged in the unauthorized practice of law. The Commission does not enforce state occupational-licensing laws like California鈥檚 unauthorized-practice-of-law prohibition. And if a company were to create a computer system capable of giving accurate legal advice and drafting effective legal documents, or honestly advertise a system that provides something less, I doubt that the aggressive enforcement of lawyers鈥� monopoly on legal service would serve the public interest.鈥�

This aligns with remarks from U.S. Supreme Court Justice Neil Gorsuch鈥檚 confirmation hearing when he stated: 鈥淲hy is it that every time certain companies that provide online legal services for basic things get sued every time they move into a new State?鈥�

While approaching it from different angles, courts are arriving at similar positions. In , an Oregon court of appeals case about fabricated citations, the court stated: 鈥淩egardless of provider, a generative artificial intelligence program is not, itself, a lawyer.鈥� While perhaps not intended, the logical extension of this is that a tool cannot be engaged in UPL 鈥� only the human using it can.

Finally, recent judicial reasoning, such as in the Supreme Court case of , suggests a closer examination of attempts to regulate speech by categorizing it as professional activity. The Court found professional speech protected by an 8-1 vote, suggesting bipartisan questions about whether professional licensing can continue to restrict speech.

Aligning regulation with reality

Within this evolving landscape, the central policy question is how best to protect consumers while enabling meaningful improvements in access to legal support. Frameworks designed for earlier models of service delivery can be difficult to apply effectively to new forms of technology without producing unintended consequences.

Instead, a consumer-focused approach can direct attention to the quality, accuracy, and transparency of services. It can evaluate whether users are misled, whether tools perform as described, and whether harm can be identified and addressed.

While AI-driven technology offers a way to extend the reach of legal support systems and to develop solutions that operate at a scale not previously achievable, the concept of UPL is still a valid one, albeit within a more limited and clearly defined scope.

As the legal system rapidly evolves, aligning regulatory approaches with current realities allows for both the protection of consumers and the development of the tools that expand access to justice in practical and sustainable ways.

You can find more about the challenges around issues of access justice here

Key insights:

• • • Financial institutions on the frontlines of trafficking prevention 鈥� As the 2026 World Cup continues, it puts financial institutions on the frontlines of detection and prevention of human trafficking, whether they are in a host city or not.

    • US government has offered guidance 鈥� FinCEN’s updated Section 314(b) guidance, issued June 12, gives institutions explicit authority to share fraud and trafficking-related information with each other, and strongly encourages them to do so.

    • Cross-sector collaboration is essential 鈥� Organizations like The Knoble are building the cross-sector collaboration infrastructure that makes that kind of information sharing operational, not just theoretical.

The 2026 FIFA World Cup is, by every measure, the largest sporting event ever staged on North American soil, drawing 3.6 million spectators through its early weeks and generating billions of dollars in economic activity 鈥� that level of transaction volume that would strain any risk & compliance team on its best day.

The World Cup and its millions of international visitors also are creating the very conditions that human traffickers are always eager to exploit.

It is a pattern that researchers, law enforcement, and financial crime professionals have documented around major global events for years. And it is precisely why, as the World Cup enters its most dramatic final weeks, compliance teams at financial institutions of every size are treating this moment as the operational inflection point it is.

The World Cup as a financial ecosystem

Most people associate the World Cup with soccer and international competition; yet for compliance professionals, it also represents a full financial ecosystem of its own that they have to navigate.

Julie Conroy, a leader at , a nonprofit founded in 2019 to bring together financial services and law enforcement to combat human trafficking, financial scams, elder financial exploitation, and child sexual exploitation, is direct about threat compliance teams face. “All of these big, massive global events bring together lots of people,鈥� Conroy says. 鈥淎nd that makes it very easy for the criminals鈥� to hide their human trafficking.”

Of course, the financial footprint of that activity runs through the banking system, through peer-to-peer transfers, prepaid card activity, late-night ATM withdrawals, unusual hotel charges, or vague payment memos reading “services” or “personal care.” None of these transactions are inherently suspicious in isolation; yet together, as a pattern layered across time and accounts, they can signal exploitation in real time.

FinCEN’s recent guidance changed the calculus

On June 12, the U.S. Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) issued clarifying how financial institutions can share information with one another about suspected fraud, money laundering, and other financial crimes under Section 314(b) of the USA PATRIOT Act.

The World Cup and its millions of international visitors also are creating the very conditions that human traffickers are always eager to exploit.

The guidance is both a clarification and a signal. It explicitly confirms that institutions may share information about suspected trafficking-related activity with any other financial institution eligible to participate in the 314(b) program. It broadens the categories of shareable information to include video surveillance footage, cyber-related data such as IP addresses, and behavioral fraud indicators such as newly added payees followed by large transfers, multiple accounts with similar identifying information, and login activity from geographically distant locations.

framed the urgency plainly: “Financial institutions are often the first to see suspicious activity in real time. They need the tools to act quickly and share information that can help stop fraud before it spreads.”

For human trafficking detection specifically, this matters because no single institution sees a complete trafficking network. One bank might observe the late-night ATM pattern, another might flag the prepaid card activity, and a third might notice the unusual payroll behavior of a temporary staffing company supplying event workers. Individually, those fragments are insufficient; however, when shared, they become actionable intelligence.

“Now we can share data among ourselves for fraud prevention purposes 鈥� which is amazing,鈥� Conroy notes.

The collaboration infrastructure already exists

The regulatory green light from FinCEN is necessary but not sufficient on its own. Effective information-sharing requires relationships, operational frameworks, and trust that take time to build. That is the gap The Knoble was created to close.

The organization has spent six years building the bridges between financial institutions and law enforcement that make inter-agency collaboration real rather than aspirational. That work is harder than it sounds due to personnel changes and departments that operate in silos. The Knoble’s member network is designed to outlast those structural challenges by creating a durable community of practice around financial crime detection.

“The amazing thing that The Knoble has been able to do is bring together banks and law enforcement, build those bridges between the two of them, and give a guide to banks about what are the red flags,” Conroy explains.

, which was developed in anticipation of the tournament, reinforces what FinCEN’s guidance also makes clear: Trafficking rarely presents itself through a single dramatic transaction. Investigators need to identify clusters of behavior across time, look at shared devices and phone numbers, and track rapid movement of funds across accounts. The behavioral anomaly, not the individual transaction, is the signal.

Every suspicious activity report filed, every bit of information shared, and every frontline employee who escalates an unusual interaction contributes to an intelligence picture that law enforcement can act on immediately, while victims are still at risk.

However, perhaps the most consequential misconception in AML and fraud around the World Cup right now is that human trafficking is a host-city problem.

Trafficking networks are geographically distributed by design. Victims may be recruited in one state, transported to and exploited in a host city, and their proceeds moved through financial institutions located elsewhere. That means that a regional bank in Kansas City or a credit union in a midsize market with no World Cup connection can still observe funnel account activity, unusual prepaid card funding, or suspicious peer-to-peer transfers tied to a network operating hundreds of miles away.

Training is not optional

FinCEN’s guidance also makes clear that transaction monitoring systems cannot address trafficking issues alone 鈥� a financial institution鈥檚 frontline staff matter.

Tellers, branch employees, and customer service representatives are often in a position to observe indicators that never appear in an alert queue. A customer who appears fearful, cannot speak freely, or gives answers that seem scripted. These behavioral signals and more require trained human observation.

That鈥檚 why these frontline professionals are so important. Every suspicious activity report filed, every bit of information shared, and every frontline employee who escalates an unusual interaction contributes to an intelligence picture that law enforcement can act on immediately, while victims are still at risk. This is critical, because human trafficking is happening in real time, and the transactions that compliance teams are observing are occurring while the exploitation is ongoing.

Conroy frames The Knoble’s mission in exactly these terms. The organization exists to take financial professionals who are already passionate about stopping human trafficking and other crimes and mobilize them within their day-to-day work.

Now, as the World Cup enters its final weeks, the question now is whether compliance teams will continue to treat this moment as an operational priority by using the collaboration tools and the regulatory guidance at their disposal to make a crucial difference in the lives of trafficking victims.

For more on this, tune into the 成人VR视频 Institute鈥檚 recent 鈥淐larity鈥� podcast听

Key takeaways:

• • • The dulling effect is real, but the root cause is older than AI 鈥� Wherever gatekeeping institutions reward a narrow formula, their output converges long before any chatbot enters the picture. AI then accelerates optimization toward your already selected criteria.

    • Using AI for efficiency alone leaves the creative upside on the table 鈥� While most organizations deploy AI for simple drafting tasks, the bigger payoff comes from using it as a discussion engine 鈥� a sort of sparring partner that pressure-tests ideas and pushes thinking past the first plausible answer.

    • The highest-leverage intervention is reforming what you reward 鈥� The fix for this is upstream of the technology, and it comes from giving people time to make sure unconventional ideas actually survive your organization’s sorting mechanisms.

A tension sits at the center of nearly every serious conversation about AI and organizational strategy, and most leaders can feel it even if they haven’t named it yet.

On one side is the promise that AI can make teams more creative. That it can accelerate brainstorming, provide deeper research, identify hidden connections, and pressure-test ideas before they reach a client or a boardroom. When used well, AI is not a replacement for thinking but an amplifier of it.

On the other side, of course, is the fear that regular AI use is quietly dulling the creativity it’s supposed to enhance. That real fear is that the more people lean on these tools, the more their thinking converges toward the same polished, plausible, and fundamentally safe middle ground 鈥� and that the less people work their creative muscles, the more they atrophy without them realizing it. This trade-off, swapping originality for efficiency, is a losing exchange.

Both of these intuitions are reasonable and both are, to varying degrees, correct. However, they aren’t equally weighted. The purely cautious camp is taking the bigger gamble, because any competitor that cracks the problem by figuring out how to capture AI’s creative upside while managing the dulling effect gets both the innovation edge and the efficiency gains. The cautious organization doesn’t just miss the upside, it falls behind on both fronts.

The catch is that cracking the problem requires correctly diagnosing what’s actually killing your creativity 鈥� and a prominent recent essay on this exact topic gets it instructively wrong.

A good question, poorly tested

Rebecca Winthrop, a senior fellow at the Brookings Institution and director of its Center for Universal Education, recently published in The New York Times arguing that AI is constricting creative thinking. Her central claim is that while chatbots produce polished language, they鈥檙e masking a narrowing range of underlying ideas 鈥� and this is especially dangerous for students, whose creative development is still taking shape.

The piece is worth reading, and not just as a foil. Winthrop draws on from Georgetown neuroscientist Adam Green, whose team has been tracking the range of ideas in college application essays before and after ChatGPT’s release. Green’s findings related to the before/after tracking study (which have not yet been peer-reviewed) are striking, finding that while post-ChatGPT essays used more diverse and colorful vocabulary, the ideas beneath that language converged. Human judges rated the AI-era essays as more creative, even though the substance had narrowed. In a separate study by Green’s team, cited by Winthrop, human-written essays contributed up to eight-times more novel ideas than AI-generated ones.

The fear is that regular AI use is quietly dulling the creativity it’s supposed to enhance, and the real fear is that the more people lean on these tools, the more their thinking converges toward the same polished, plausible, and fundamentally safe middle ground 鈥� and the less people work their creative muscles.

And Winthrop flags serious concerns that deserve far more attention than they typically get. For example, AI’s homogenizing pressure falls hardest on those students who sit farthest from the mainstream, including neurodivergent students and those from racial and linguistic minorities. That finding alone should be shaping education policy conversations and acting as a warning for innovation-conscious reformers.

Here’s where Winthrop鈥檚 piece stumbles, however, and where it becomes a cautionary tale for organizations that may be thinking about their own AI and innovation strategies. The evidence Winthrop chooses to build her case on 鈥� the college admissions essay 鈥� is possibly the worst genre in American education for measuring whether AI is killing creativity. Because the creativity in college admissions essays was already dead.

I should know. I鈥檓 one of its murderers.

The most templated genre in America

The college admissions personal statement has been reverse-engineered for decades. Well before any large language model existed, applicants had cracked the code: Be damaged, but not too damaged; be resilient but make it look like you did it yourself; and be whole now, because the institution wants guaranteed successes, not risky projects. And all of this must be delivered in a tone that makes the committee feel good about their institution’s role in a meritocratic society. Deviate from this formula and you’re taking a risk, but hit every beat and you’re in the pile that moves forward.

I know this because I lived it recently enough to still remember the specific frustration of trying to fit my own experiences into that template at the age of 17, twisting and contorting experiences I’d actually lived through into the shape I knew admissions readers were looking for while sanding away the human beneath when it didn鈥檛 fit. The authentic version of my story wasn’t what they wanted, the version that hit the beats was.

And there’s a further detail conspicuously absent from Winthrop’s essay: The college admissions consulting industry. It’s enormous, it’s been around for decades, and its entire business model is teaching applicants to write to the template. Some of these consultants charge $5,000 or more, and their product isn’t creativity, it’s optimization. They teach students to identify what the admissions committee rewards and deliver exactly that, with the rough edges smoothed away and the personal experiences torqued into the right emotional shape.

My family took this seriously enough to invest in help, and I was fortunate enough they had the means to do so. I had one of those consultants. Mine cost $2,000, and my parents had to sell my mom’s pinball machine to pay for it. I think sometimes about what it says that the path to higher education ran through a professional who taught me, essentially, to write to a formula rather than to present myself in a way that would have given the committee a more honest, unique portrayal of just who they were letting into their institution. The consultant didn’t make me less creative; the system that made the consultant necessary did.

And this is the blind spot in Winthrop’s argument. She treats pre-ChatGPT essays as the baseline for authentic creative expression, but that baseline was already shaped by an industry dedicated to template optimization. So when Green’s research finds that post-ChatGPT essays use richer vocabulary but converge on familiar ideas, the question worth asking isn’t just whether AI caused a measurable shift (Green’s controlled experiments suggest it did) but whether the underlying ideas were already converged at a more fundamental level that the metrics don’t capture. In essence, all AI may have done is make that convergence more visible while democratizing the surface polish.

There’s an entirely different version of Winthrop’s essay waiting to be written 鈥� one in which the same data tells a democratization story rather than an erosion story. Where a free chatbot gives a first-generation college student the same surface-level advantage that a $5,000 consultant gave wealthier applicants for years. That’s not a comfortable reframe for institutions already invested in the idea that their selection processes brings forth authentic individuality 鈥� but it’s the reframe the data actually supports.

The template always comes first

This isn’t unique to college admissions. Wherever institution rewards a narrow formula, it gets gamed 鈥� and the gaming predates whatever technology that has made it easier.

The video essayist Sarah Z traced exactly this pattern in , which makes the gap in Winthrop鈥檚 argument clearer. When the publishing industry rewarded a specific shape of trauma narrative in the 1980s and 鈥�90s 鈥� suffering resolved through individual resilience 鈥� the template grew so predictable that fabricators outcompeted honest writers. Laurel Rose Willson sold a satanic-ritual-abuse memoir and, years later, a Holocaust-survival story, citing the same self-inflicted wounds as evidence for both. Publishing houses weren’t fooled just because they were careless, they were fooled because they’d built a machine that searched for formula 鈥� and the system that rewards a narrow pattern is the same one that makes it exploitable.

AI doesn’t create that convergence, it just accelerates the optimization toward whatever you’re already selecting for. Blaming AI for homogenized output in an already-homogenized system is like blaming your GPS for traffic on the BQE 鈥� the bottleneck was there long before the tool arrived.

If your organization has ever received a stack of pitch decks, strategy memos, or RFP responses that all hit the same beats in the same order, congratulations! You’ve built your own admissions committee, but don鈥檛 blame AI.

AI doesn’t create that convergence; it just accelerates the optimization toward whatever you’re already selecting for. Blaming AI for homogenized output in an already-homogenized system is like blaming your GPS for traffic on the BQE. The bottleneck was there long before the tool arrived.

Threading the needle

Of course, none of this means the concern about AI and creativity is unfounded. The dulling effect is real, and anyone who uses these tools regularly has probably felt its subtle gravitational pull toward the center. Or in the way a chatbot’s first suggestion can quietly foreclose any other directions you might have explored on your own, or how it may produce something that sounds polished but carries none of your voice

A different research team 鈥� Anil Doshi and Oliver Hauser, behind the , Winthrop herself points to 鈥� put a name to the mechanism, anchoring. Handed an AI-generated idea, writers locked onto it, narrowing the range of what they produced before they’d really begun.

However, the solution on an organizational level isn’t to restrict the tool; rather it’s to address the institutional and behavioral factors that determine whether the tool narrows thinking or expands it.

In this determination, three things matter most:

First, use AI as a discussion engine, not just an automation tool 鈥� There’s a meaningful difference between asking a chatbot to draft something for you and using it to create something with you. This article is a case in point. I didn’t read Winthrop’s essay and immediately decide to write a response. Instead, I spent almost half an hour talking to Claude about the article, debating the argument, testing my objections, diving into Green鈥檚 research more deeply, and connecting the piece to ideas I’d been thinking about from completely different contexts, such as the Sarah Z essay. This article emerged from that conversation unintentionally, and it would not have existed without it.

Further, the ideas emerged pressure-tested and sharpened through a process that felt more like sparring than delegation 鈥� and that鈥檚 exactly the kind of process organizations should be targeting. Most organizations deploying AI are using it for efficiency 鈥� drafting, summarizing, formatting 鈥� and that’s fine. However, if that’s all you’re doing, you’re leaving the creative upside untouched, and your people are feeling the dulling effect without the compensating benefit. It takes an intentional push from leadership to get teams using AI as a thinking partner rather than a shortcut.

Second, give people time 鈥� This sounds obvious, but it matters specifically because of how AI interacts with time pressure. When people are rushing, they take the first adequate output and move on. With traditional workflows, shortcuts save time at the cost of quality or risk. With AI-assisted workflows, however, shortcuts save time at the cost of originality, because the first output from a chatbot is almost always the most conventional one. It’s the statistically average response, and reaching the edges takes iteration, pushback, and follow-up prompts that challenge the initial direction. That takes time, and if your people don’t have it, they’ll use AI the way a stressed applicant uses a college essay consultant, producing the safest possible version of whatever the system rewards rather than the innovative one which could change the game.

Third, reform what you reward 鈥� This is the intervention that actually addresses the root cause, and it’s the one most organizations will resist because it requires examining their own sorting mechanisms. If your evaluation criteria, your promotion structures, your review processes, and your RFP scoring rubrics all select for the safe and conventional, then AI will only turbocharge that selection.

You’ll get the template faster and more polished than ever, much like the admissions committee that rewards a narrow emotional arc and gets 300,000 identical essays. Or, if your firm rewards the pitch deck that hits every expected beat and takes no risks, AI will produce that pitch deck beautifully 鈥� and you’ll wonder why innovation has stalled.

Again, the intervention is upstream of the tool. What does your organization actually do when someone brings in an unconventional idea? What happens to the proposal that doesn’t fit the template? If the answer is that it gets smoothed out in review or tossed altogether, that’s not an AI problem.

The old traps didn’t disappear

Winthrop is right that creative thinking is something to protect and nurture. She’s also right that AI introduces new pressures that deserve serious attention. And she’s right that the stakes are highest for the people whose perspectives are already farthest from the mainstream.

But the college admissions essay wasn’t homogenized by ChatGPT, it was homogenized by decades of institutional selection pressure that rewarded a single template and penalized everything that didn’t fit. AI didn’t create that problem, it just made the template accessible to everyone, including the families that couldn’t previously afford $2,000 and a pinball machine to get their kid across the threshold.

Similarly, your organization’s creative output won’t be determined simply by which AI tools you adopt. It will be determined by what your leadership rewards, what your processes select for, and whether your people have the time and incentive to push past the first plausible AI-supplied answer.

The technology is new, but the traps are very old. And if you want to use AI to make your organization more innovative, the place to start isn’t the tool 鈥� it’s the template.

You can find more听听from the 成人VR视频 Institute here

Key insights:

• • • De-banking reasons have to be articulated and verifiable 鈥� De-banking that鈥檚 driven by category avoidance rather than individual risk assessment, exposes institutions to legal, regulatory, and reputational harm and pushes legitimate customers out of the regulated financial system.

    • A screening flag is not a conclusion 鈥� Proper investigation must follow any identified red flag before any de-banking decision is made.

    • Political considerations have no place in de-banking decisions 鈥� The only defensible standard is documented, individualized risk analysis that would be applied consistently not matter the customer.

De-banking 鈥� or the involuntary removal of a customer from financial services 鈥� has moved from a compliance back-office concern to a front-page issue. Members of Congress have called for hearings.; advocacy groups representing small businesses, cryptocurrency firms, firearms dealers, and faith-based organizations have filed complaints; and financial institutions, often caught between genuine compliance obligations and growing pressure to justify their de-banking decisions, are operating in an environment with significant legal and reputational exposure on both sides.

Banks have the legal right to exit customer relationships, of course; however, what is in dispute is whether the decisions driving those exits are defensible. Are they grounded in documented, individualized risk analysis? Or are they being shaped by broad category avoidance, reputational anxiety, or political considerations that have no formal basis in law?

Getting this wrong is not a minor procedural failure; rather, it鈥檚 a legal exposure, a regulatory liability, and, increasingly, a legislative headache.

Objectivity requires removing politics from the process

The de-banking debate did not emerge recently. During , the U.S. Department of Justice (DOJ) initiative began in 2013, the government applied pressure on banks to exit relationships with industries it found undesirable, including payday lenders and firearms dealers, without formal legal prohibition.

The episode revealed a structural vulnerability: Financial institutions are susceptible to removing customers not because individual accounts present documented risk, but because external pressure, political or otherwise, has labeled entire categories of customers inconvenient. (The DOJ ultimately acknowledged the program was in August 2017.)

Banks have the legal right to exit customer relationships, of course; however, what is in dispute is whether the decisions driving those exits are defensible.

Yet, that pattern has persisted in subtler forms. Today, cannabis businesses operating legally under state law, money services businesses, crypto exchanges, and organizations associated with politically sensitive causes routinely report being dropped from banking relationships with little explanation and no apparent individualized analysis. The common thread is not confirmed financial crime, rather it鈥檚 membership in a certain category of enterprises.

In , President Trump issued an Executive Order titled “Guaranteeing Fair Banking for All Americans,” directly addressing this pattern and directing federal banking regulators to remove “reputation risk” and other subjective criteria from supervisory guidance and examination materials.

This is precisely where objectivity becomes a legal and operational imperative, not just a principle. A risk-based de-banking or off-boarding process must apply the same documented criteria to every customer, regardless of industry association, political affiliation, or public profile. When an institution debanks one customer for activity it tolerates in another, the inconsistency itself becomes the liability. have long reinforced that risk-based compliance means evaluating customers on their own merits, and that blanket policies applied to industries rather than individuals do not satisfy that standard.

Screening raises questions, and investigation answers them

One of the most consequential errors that financial institutions make is treating a screening alert as a final determination rather than a starting point. Know your customer frameworks, customer due diligence requirements, governmental watchlists, adverse media flags, and transaction monitoring alerts are tools for identifying accounts that warrant closer review. By themselves, they are not grounds for termination.

The gap between a flag and a confirmed risk finding is where decisions 鈥� both defensible and indefensible 鈥� are actually made. An adverse media hit on a business owner may reflect a decade-old civil dispute that has no bearing on current account activity. A transaction pattern that triggers a monitoring alert may have a straightforward, documented business explanation. Enhanced due diligence exists precisely because some customers require deeper analysis before a meaningful risk determination can be made.

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A risk-based de-banking or off-boarding process must apply the same documented criteria to every customer, regardless of industry association, political affiliation, or public profile.

A sound investigation process includes several elements that are often absent in practice, such as documented escalation paths from front-line staff to BSA officer to legal review; a genuine opportunity for the customer to respond to concerns before a decision is finalized; findings recorded in writing with sufficient specificity to withstand external scrutiny; and a proportionality review requiring the institution to evaluate whether risk mitigation short of termination is viable before defaulting to de-banking.

The stakes extend well beyond any single customer relationship

Financial institutions often treat de-banking as a discrete internal risk decision; however, the aggregate effect of category-based de-banking carries systemic consequences that regulators and legislators are increasingly unwilling to overlook.

When categories of legitimate customers cannot access banking services, the burden falls hardest on those with the fewest alternatives. Equally important, pushing customers out of the regulated financial system does not eliminate risk; instead, it relocates it to less transparent channels in which illicit activity is harder to detect and report.

Three states 鈥� Florida, Tennessee, and Idaho 鈥� have already enacted fair access laws requiring that financial institutions make services available based on objective risk criteria. And more than a dozen additional states have proposed . At the federal level, the would require larger banks to provide services based on quantified, documented risk standards.

Practical steps for financial institutions

Institutions need to build defensible, consistently applied processes as the foundation for any de-banking decision. There are several steps they can take, including:

• • • Audit current de-banking criteria for political and categorical language 鈥� Review existing off-boarding policies for any language that excludes industries or customer types based on perceived political sensitivity or reputational association rather than documented risk.
    • Establish a neutrality standard in all de-banking decisions 鈥� Require that every de-banking decision be traceable solely to facts in the customer file. External pressure, government signals, and industry headlines should play no role in the determination.
    • Separate screening from decision-making 鈥� Build a formal investigation step between any monitoring alert or red flag and a de-banking decision. Document what was reviewed, who reviewed it, and what the findings support.
    • Create a customer response mechanism 鈥� Where legally permissible, provide customers with an opportunity to respond to concerns before a final decision is made. Record whether and how that response was considered.
    • Establish a proportionality review 鈥� Before exiting a relationship, require a written determination that any other risk mitigation, including enhanced monitoring, transaction limits, or additional documentation requirements, was evaluated and found insufficient. Document everything.

As regulatory scrutiny around de-banking decisions intensifies, financial institutions can no longer treat it as a routine internal decision. The path forward demands consistent, well-documented, and objectively applied processes that stand up to legal, regulatory, and public scrutiny. Institutions that embed neutrality, transparency, and proportionality into their decision-making will not only reduce risk but also will strengthen trust in the financial system as a whole.

You can find more about the challenges facing financial institutions here

Key highlights:

• • • Plans without funding are political theater, not strategy 鈥� Across the G20 and beyond, governments spend about $1 per vulnerable person per year, making the most comprehensive national action plans functionally undeliverable.

    • Corporate forced labor is a crime with no perpetrators 鈥� Despite an estimated tens of millions of victims in global supply chains, there has been only one forced labor investigation ever brought against a Fortune 500 company, exposing a near-total absence of criminal accountability in non-financial industries.

    • Real-time data accountability can work on a shoestring budgets 鈥� Uganda’s TipMap platform, built on a budget of just hundreds of thousands of dollars with NGO and US government support, demonstrates that transparent, publicly accessible prosecution tracking is achievable even for low-income countries 鈥� yet most wealthy nations have yet to replicate this model.

Every year, governments around the world publish sweeping national action plans to combat modern slavery, covering everything from vulnerable children, forced labor, and gender-based violence to prosecution targets and victim support. These action plans are, in many cases, genuinely comprehensive documents, and also in many cases, they are almost entirely unfunded.

That is the central finding of the (MSPI), a new tool developed by Duncan Jepson, Director of Strategy and Operations at . After decades working across supply chains, corporate law, and financial crime compliance in Asia, Jepson grew frustrated with a sector that was generating more conferences and consultants than criminal prosecutions. The MSPI takes a step back from that ground-level work and asks how governments are investing in this problem at a scale that matches their stated ambitions.

The answer, unsurprisingly, is that there is a big gap between plans and funding the execution of those plans. Across the G20 plus additional countries, total government spending on modern slavery prevention amounts to roughly $1.6 billion annually, Jepson notes. When measured against the estimated population of up to 2 billion people living in conditions of poverty and precarity that make them vulnerable to exploitation, the 鈥渋nvestment鈥� by governments works out to approximately $1 per person per year.

Grand plans & empty coffers

The MSPI evaluates governments across four dimensions, which include the context of exploitation within their borders, the comprehensiveness of their national action plan, the funding allocated to that plan, and the measurable outcomes produced. The gap between the second and third dimensions is the point at which the analysis reveals the most confounding gap.

Most national action plans, Jepson notes, look remarkably similar regardless of whether they come from wealthy nations or some of the poorest countries in the world. They include all the right elements; however, the problem is that the ambition of the plan rarely maps onto available resources. “If you see a similar kind of plan in a country which is not providing anywhere near the same investment, maybe only providing $10 million to $20 million,” then they’re clearly not going to be able to build the kind of institutional mechanisms and have them operational to achieve their stated ends, Jepson explains.

When measured against the estimated population of up to 2 billion people living in conditions of poverty, the 鈥渋nvestment鈥� by governments works out to approximately $1 per person per year.

This gap is partly a result of how these plans get written. Policy teams include every desirable outcome, every population group, and every intervention type because comprehensiveness signals seriousness. The result is what Jepson describes as a political product rather than a strategic one because it is detached from realities of resource constraints.

The three Ps framework 鈥� set out in the , which organizes anti-trafficking efforts around prevention, protection, and prosecution 鈥� has drifted from being a planning tool into being a target in itself. Governments check the boxes, publish the plan, and treat that as a win. The actual investment required to deliver outcomes becomes secondary.

Many perpetrators face no accountability

Perhaps the most sobering element of Jepson’s analysis concerns corporate accountability which, outside of healthcare and financial services, is extremely limited for criminal matters such as forced labor. Modern slavery in global supply chains, particularly forced labor in agriculture, manufacturing, fishing, and extractive industries, generates enormous profits. Prosecutions against the corporations involved are nearly nonexistent.

The , which Jepson brought to the U.S. Department of Homeland Security鈥檚 investigations unit a few years ago, remains a rare landmark. When he received a World Customs Organization award for the work, the citation described it as recognition for “the first investigation into a Fortune 500 company.鈥� Indeed, the fact that there is only one successful investigation in the entire history of Fortune 500 enforcement on forced labor is stunning in itself.

The structural reason for this, Jepson argues, is that non-financial industries operate without a criminal legal framework wrapped around their regulatory obligations. Banks are required to identify suspicious transactions, file reports, and de-risk clients connected to illicit activity, all under threat of serious legal regulatory consequence.

Modern slavery in global supply chains, particularly forced labor in agriculture, manufacturing, fishing, and extractive industries, generates enormous profits, while prosecutions against the corporations involved are nearly nonexistent.

Manufacturers, food producers, and commodity traders face no equivalent pressure. Their obligations tend to be framed in the language of sustainability and ethical sourcing, which are voluntary, subjective, and entirely company controlled.

When violations are discovered, the response is typically managed internally through grievance mechanisms, remediation programs, and consultant-led audits. Workers rarely have access to independent legal recourse and access to justice.

What good funding and enforcement should look like

Jepson is careful to point out that meaningful progress exists, even on limited budgets. , developed with support from the Human Trafficking Institute and US funding, provides a real-time, publicly accessible database of trafficking prosecutions and arrests. For a country investing only hundreds of thousands of dollars in this space, the platform demonstrates how transparency and institutional accountability can be achieved without enormous resources.

Italy and Germany both earn recognition for aligning their plans with their investment levels and for building on contextual knowledge. Yet neither country has solved corporate supply chain accountability, even though both demonstrate that coherent strategy tied to realistic resourcing produces better outcomes than aspirational planning without funding.

The US import ban mechanism, developed through U.S. Customs and Border Protection, remains the most significant enforcement tool in the world, although it鈥檚 still largely unique to one country.

The case for realistic investment

What Jepson would like to see instead is relatively straightforward. Governments need to develop a deeper, intentional recognition that their current spending levels are insufficient, he says, adding that investment in prevention also makes economic sense.

Every dollar not spent stopping exploitation upstream generates far greater costs in law enforcement response, victim and social services, and lost economic productivity downstream. Clearly, $1 per vulnerable person per year will not build the necessary infrastructure to protect anyone.

You can find out more about the challenges in combatting force labor in supply chains here

Key insights:

• • • The Panama Papers transformed beneficial ownership 鈥� The release of the Papers in 2016 changed the idea of beneficial ownership from a technical compliance footnote into a global policy imperative, and the pressure has not let up.

    • Regulatory responses have been significant but uneven 鈥� The EU has pushed forward aggressively, while US reforms under the Corporate Transparency Act have been substantially narrowed.

    • For compliance professionals, the enduring lesson is not about any single regulation 鈥� Rather, compliance professionals should have one goal: Maintaining the discipline of asking who, ultimately, is behind the transaction.

When 11.5 million documents from Mossack Fonseca were published on April 3, 2016, compliance teams across financial institutions around the world faced unprecedented pressure from senior leadership to prove they actually knew the true identities of their clients’ beneficial owners. A decade later, establishing that ultimate ownership remains both the most important and the most difficult task in anti-money laundering compliance.

A watershed moment, but not a starting point

It would be a mistake to credit the Panama Papers with inventing beneficial ownership as a compliance concern. The Financial Action Task Force (FATF), an intergovernmental organization created to promote anti-money laundering (AML) activities, had long emphasized the risks of anonymous shell companies. The United Kingdom was already developing its Persons with Significant Control register; and the United States鈥� Treasury Department鈥檚 Financial Crimes Enforcement Network (FinCEN) had a draft of customer due diligence guidance in circulation before a single Mossack Fonseca document was made public.

Yet, what the leak of the Panama Papers did was something more powerful than create law 鈥� it created political will.

The leak showed, with granular specificity, how shell companies, nominee directors, layered trusts, and intermediary accounts could be stacked together to place meaningful distance between regulators and the individuals who actually control the assets. These were not fringe techniques; rather, they were routine services offered at scale to clients in more than 200 jurisdictions. The “gatekeeper problem” 鈥� the tendency of lawyers, accountants, and formation agents to introduce clients without responsibility for verifying who those clients ultimately were 鈥� was no longer theoretical. It was documented, widespread, and systemic.

What the decade of response produced

The regulatory response to the Panama Papers was substantial, even if ultimately uneven in execution.

In the US, FinCEN’s 2016 CDD Final Rule standardized what many institutions were doing selectively: requiring identification and verification of beneficial owners of legal-entity customers using a 25% ownership threshold and a control prong. For the first time, this was an enforceable expectation across covered financial institutions 鈥� not a best practice, but a mandate.

The regulatory response to the Panama Papers was substantial, even if ultimately uneven in execution.

Globally, the momentum was stronger. The European Union moved through successive Anti-Money Laundering Directives, expanding registration requirements and tightening obligations for designated non-financial businesses and professions. Ultimately, the EU established the Anti-Money Laundering Authority (AMLA) in its 2024 package to deliver cross-border supervisory consistency. And the FATF’s revised Recommendation 24 in 2022 raised the bar further, shifting the mission from collecting beneficial ownership data to ensuring it is accurate, current, and verifiable, with timely access for competent authorities. Having a register is not the same as having reliable information, and regulators have spent a decade making that distinction explicit.

The 2020 FinCEN Files added a further dimension. Where the Panama leak exposed the formation agents who were enabling shell company abuse, the FinCEN Files implicated the banks themselves, showing that suspicious activity reports (SARs) were being filed on transactions that institutions continued to process. Together, these successive leaks sustained the political will that the Panama Papers first generated.

The data is only as good as what’s behind it

The Panama Papers exposed that beneficial ownership frameworks could be gamed in ways that left regulators technically satisfied but substantively blind. Nominee arrangements created paper trails that went nowhere, and outdated register entries gave the appearance of compliance while concealing real control.

The lesson that proved most durable is that transparency requires verification, accessibility, and enforcement working together. A register without verification is a filing cabinet, verified data without accessible reporting channels is compliance theater, and accessible data without enforcement consequences for misrepresentation is an honor system.

For compliance professionals today, this translates into a concrete operational expectation. Enhanced scrutiny for complex legal entity customers is not optional. Nominee arrangements, offshore links, unexplained control structures, and identifying a politically exposed person (PEP) are not risk factors to note and move past. They are the scenarios that point to where the framework is most likely to fail, and examiners know it.

Where the picture gets complicated

Today, further progress is real, but uneven. In the US, the Corporate Transparency Act of 2021 was the most ambitious attempt to extend beneficial ownership reporting to companies themselves, not just the financial institutions serving them.

Under FinCEN’s March 2025 interim final rule, that ambition has been significantly narrowed: US-formed entities and US persons are now exempt, with reporting obligations falling primarily on certain foreign entities registered to do business domestically. That outcome followed a prolonged and contentious legal battle, involving multiple conflicting injunctions, a Supreme Court intervention, and sustained pushback from small business and industry groups, which ultimately made a political resolution rather than a judicial one the path of least resistance for the U.S. Treasury Department.

听The core problem shone by the Panama Papers leak in 2016 remains unresolved. A decade of regulatory response has only narrowed it.

Real estate reporting faces its own legal turbulence, with the Residential Real Estate Rule vacated and on appeal; and investment adviser AML coverage has been pushed to 2028, a delay driven in part by industry objections and competing agency priorities. These are not minor footnotes; rather, they are meaningful gaps in a system that was supposed to be closing.

Enforcement outcomes globally have been equally inconsistent. Panama’s own courts in a major Panama Papers-related trial in 2024. And Germany charged , the firm’s co-founder, in 2026. Jurisdiction still matters enormously, which is precisely what offshore structures were designed to exploit.

The durable lesson

Of course, none of this means the decade of reform was without consequence. It simply means the work is not done.

The Panama Papers’ most important legacy is not any specific regulation; rather it鈥檚 a permanently elevated expectation around knowing your customer, not just by name, but by ultimate beneficial owner, control structure, the credibility of information on file, and the ongoing monitoring that keeps that picture current. The most effective AML programs treat beneficial ownership as a living element of the customer relationship, not a checkbox at onboarding.

Still, the core problem shone by the Panama Papers leak in 2016 remains unresolved. A decade of regulatory response has only narrowed it and made it significantly harder to exploit, but as compliance professionals know better than most, the absence of a finding is not the same as the absence of risk.

You can find out more about the challenges of fraud identification and prevention here

Key insights:

• • • Fraud management works best as a connected workflow 鈥斕鼳ligning corporate fraud, AML, compliance, and investigation teams can strengthen visibility and response.

    • Monitoring must move beyond on-boarding听鈥� Existing customers require ongoing risk-based review, smart alerts, and transaction monitoring that can identify potentially suspicious behavior without overwhelming teams.

    • AI can accelerate investigations, but humans remain essential听鈥� AI-driven automation helps process data and prioritize alerts; however, skilled analysts are still needed to provide context, judgment, and industry expertise.

Fraud prevention represents only the first step in comprehensive fraud management. Organizations must develop robust detection and investigation capabilities to identify fraudulent activity and respond effectively.

Indeed, the most successful organizations think about fraud management in a systematic way, says Andrew Pellington, a senior director in Risk & Fraud solutions at 成人VR视频. 鈥淭he most successful organizations think about fraud management in more of a workflow phase that moves systematically from initial prevention through ongoing detection and into detailed investigation,鈥� explains Pellington.

Phases of organizational structures

Understanding how these phases interconnect and then building the proper organizational structures to properly execute them can help corporate risk, fraud & compliance teams create the foundation for effective fraud protection. These phases include:

1. Build organizational alignment across fraud and compliance functions

One of the most significant structural shifts in fraud management is the convergence of corporate fraud and anti-money laundering (AML) departments. Historically siloed, these functions are increasingly merging because fraud and money laundering are deeply intertwined. Fraudsters commit fraud, obtain illicit proceeds, and then need to launder those funds 鈥� effectively, two sides of the same coin, Pellington notes.

That means, financial and non-financial institutions can benefit from unified teams sharing data, processes, and expertise; and this convergence extends beyond AML and fraud to prevention, detection, and investigation phases. Organizations can gain competitive advantage when these functions share integrated toolsets, consolidated data sources, and cross-departmental communication. Before sharing knowledge across institutions, however, organizations must first establish robust information sharing across their own departments.

2. Establish monitoring systems for existing customers and accounts

As your organization moves through the fraud management workflow, the focus shifts from high-volume account opening activities to continuous monitoring of existing customers and account holders. This phase requires different tools, processes, and resources than does prevention.

Monitoring 鈥� both proactively and reactively 鈥� allows organizations to identify suspicious patterns and behaviors, then sophisticated systems must track transactions across time, identify deviations from normal behavior, and flag accounts for review.

Proactively, organizations should segment customers by risk level and establish review cycles: monthly for high-risk customers, semi-annual for medium-risk, and annual for lower-risk accounts. Reactively, they should deploy adverse media and sanctions alerts against public records, coupled with transaction monitoring models that specifically identify potential money laundering or structuring patterns.

“As you move through the monitoring, now you’re looking at your existing customers and account holders, and then you get alerts thereafter,鈥� Pellington explains.

3. Implement alert systems and prepare for regulatory scrutiny

While effective monitoring generates alerts that bridge passive systems and active investigation teams, these alerts need to be calibrated to identify genuine fraud risks without overwhelming investigators with false positives. This requires regular tuning and coordination between technology and investigation teams.

Organizations should adopt scenario planning and war games to test their processes by simulating potential fraud cases, regulatory inquiries, and adverse media incidents. Fraud incidents are a matter of when, not if, Pellington says, and those organizations that proactively test their response processes 鈥� rather than waiting for actual events 鈥� will maintain regulatory confidence and demonstrate institutional readiness.

4. Leverage AI while maintaining human expertise in investigations

While AI-driven automation of some work processes is a big advantage, deeper dive investigations require specialized expertise that cannot be fully automated. This is where generative AI (GenAI) and agentic AI can create significant opportunities. Agentic AI can prescreen alerts and determine which warrant investigation; and GenAI can rapidly produce enhanced due diligence reports by pulling together transaction histories, communications, vendor relationships, and public records.

Automating this work frees specialized fraud analysts to focus on what humans do best 鈥� applying industry knowledge and making judgment calls. Indeed, investigation is equal parts art and science, Pellington explains, adding that AI excels at the science 鈥� processing data at scale, and humans excel at the art 鈥� understanding context, industry fraud typologies, and customer relationships.

5. Transform data into knowledge and wisdom

The final critical gap Pellington identifies is the journey from information to knowledge to wisdom. Organizations possess unprecedented volumes of data, yet many drown in it without extracting actionable intelligence.

More data doesn’t guarantee better decisions; and organizations must elevate information to knowledge, understanding what their peers are doing, what best practices exist, and which approaches work best for the organization. Wisdom then comes from sharing across institutions, learning from industry experts, and avoiding mistakes others have experienced. This requires deliberate peer learning and thought leadership engagement.

Preparing for the future of fraud

Fraud risks are evolving fast, and those organizations best positioned to keep up will be the ones that keep their teams connected, sharpen their investigative tools, and pair AI with human judgment to act faster and stay more resilient while proactively transforming data into actionable wisdom.

By implementing these five phases of fraud protection, organizations can improve their detection and investigation capabilities and create comprehensive fraud protection that evolves with emerging threats.

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