• • • 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 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 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 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.

You can find out more about ways to

Key insights:

• • • AI is supercharging old fraud schemes听鈥� By making synthetic identities, deepfake scams, and customer fraud faster, more credible, and harder to detect, AI is amplifying fraud and crime.

    • The real vulnerability may be internal silos听鈥� Institutions need to be on the lookout, because what looks like a credit loss, an HR issue, or a payment request may actually be part of a wider multi-vector AI-enabled attack.

    • Institutions already have the tools to respond听鈥� Through KYC and internal and behavioral data, financial institutions have the ability to respond to fraud threats 鈥� but only if teams connect and act together.

Fraud and crime existed long before AI, of course, but today鈥檚 technology delivers an acceleration in speed, scale, and success rate for fraudsters, resulting in billions of dollars in losses for victims. AI-enabled frauds on financial institutions by 2027 in the United States alone, and of detected fraud attempts on financial institutions use AI 鈥� and of these, 29% are successful.

To respond effectively to these threats, institutions need to implement a unified response that brings together departments that may not traditionally be partners. This cross-functional coordination should include not only the institution鈥檚 fraud and financial crime risk teams but also its credit risk, cybersecurity, and human resources functions.

And this response is critical, because today, financial institutions are being targeted by multiple types of AI-enabled attacks, including tactics such as:

• • • use of synthetic identities to circumvent know your customer/customer due diligence (KYC/CDD) controls and perpetrate fraud or launder money;
    • use of deepfake identities to gain employment, particularly by North Korean IT workers;
    • AI-enhanced 鈥淐EO frauds鈥� to deceive staff into taking unauthorized actions; and
    • Bank customers may be targeted by fraud too, presenting further risk to financial institutions.

Let鈥檚 look at these threat vectors individually:

Vector 1: Synthetic identities and KYC/CDD

Synthetic identities can be entirely fabricated or may use combinations of real and fabricated personal information to create a new identity. For example, a fraudster may construct a synthetic identity using a Social Security number exposed during a data breach combined with an AI-generated passport.

This threat is real and happening now: identifies that criminals have already used AI to successfully open accounts using falsified documents, photographs, and videos. And according to , synthetic identities were used to open as many as 3% of US bank accounts, representing millions of identities. Not surprisingly, these illicit accounts are used to commit fraud and launder the proceeds of money laundering.

Vector 2: North Korean IT workers

North Korean individuals have successfully gained employment as remote IT workers at American companies, often passing themselves off as US nationals using AI-generated face-swapping technology combined with proxy computers and false identity documents. North Korean IT workers are almost $800 million annually for the regime.

Institutions deceived into employing these workers are not only against North Korea, but they are also exposing commercially sensitive data and systems to an adversary state, increasing the possibility of theft, cyber-attacks, and extortion.

Vector 3: CEO Fraud

A 鈥淐EO fraud鈥� is a cybercrime in which an attacker impersonates an executive to deceive an employee into taking actions such as sending unauthorized wire transfers or disclosing sensitive information. AI accelerates these frauds by making them more personalized and credible.

In one of the more well-known examples, in an AI-enhanced CEO fraud in 2024 after the fraudster impersonated Arup Engineering鈥檚 CFO and requested a staff member to make several financial transfers. The criminals added credibility to the fraud by using a in which the target recognized many of their colleagues 鈥� unfortunately, all of them were deepfakes.

Vector 4: Frauds targeting customers

Where customers are targets, AI provides the scale, speed, and personalization to allow illicit actors to deliver individualized fraud. For example, whereas romance scams previously used repetitive scripts and re-used the same images of the romantic 鈥減artner,鈥� fraudsters can now use AI-generated messages, images, or videos, continuously adapting the execution of the scam to the target鈥檚 responses and behaviors.

Creating a cross-functional and unified response

The examples above demonstrate the diverse and highly sophisticated uses of AI by illicit actors, both adversary states and criminal networks. Detecting and responding to these illicit activities requires joint action between teams that may not traditionally work closely together.

For example, if an account holder fails to repay a loan, the credit team may consider it to be a default by a legitimate customer and write it off as a credit loss. However, if the account was opened using a synthetic identity, investigation may reveal other accounts that share similar customer data points or transactional patterns. This could reveal a network of accounts that are perpetrating a fraud or money-laundering scheme. To detect and respond effectively, joint action is needed between KYC/CDD on-boarding teams, financial crime investigators, and fraud and credit risk professionals.

Alternatively, for HR teams to effectively identify use of face-swapping videos during a hiring process, knowledge from the organization鈥檚 cybersecurity team, especially of deepfake indicators, would be valuable. If a North Korea IT worker is hired and only later identified, cybersecurity and sanctions teams must be involved in the response to mitigate data, network, and compliance exposures.

Detecting and responding to all illicit activities requires joint action between teams that may not traditionally work closely together.

Finally, all staff may be targeted by deepfake fraud, but those in senior positions or departments with financial authority are the most vulnerable. This means it is essential for institutions to deliver employee training using real-life case studies, 鈥渘ear misses,鈥� and scenarios drawn from across the institution and industry. This type of training will increase vigilance and minimize the likelihood of a successful attack.

For customers, financial institutions are well-positioned to identify indicators of fraud due to their extensive datasets of KYC/CDD records, transactional, and behavioral information. Institutions should enhance their customer relationships (as well as meet applicable regulatory requirements) by taking proactive measures to inform and protect their customers.

While AI has accelerated fraud and crime, financial institutions also hold valuable and relevant assets: the knowledge distributed across their cybersecurity, HR, credit risk, financial crime compliance, fraud, and KYC/CDD teams. By connecting these teams together, even in contexts in which these departments have not traditionally been partners, institutions will be well-positioned to protect both themselves and their customers from illicit actors鈥� sophisticated AI-enabled threats.

You can learn more about the fraud-fighting challenges faced by financial institutions and other organizations here

Key takeaways:

• • • Mandatory compliance mandates are growing 鈥� Pillar 2, DAC6, and other real-time reporting mandates are increasing obligations in dozens of jurisdictions today, and those tax departments without the infrastructure to meet these obligations are already behind.

    • Real-time documentation is critical 鈥� The window between a transaction occurring and a tax authority scrutinizing it is shrinking to near zero in some markets, meaning that documentation must exist at the moment it is generated, not reconstructed afterward.

    • Data quality is compliance quality 鈥� Real-time compliance brings with it heightened pressure to avoid incomplete or inconsistent inputs, because increasingly sophisticated analytics used by tax authorities will find them.

In 2023, a major European manufacturer was hit with a seven-figure penalty not because its tax return was wrong, but because it couldn’t demonstrate how it arrived at the right answer. No documented governance framework, no clear ownership, and no audit trail. The numbers were defensible, but the process wasn’t.

That gap 鈥� between getting the right answer and being able to prove it 鈥� is where corporate tax risk now lives.

Governments and tax authorities worldwide are to self-report accurately. They are building legal frameworks, digital infrastructure, and enforcement mechanisms to verify compliance in real time. And for tax departments accustomed to managing compliance on their own terms, the window for a comfortable transition is closing fast.

A global tightening

Tax governance requirements are intensifying on multiple fronts. In the United States, for example, the IRS’s Large Business & International division has significantly expanded its compliance campaigns, targeting transfer pricing, research & development (R&D) credits, and multinational structures. Section 174 of the 2017 Tax Cuts and Jobs Act now requires companies to amortize R&D expenditures over five or 15 years depending on where research occurs 鈥� a change that many tax departments are still working through while absorbing new obligations on top of it.

Internationally, the pace is faster still. The framework that the Organisation for Economic Co-operation and Development (OECD) created for its base erosion and profit shifting (BEPS) rules has been adopted by more than 135 countries. Pillar 2 鈥� the global 15% minimum corporate tax rate 鈥� is already in effect in dozens of jurisdictions and is actively reshaping how multinationals structure their tax affairs. These are not coming changes 鈥� they are current ones.

Mandatory disclosure regimes have expanded in parallel. The European Union’s DAC6 directive requires intermediaries and taxpayers to report potentially aggressive cross-border arrangements, with penalties in some member states reaching hundreds of thousands of euros. The United Kingdom’s Senior Accounting Officer regime goes even further, placing personal legal accountability on named senior executives for the adequacy of their company’s tax accounting arrangements. Similar regimes are expanding in Australia, Canada, and Brazil.

These are not isolated experiments. They represent that is not going to reverse any time soon.

The real-time reporting challenge

That means, corporate tax departments must respond to this shift because the traditional audit model 鈥� authorities review historical returns and request documentation years later 鈥� is being replaced in a growing number of markets. Spain, Hungary, and South Korea already require taxpayers to submit transactional data directly to tax authorities through mandatory electronic systems. The EU’s Value added tax (VAT) in the Digital Age initiative will extend similar requirements across all 27 member states beginning in 2028.

For tax departments, this reporting compression is the central operational challenge of the next five years. A team that once had 12 to 18 months to reconstruct documentation for an audit now needs that documentation to be accurate and defensible at the moment it is generated. That requires a fundamentally different operating model 鈥� not just better record-keeping, but automated data capture and real-time reconciliation built into core financial systems 鈥� along with the ability to transfer that documentation electronically in real time.

3 actions tax departments must take now

To begin to address this dramatic change, corporate tax departments need to act now, taking steps that include:

1. Building a formal governance framework

Tax departments need written governance frameworks that clearly define what party owns each compliance decision, how decisions are reviewed and approved, and what controls exist to catch errors before filing. This means named ownership of obligations, documented sign-off processes, and regular internal reviews against a compliance calendar.

In the UK, this is already a legal requirement ; and similar standards are emerging in Germany, Australia, and across the EU. A framework should cover at minimum; the ownership of each material filing obligation; the review and approval chain for positions taken; escalation procedures for uncertain tax positions; and a schedule for internal control testing. Without these processes in place, tax departments could face regulatory penalties, personal liability for senior leaders, and reputational damage that may be difficult to recover from.

2. Fixing the data access problem

Tax departments consistently lack reliable, timely access to the financial data they need. This is primarily an organizational problem, not a technology one. Tax functions often sit downstream from finance systems designed without tax requirements in mind 鈥� meaning data often arrives aggregated, reclassified, or stripped of the granularity needed for compliance work.

Solving this requires tax leaders such as finance, IT, and business operations 鈥� not just to request data, but to influence how that data is captured at its source. That means participating in enterprise resource planning implementations, establishing data requirements for new business lines before they launch, and building direct feeds from source systems rather than relying on manual extracts.

3. Treating data hygiene as a compliance control

Tax authorities in the UK, the Netherlands, Germany, and the US are deploying advanced analytics to identify anomalies in corporate filings. Unexplained variances between statutory accounts and tax returns, inconsistencies in intercompany pricing, or mismatches between VAT and corporate income tax data could all trigger closer scrutiny.

Data hygiene must be treated as a compliance control, not an IT issue. In practice that means establishing reconciliation checkpoints between source data and tax inputs, maintaining documented data lineage so any figure in a return can be traced to its source, and conducting data quality reviews before filing deadlines 鈥� not after.

The bottom line

The regulatory trajectory is set, so that means the question for tax leaders whether their department will be ready when tested. Governance, data access, and data quality are no longer back-office concerns 鈥� they are the foundation upon which defensible compliance is now built.

Tax department leaders need to build that foundation now, before the examiner asks.

You can find out more about

Key highlights:

• • • AI governance is hard to prove in practice 鈥� While our research shows that 44% of companies publish an AI strategy, 76% of those same companies show no evidence of having policies to evaluate the quality of data used to train AI systems.

    • Workers are being left under-prepared and under-protected 鈥� Only 14% of companies have policies to mitigate the negative impacts of AI on workers, and only 31% offer any reskilling or training programs around adapting to an AI-integrated workplace.

    • Human rights and ethics appear an afterthought in AI governance 鈥� Almost three-quarters (72%) of companies conduct no AI impact assessments, and less than 1 in 10 companies conduct ethical or human rights assessments.

There is a widening chasm at the heart of corporate AI governance, according to a new report, , published by the 成人VR视频 Foundation and the United Nations Educational, Scientific and Cultural Organization (UNESCO).

The Foundation鈥檚 analyzed publicly available information from nearly 3,000 companies across 11 industry sectors, creating the most comprehensive picture yet of how organizations are managing AI.

Beneath the surface of corporate AI governance mechanisms, divergence between the speed of AI adoption and meaningful human oversight is growing. The report’s findings make clear that this is no longer a gap that organizations can afford to ignore, especially when backlash against is growing and are solidifying among consumers in the United States.

Data highlights the illusion of AI governance

Businesses of different sizes and across multiple sectors are adopting AI technology at a rapid pace. When governance exists only in the wording of a strategy or company vision, however, the people most affected by AI systems 鈥� workers, consumers, and communities 鈥� are left vulnerable. According to the report:

• • • 44% of companies publicly communicate having an AI strategy. However, a gap in AI governance is evident as more than three-quarters of those companies (76%) do not seem to have policies to evaluate the quality of data used to train AI systems.
    • 40% of companies report board- or committee-level oversight of AI. At the same time, strategic signals do not necessarily indicate operational capacity or day-to-day governance. In fact, less than one-third of all sampled companies claim to have an additional team or resource dedicated to AI governance. Moreover, limited information is publicly disclosed on the teams, processes, and accountability mechanisms that translate intent into action.

Workers are being left behind

Research by the International Monetary Fund finds almost , highlighting the acute nature of concerns about job displacement and declining opportunities for some groups. Without sufficient oversight, AI can threaten workers’ rights, amplify bias, and increase surveillance and work intensity, which can enable inhumane decision-making at scale.

The TR Foundation/UNESCO report notes that many companies are adopting AI without the safeguards needed to support workers and help them to adapt to the changes this technology brings. Less than one-third of companies were shown to offer training and reskilling programs for employees who may be adapting to an AI-integrated workplace. Even within the 31% of organizations in which these training programs exist, there is a vast variation in the scope and depth of the training offered.

In fact, many company training programs are not enterprise-wide or structured. Instead, they are ad-hoc or limited to leadership roles. This lack of investment in talent risks undermining the significant investment that companies are making in AI.

Despite growing pressure from regulators, policymakers and social justice campaigners, the ethical impact of AI appears poorly governed, with companies sharing limited information publicly.

The picture on worker protections is equally concerning. Only 14% of companies have public policies in place to mitigate the negative impacts of AI systems on workers, the report shows. This means the majority of companies either have no policies in place or do not publicly communicate them.

What is more troubling is that when workers experience harm, there is almost nowhere for them to turn. Only 2% of companies indicated they had a complaints mechanism 鈥� a critical early warning system for potential concerns. The findings suggest many organizations lack a mechanism for AI-related internal complaints beyond the broad generic complaint channel, and this is compounded by low awareness of the areas in which AI systems may infringe employees’ rights and protections.

Ethics and human dignity as an afterthought

Despite growing pressure from regulators, policymakers and social justice campaigners, the ethical impact of AI appears poorly governed, with companies sharing limited information publicly.

Human rights and ethical use of AI are treated as secondary considerations to compliance, according to our research. The majority of companies (72%) do not conduct any impact assessment with regard to AI. Only 7% publicly communicate conducting a fundamental or human rights impact assessment, and just 5% report conducting an ethical impact assessment.

Among those companies conducting some form of impact assessment, the focus skews sharply toward compliance rather than people. The most prevalent assessments are privacy or compliance-focused, with 18% of those companies that conduct some form of impact assessment reporting that they conducted a data protection impact assessment, and 14% reporting they conducted a privacy impact assessment.

How to center people in AI governance

Closing this governance gap is essential for companies in order to adopt AI responsibly and avoid costly legal, ethical operational, talent-related risks.

To support companies in navigating this challenge, offers a free survey to help companies map the areas in which AI is used across products, operations and services, and then benchmark those against peers their sector.

The report also contains case studies from companies that voluntarily shared their responsible practices with us. For example, German software company SAP intentionally designs and deploys its internal AI systems with a human-in-the-loop in which AI automates repetitive tasks and supports decision-making while final judgment and complex problem-solving remain firmly in the hands of employees.

As AI becomes part of core business infrastructure, companies must move beyond statements of intent and toward measurable AI governance.

In another example, BASF, a German chemical conglomerate, has jointly agreed with its workers’ councils on a general reskilling program that covers technical, hard, and soft skills. Finally, Canadian telecom company TELUS’ Indigenous Advisory Council provides guidance on AI ethics issues that directly affect indigenous communities.

Next steps for companies

The TR Foundation/UNESCO report highlights the most impactful concrete commitments that companies can take now to future proof against AI-related risk, including:

• • • investing in structured, enterprise-wide worker-reskilling programs that measure outcomes, not just participation;
    • establishing enforceable human rights impact assessments as a standard part of AI deployment, not as an optional addition; and
    • creating accessible, AI-specific internal grievance mechanisms so that workers and users have a genuine pathway to raise concerns and seek remedy.

As AI becomes part of core business infrastructure, companies must move beyond statements of intent and toward measurable AI governance. While this data demonstrates clear governance gaps, it also presents an opportunity for companies to take the lead on implementing responsible AI that operates openly in the public interest.

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Key insights:

• • • Define your risk appetite 鈥� A clearly defined fraud risk appetite aligns prevention efforts with strategic objectives and ensures accountability by establishing acceptable levels of fraud risk across the organization.

    • Create a fraud-specialized team 鈥� Dedicated ownership of the vendors that supply fraud solutions by a fraud-specialized team 鈥� rather than by the procurement function 鈥� is critical to maximizing technology performance and adapting to emerging threats.

    • Establish a specialized prevention division 鈥� The rise of sophisticated scams demands the creation of a separate, specialized prevention division to avoid overburdening core fraud teams and ensure targeted, effective responses.

Corporate fraud represents one of the most significant risks facing organizations today. Yet many companies lack the structured governance and technology infrastructure needed to combat fraud effectively.

The solution requires that comprehensive fraud prevention frameworks be built on clear governance, proper technology deployment, and data-driven insights, according to Aaron Frye, Founder & CEO of Lucid Point Consulting. Organizations that implement these five pillars create resilient fraud prevention functions capable of identifying and preventing fraud before it impacts results. These five pillars include:

1. Develop a fraud risk appetite

Effective fraud prevention begins with a well-defined fraud risk appetite that tells the right story to the right stakeholders. Your framework must communicate to your board, executive leadership, and operational teams the level of fraud losses your organization should tolerate, and in which areas you should prioritize fraud prevention investments.

The fraud risk appetite framework must address several key considerations; for example, it should define the level of fraud risk that aligns with the organization’s growth objectives, identify the areas of greatest vulnerability, and evaluate which investments will yield the strongest return. Equally important is the ongoing monitoring and communication of progress through regular reporting on fraud risk metrics, vendor assessments, and investigation outcomes. These actions demonstrate to stakeholders that fraud prevention remains an active priority for the organization and ensures that fraud risk continues to inform organizational decision-making.

2. Establish clear ownership of risk-solution vendors

Many organizations invest significantly in fraud detection tools only to see disappointing returns. The problem often lies not in the tools themselves, but in unclear ownership and accountability for their performance.

Organizations that implement these five pillars create resilient fraud prevention functions capable of identifying and preventing fraud before it impacts results.

If your organization lacks a designated person or team within your fraud strategy function whose job it is to ensure the risk-solution tools you鈥檙e getting from vendors are the best for your enterprise, you likely aren’t getting the most out of your vendors. This dedicated fraud service ownership role must act as your internal champion, evaluating vendor performance, staying current with product enhancements, and ensuring integration with other fraud prevention initiatives.

Critically, procurement, sourcing, and vendor management functions should never own this role. These teams, by the nature of their titles and responsibilities, don’t prioritize fraud. They lack the specialized knowledge required to assess whether your fraud detection technology is performing optimally or adapting to emerging threat landscapes. Without dedicated fraud expertise overseeing your technological investments, advanced tools sit underutilized and critical fraud signals go undetected.

3. Develop a fraud governance function

Every organization should have a dedicated fraud risk governance team within its fraud risk management organization. This governance function serves as your second line of defense, working proactively to reduce operational chaos within your fraud strategy, operations, and investigation groups.

If a non-fraud governance function owns fraud governance, you are guaranteed not to be getting the best form of governance. Fraud is a specialized discipline requiring dedicated expertise and focus; and your governance team must develop policies, establish standards, monitor control effectiveness, and ensure consistent application of fraud prevention practices across the enterprise.

4. Document existing risks and resource gaps

One of the most important responsibilities of your fraud governance function is identifying and documenting the areas related to fraud risk that your current fraud risk teams don’t have time to review. Due to capacity constraints, it is impossible for many fraud risk teams to cover all open gaps. Your organization must understand those open gaps and not be ashamed to address them.

Create an action plan that documents open risk and self-identified issues that your current team cannot adequately address. This transparency demonstrates clear-eyed realism about your organization鈥檚 limitations and creates the business case for requesting additional resources or engaging external consultants to help close these risk gaps.

5. Address the growing scam-prevention challenge

needs its own prevention strategy division within your fraud risk function. Compromised business email, investment scams, and vendor fraud schemes represent an entirely new category of fraud risk that demands specialized attention.

Every organization should have a dedicated fraud risk governance team that serves as its second line of defense, working proactively to reduce operational chaos within corporate strategy, operations, and investigation groups.

There has never been a full manageable grip on fraud prior to the spike in scams. Therefore, you cannot expect your existing fraud risk teams to tackle a new wave of scams as a priority as well as to manage traditional fraud prevention responsibilities. Your core fraud function manages internal control systems, transaction monitoring, and investigation protocols. Adding comprehensive scam prevention to this workload without dedicated resources guarantees that identifying and preventing scams will receive insufficient attention.

Establish a dedicated scam-prevention division focused specifically on emerging scam threats, employee education, scam-specific prevention technology, and response protocols. This specialized approach ensures sophisticated scam schemes receive the expertise and resources necessary while your core fraud function continues addressing traditional fraud prevention requirements.

Going forward into the fight against fraud

In an era of escalating fraud threats, reactive detection is no longer sufficient. Organizations must adopt a proactive stance grounded in strong governance, clear accountability, and strategic resource allocation.

By defining a fraud risk appetite, assigning ownership of fraud prevention tools, strengthening governance, documenting unaddressed risks, and establishing a dedicated scam prevention function, companies can build resilient, forward-looking fraud prevention frameworks. These five pillars enable organizations to anticipate threats, allocate resources effectively, and protect both financial performance and reputational integrity.

Today, the path to fraud resilience begins not with technology alone, but with deliberate, enterprise-wide commitment to proactive risk management.

You can find out more about ways to

Key insights:

• • • Prediction markets sit in a regulatory gray zone 鈥� Prediction markets鈥� economic function often looks much closer to gambling than traditional finance.

    • That ambiguity creates an AML blind spot 鈥� This blind spot allows potentially weaker controls around KYC, source of funds, sanctions screening, and suspicious activity reporting.

    • Banks and payment processors should focus on actual risk, not labels 鈥� Reputational, legal, and financial crime risk exposure can arise long before regulators clarify the rules.

Prediction markets have grown into a multi-billion-dollar ecosystem, offering the ability to enter into a contract to predict the outcomes on everything from elections and sports games to economic data and weather events. Yet as these platforms expand, they operate in a regulatory gray zone that raises serious questions for banks, payment processors, and compliance professionals.

Yet, the classification question that regulators and financial institutions continue to debate is not merely academic. It determines whether prediction market platforms will face the same anti-money laundering (AML) and know-your-customer (KYC) obligations as casinos and sportsbook venues, or whether prediction markets can continue to operate with minimal compliance oversight. This distinction has real consequences for the financial system.

鈥淧rediction markets are not just a classification problem, they represent a structural gap in how financial crime risk is currently understood and managed,鈥� says James Lephew, Founder & CEO of , a Charlotte-based consulting firm that serves major gambling operators and financial institutions globally.

Clarification is required in classifying this sector

Prediction markets occupy an ambiguous middle ground. Market operators position their platforms as financial derivatives or forecasting tools rather than gambling venues, emphasizing price discovery and statistical analysis over chance-based wagering. A contract on the outcome of a presidential election or a sports event, they argue, reflects crowd-sourced probability estimates grounded in information aggregation, not gambling luck.

Yet the fundamental mechanics raise legitimate questions. A user who buys a contract predicting that a candidate will lose an election is, in economic terms, wagering money on an uncertain outcome. The distinction between betting on a football game and trading a contract on the outcome of that same game becomes difficult to defend from a regulatory standpoint 鈥� and this classification matters enormously.

The distinction between betting on a football game and trading a contract on the outcome of that same game becomes difficult to defend from a regulatory standpoint 鈥� and this classification matters enormously.

If prediction markets are treated as gaming operations, they trigger Title 31 obligations under the Bank Secrecy Act, including currency transaction reporting, suspicious activity reporting (SAR) requirements, and comprehensive KYC procedures. If on the other hand, prediction markets are classified more akin to financial markets, these requirements may not apply. Currently, many prediction market platforms claim financial market status, allowing them to operate outside gaming regulations and with potentially weaker AML controls.

There is a compliance gap

Without clear regulatory classification, prediction markets create a significant AML blind spot. Casinos must report cash transactions exceeding $10,000, conduct source-of-funds reviews, and maintain detailed customer profiles. Sportsbooks face licensing requirements, geolocation checks, and responsible-gaming safeguards. Prediction market platforms, by contrast, often operate with minimal reporting obligations.

This gap introduces concrete risks. Digital wallets and cryptocurrency channels can obscure the source of funds. Structuring and layering of sources become easier without robust verification, further clouding who exactly playing in these markets. Collusive trading through multiple accounts allows value transfer that may go undetected. And VPN use and foreign payment channels can enable sanctions evasion.

Further, without mandatory SAR reporting, suspicious patterns tied to money laundering, terrorist financing, or market manipulation may never reach law enforcement.

“What we’re seeing is an AML blind spot,鈥� says Lephew. 鈥淧latforms enabling financial flows with characteristics of gambling, but without the controls that regulators would normally expect.” Until classification catches up with the technology, he adds, this blind spot remains open 鈥� and exploitable.

Why this matters for banks and processors

Banks and payment processors that support prediction market platforms may carry significant reputational and legal risk if they haven’t conducted thorough due diligence 鈥� and they cannot rely on a platform’s self-classification as a financial market or forecasting tool. Nevada and other jurisdictions are actively examining whether these platforms constitute gambling, echoing concerns from the American Gaming Association that products carrying similar economic risks deserve similar regulatory treatment.

If a product allows participants to wager on uncertain outcomes and creates risk that is substantially similar to gambling, it should face AML and customer identification requirements proportionate to that risk.

“Risk must be assessed based on how the product actually behaves, not how it is marketed,” Lephew explains. And that means evaluating whether a platform applies robust KYC procedures, verifies the source of deposits and beneficial ownership, screens against sanctions lists, reports SARs to the government, prohibits contracts on high-risk events such as assassinations or terrorism, and uses geolocation controls to block users in restrictive jurisdictions. Those answers matter far more than whatever label the platform chooses, Lephew says.

The path forward

Regulators have several options. One approach applies gaming regulations uniformly, treating all prediction markets with economic characteristics similar to gambling as gaming operations subject to Title 31. A second approach creates explicit financial market classification with statutory AML obligations and enhanced scrutiny of high-risk contracts. A third option adopts a tiered or risk-based framework, classifying contracts on lower-risk events such as economic data or weather under financial market rules, while sports and election markets could face enhanced scrutiny. Violent outcome markets would be prohibited entirely.

Regardless of which path regulators choose, the principle should be the same: Classification should follow economic function. If a product allows participants to wager on uncertain outcomes and creates risk that is substantially similar to gambling, it should face AML and customer identification requirements proportionate to that risk.

Financial institutions should not wait for regulatory clarity. They should apply rigorous due diligence now, treating prediction markets with a heightened level of scrutiny appropriate to their actual risk profile rather than their claimed legal status.

The goal is not to eliminate prediction markets, but to ensure they operate within a framework that prevents money laundering, terrorist financing, and market abuse. “If it looks like gambling, behaves like gambling, and carries the same financial crime risk, it should be regulated accordingly,鈥� Lephew notes. 鈥淎nything less creates systemic exposure.”

You can find out more about the challenges financial institutions face in their anti-money laundering efforts here