Key insights:

• • • AI is supercharging old fraud schemesĚý— 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 silosĚý— 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 respondĚý— Through KYC and internal and behavioral data, financial institutions have the ability to respond to fraud threats — but only if teams connect and act together.

Fraud and crime existed long before AI, of course, but today’s 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 – 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’s 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 “CEO frauds” to deceive staff into taking unauthorized actions; and
    • Bank customers may be targeted by fraud too, presenting further risk to financial institutions.

Let’s 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 “CEO fraud” 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’s 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 – 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 “partner,” fraudsters can now use AI-generated messages, images, or videos, continuously adapting the execution of the scam to the target’s 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’s 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, “near misses,” 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’ 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 — 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 — 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 — 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 — between getting the right answer and being able to prove it — 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 — 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 — the global 15% minimum corporate tax rate — is already in effect in dozens of jurisdictions and is actively reshaping how multinationals structure their tax affairs. These are not coming changes — 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 — authorities review historical returns and request documentation years later — 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 — not just better record-keeping, but automated data capture and real-time reconciliation built into core financial systems — 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 — 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 — 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 — 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 — 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 — 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 — 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 — 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 ¶¶ŇőłÉÄę Foundation and the United Nations Educational, Scientific and Cultural Organization (UNESCO).

The Foundation’s 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 — workers, consumers, and communities — 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 — 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.

You can learn more about

Key insights:

• • • Define your risk appetite — 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 — Dedicated ownership of the vendors that supply fraud solutions by a fraud-specialized team — rather than by the procurement function — is critical to maximizing technology performance and adapting to emerging threats.

    • Establish a specialized prevention division — 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’re 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’s 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 — Prediction markets’ economic function often looks much closer to gambling than traditional finance.

    • That ambiguity creates an AML blind spot — 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 — 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.

“Prediction markets are not just a classification problem, they represent a structural gap in how financial crime risk is currently understood and managed,” 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 — 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 — 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,” says Lephew. “Platforms 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 — 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 — 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,” Lephew notes. “Anything less creates systemic exposure.”

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

Key insights:

• • • Conflict of interest doesn’t start with bad intent — Often, conflict of interest starts with tenure, trust, and relationships that slowly blur the line between good judgment and personal interest.

    • The real exposure isn’t the fraud itself — The real damage from conflict of interest can be years of skewed vendor decisions, above-market pricing, and lost competitive ground.

    • Companies shouldn’t treat conflict of interest as a disclosure problem — Companies would do well to remember that often conflict of interest is really a data and systems problem.

His access logs were clean, so it took weeks to find out what actually happened. He had been borrowing colleagues’ IT logins, who had handed them over without much thought, even though they knew it broke policy. They just didn’t think it mattered. He used those logins to steer million-dollar contracts to selected vendors who were paying him kickbacks.

The company’s conflict of interest policy existed, and people had signed it. Yet, nobody checked whether anyone followed it. And this scheme wasn’t even caught internally. Fortunately, someone outside found it.

This gap between knowing something is wrong and believing it matters — that’s where conflict of interest lives.

The financial exposure goes well beyond the kickback itself

The kickback that was paid to an insider is not the real cost to the company. The real cost is what happens while nobody is looking. As a result of this fraud, this company didn’t even know they were experiencing years of sourcing decisions that were shaped by hidden interests, vendors who never got a fair shot, and pricing that stayed above market price because the person managing the relationship had a reason to keep it there.

Throughout many industries, the numbers back this up. The from the Association of Certified Fraud Examiners (ACFE) found corruption in almost half (48%) of all fraud cases. Median loss for corruption schemes was around $200,000, and the average scheme run for about 12 months before anyone catches on. Not surprisingly, 87% of conflict-of-interest fraud perpetrators had no prior criminal record. Indeed, they were trusted employees, not career criminals.

What makes this worse is that most organizations have no reliable way to catch it. Across industry guidance, compliance publications, and professional forums, a consistent picture emerges: The majority of organizations rely entirely on disclosure forms and self-reporting to manage conflicts of interest. Leading compliance expert, Rebecca Walker has publicly admitted that — and even though the tools exist, almost nobody is using them.

The statistics, however, only capture what gets caught. The psychology of how it starts is harder to measure — and more important to understand. Conflict of interest rarely begins with a plan to steal. Rather, it starts with tenure, trust, and relationships that make someone hard to replace. Over time, the line between good judgment and personal interest doesn’t get crossed, it just disappears.

Taking a more structured approach

Most companies rely on disclosure forms, ethics training, and a code of conduct. They want to tell people what a conflict looks like, ask them to report it, and assume they will. Too often, they won’t.

Disclosure forms ask employees to self-report behavior they often don’t recognize as problematic, and those who do recognize it worry they’ll be investigated or treated unfairly themselves. They’ve watched junior staff held to strict standards while senior leaders get a pass. Unfortunately, that teaches everyone the same lesson: Stay quiet. When 85% of companies with a code of conduct still have fraud at this scale, the problem is not what people know, rather it’s how the program is built.

These failures point to three specific gaps in how most organizations approach conflict of interest: i) how they gather information; ii) how they monitor risk; and iii) how they receive reports. A structured framework — one based on concepts of design, detect, and deploy — can address each one of these gaps directly, with each component being measurable in financial terms.

Design: Are you collecting facts or asking people to confess?

Take a look at how you approach employees around conflict-of-interest issues. Are you seeking information or just generally hoping the employee admits wrongdoing, even inadvertently. A better approach could be to ask specific questions: How long has the employee worked with this vendor? Can the employee award contracts to them? Does the employee have any ownership stake in a company on the approved vendor list?

Let the employee give the facts and then let the system make the call. When you separate sharing information from being judged for it, people actually share and you get better data. And better data means better procurement decisions. That is not a compliance win — that’s a business win.

Detect: Are you looking for conflicts or hoping someone speaks up?

Run your vendor list against your employee records and flag matching addresses, phone numbers, and bank accounts. Check public registries for shared directors between your staff and your suppliers. Look at who has been awarding contracts in the same role for years without rotating, and managers who keep hiring from former employers.

Any company with an ERP system and an HR database can run these checks quarterly. And ACFE data underscores the value in taking the proactive approach: On average, companies using automated transaction monitoring catch fraud within six months and lose about $83,000; and companies that wait for law enforcement to alert them to the fraud take 24 months and lose $675,000.

Deploy: Is your hotline a business tool or a poster on a wall?

Tips catch 43% of all fraud — more than audits, management reviews, and law enforcement combined. Companies with hotlines lose $100,000 in median fraud; but companies without them lose $200,000. A working tips hotline can cut your losses in half.

However, most hotlines are not functioning as intended. They exist on paper without the visibility, trust, or independence required to generate reliable reports. For example, a senior executive was steering contracts to his own associates. And even though a company hotline existed, the executive actually sat on the committee that received the reports. The tool was built to catch misconduct and was working properly, yet it was controlled by the person committing the fraud. The matter had to be escalated outside normal channels, and the senior executive was eventually fired for cause.

Almost half (46%) of employees who report misconduct face retaliation, according to the , from the nonprofit Ethics and Compliance Initiative. When that is the outcome, silence becomes the rational choice. If you want your hotline to work, promote it every quarter. Show people what was reported and what happened because of it. Make sure no single person can block or read a report before it reaches the right people. Being that proactive around your hotline will give employees proof that the system protects them.

Is it worth the investment?

Of course, the question is not whether your company has a conflict-of-interest policy, it most likely does. Rather, the question is whether you would know if someone were breaking it right now.

Companies that design better fact-gathering, detect through monitoring, and deploy trusted reporting can do more than catch fraud early. They can buy from better vendors, compete on fairer pricing, protect their board from liability, and build a culture in which raising a red flag is seen as protecting the business.

If the honest answer is that you would not know if someone was violating your company’s conflict of interest policy, then business case for being more proactive has already been made.

You can find more about how companies can best manage business fraud here

Key insights:

• • • Getting at the core legal question — In a case brought by defendant Ongkaruck Sripetch, the Supreme Court is deciding whether the SEC must prove investors suffered measurable financial loss before courts can order disgorgement, which would require fraudsters to give up illegal profits.

    • Why it’s high-stakes — Disgorgement is a major SEC enforcement tool — representing billions of dollars annually — so a new requirement to prove investor losses could sharply limit when and how much the SEC can recover.

    • How the justices seemed to lean (so far) — Questions at the argument before the Court suggested skepticism toward Sripetch’s position, with several justices asking why it would be an unfair penalty to take back ill-gotten gains and noting the practical difficulty of proving each investor’s exact loss.

If you’ve ever wondered how the U.S. Securities and Exchange Commission (SEC) actually gets money back after it catches a fraudster, one of its biggest tools, disgorgement, is now under the microscope. This week, the U.S. Supreme Court heard arguments in a case, Sripetch v. SEC, that sounds technical on paper but has at its core a simple question: When the SEC makes a fraudster give up illegal profits, does it have to prove that investors suffered measurable, out-of-pocket losses first?

The case centers on Ongkaruck Sripetch, who the SEC says pocketed illicit proceeds through a classic pump-and-dump scheme from 2013 to 2017. Pump-and-dumps often involve penny stocks in which a person will hype up the price of these thinly traded stocks, then sell into the price spike they caused and walk away richer. Other stock traders who bought into the hype are the ones left holding the bag.

Sripetch admitted violating securities law and, in his subsequent criminal case, was sentenced to 21 months in prison. Separately, in the SEC’s civil action, a federal court in California ordered Sripetch to repay more than $3 million in ill-gotten gains plus interest.

The Supreme Court case isn’t a serious argument against the SEC’s ability to seek disgorgement — numerous courts have recognized the remedy for years, and Congress has since written the SEC’s ability to pursue it into federal law. The core question in the case is narrower, yet crucial for the SEC’s mission. It asks whether the SEC must show that victims suffered pecuniary or economic harm before a court can order disgorgement. Federal appeals courts have split on that point, which is why the Supreme Court agreed to take the case.

What is disgorgement, exactly?

Think of disgorgement as a legal give it back order. If a person or company makes money by breaking the securities laws — say by manipulating prices, lying to investors, or running a Ponzi-style scheme — disgorgement is designed to strip the profits away from that wrongdoing and the wrongdoers. In theory, it’s not about punishing someone for being bad, rather it’s about making sure crime doesn’t pay.

In real markets, harm can be scattered across thousands of trades, mixed up with normal price swings, and hard to trace to one bad actor. Disgorgement, on the other hand, gives securities regulators a way to focus on the part that’s often the clearest: How much ill-gotten profit the fraudster made.

Indeed, that not a punishment framing is important because the SEC has other ways to punish those convicted of securities law violations — such as civil penalties, disbarment from serving as an officer or director, industry suspensions, and more. Disgorgement is supposed to be different — an action that aims at profits, not pain. The government’s position in the Sripetch case puts it bluntly: Disgorgement is meant to strip ill-gotten gains from wrongdoers, not to compensate victims for their losses.

And disgorgement is not a niche tool. The SEC regularly collects big sums of seized money through disgorgement. According to recent figures, the SEC obtained about $1.4 billion through disgorgement in fiscal 2025 (excluding certain amounts), and $6.1 billion the year before, which represented nearly three-quarters of its total financial penalties for that year.

Those numbers may help explain why this Supreme Court fight is being watched so closely: The outcome could either keep the SEC’s playbook intact or force it to do a lot more legwork before it can ask courts to order payback.

The arguments before the Court

Earlier this week, both sides argued before the Supreme Court as to the potential future use of disgorgement and what requirements the SEC might have to meet when requesting court to order it.

Sripetch’s argument — Lawyers for Sripetch told the Court that the SEC shouldn’t be able to get disgorgement unless it can show that investors actually suffered financial harm, such as a price drop caused by the fraud or some other measurable loss. If the SEC can’t prove that kind of harm, the lawyer argues, then making Sripetch pay money looks less like giving it back and more like an impermissible penalty that the SEC is not allowed to levy.

The government’s argument — Lawyers for the U.S. Justice Department, defending the SEC, said the proof-of-loss requirement makes no sense. Disgorgement, in their view, is about the defendant’s gains, not the victim’s losses. One government lawyer summed it up as a straightforward principle: Disgorgement is intended to ensure a defendant does not profit from their own wrongdoing.

At this week’s argument, the justices sounded (at least generally) more sympathetic to the government than to Sripetch. Justice Amy Coney Barrett pressed the defense on its basic logic: If the court is only taking away ill-gotten gains — money the wrongdoer was never entitled to — why is that a penalty at all? Justice Ketanji Brown Jackson made a similar point, suggesting disgorgement would only feel like punishment when someone is forced to pay money that was rightfully theirs.

When Sripetch’s lawyer suggested the SEC should have to identify and prove each victim’s dollar loss, Justice Sonia Sotomayor’s response was basically, Why would anyone bother? If the SEC has to run a mini-trial on every investor’s exact harm just to reclaim the fraudster’s profits, disgorgement would be unworkable in many cases.

The practicality of that point is a big deal in securities fraud. In real markets, harm can be scattered across thousands of trades, mixed up with normal price swings, and hard to trace to one bad actor. Disgorgement, on the other hand, gives securities regulators a way to focus on the part that’s often the clearest: How much ill-gotten profit the fraudster made. The idea is deterrence-by-math — if you can’t keep the profits, the incentive to run the scheme shrinks.

The Supreme Court’s ruling, when it comes, could re-shape how the SEC negotiates settlements, litigates fraud cases, and talks about remedies and punishments going forward.

Still, some justices raised broader concerns about how disgorgement gets used in the real world, such as whether certain applications start to look punitive, or whether they raise questions about a defendant’s right to a trial by jury. However, the Court also seemed interested in deciding only the question of the requirement to prove victims’ losses and leaving those bigger constitutional debates for another day.

Why this matters (even if you aren’t the SEC)

If the Supreme Court agrees with Sripetch and requires proof of investor pecuniary harm, the SEC could face a higher hurdle in cases in which misconduct is real, but losses are tough to quantify on a trade-by-trade basis. That could mean fewer disgorgement awards, smaller ones, or more pressure to rely on classic penalties instead.

If the Court backs the government, however, disgorgement stays what it has largely been — a fast, flexible way to reclaim profits from securities fraud and a core part of how the SEC tries to keep the securities markets honest.

Either way, the ruling will shape how the SEC negotiates settlements, litigates fraud cases, and talks about remedies and punishments going forward. With the Court expected to issue its decision by the end of June, securities lawyers and stock market mavens will be keeping an eye on this case.

You can find more about the challenges facing the SEC here

Key insights:

• • • Human rights and environmental sustainability in sports are inseparable — Environmental harms from major sporting events — such as pollution, extreme heat, and flooding — directly undermine fundamental human rights including health, housing, and safe working conditions.

    • Mega sporting events require an integrated, lifecycle-wide approach — From supply chains and stadium construction to urban planning and event delivery, the sports industry’s environmental footprint and human rights impacts span the full lifecycle of these events, demanding a single, integrated playbook.

    • Accountability extends to sponsors and partners, not just hosts and organizers — As scrutiny from regulators, media, and civil society grows, sponsors and corporate partners are increasingly seen as responsible for the combined human rights and environmental impacts of the events they support.

This blog post was co-written with Sreeratna Kancherla and Anna J. Christians of the Henekom Group.

Sports are entering a defining decade. The convergence of climate and nature risk, growing environmental accountability, and increasing scrutiny of how mega sporting events affect the communities that build and host them has brought a long-overdue challenge to the center of sports governance.

Due to their scale, frequency, and global reach, the upcoming FIFA World Cup 2026 and the 2028 Olympics to be held in Los Angeles, alongside competitions such as the 2027 Rugby World Cup and the ICC Men’s T20 World Cup, form part of an ambitious pipeline of major events in a generation. How the sports sector responds to that challenge will shape how the next era of global sport is planned, delivered, and remembered.

Human rights due diligence during mega sporting events and environmental sustainability are often thought of as neighboring agendas, related but managed separately. In practice, however, they are inseparable. When air quality deteriorates, the right to health is at stake. When flooding displaces communities, the right to housing and livelihood is at stake. When extreme heat makes outdoor labor dangerous, the right to safe working conditions is at stake.

The environment is the condition in which human rights are either protected or violated, and sustainability, properly understood, is the commitment to preserving those conditions for current and future generations.

The need for an integrated playbook

The case for an across the lifecycle of sport reflects the scale and complexity of the sporting industry’s impact, with emissions comparable to those of a midsize country, according to . The industry’s heavy reliance on plastics across stadiums, equipment, and apparel contributes to pollution that worsens the global environmental crisis. And those environmental choices carry human consequences at every stage, for the workers who build the facilities, the residents who live alongside them, and the fans who attend the events.

The environmental footprint of the sports industry touches people across the entire lifecycle of a major event. The supply chains necessary to deliver a mega-sports event span facility development, apparel, technology, and food & beverage. These industries are among the highest risk for labor exploitation, migrant worker abuse, and unsafe working conditions. When a host city builds a stadium and hosts events there, the environmental impact is measurable and so is the human rights impact on the workers building the stadium. Indeed, this impact extends to the neighborhoods that may be displaced to make room for it, and to the residents left to live alongside its infrastructure once the event has ended.

You can find more about the resources, tools, and information that cities and organizations need to addressĚýhuman trafficking around large-scale sporting events at the Thomson Reuters Institute’s Large-Scale Public Events Toolkit here

In addition, major events that rely on street circuits or temporary urban infrastructure can significantly reshape public space and surrounding neighborhoods. Air pollution, construction zones, and rising short-term rental demand also may displace residents and the unhoused population, restrict access to services, or place pressure on already fragile housing markets. In these cases, mega-sports event planning intersects directly with citizens’ rights to housing, mobility, and access to public space.

Expanding accountability

, rooted in the , is the structured process that makes those consequences visible and gives sustainability strategy its human accountability. Because environmental and human rights impacts are inseparable in practice, that accountability extends beyond organizers and host governments to the sponsors and corporate partners of the event. Many operate in sectors which already face scrutiny over their global supply chains; and therefore, alignment with a contentious event can amplify these vulnerabilities while inviting additional public and regulatory attention.

As the regulatory landscape, advocacy groups, and the media intensify their focus on the impact of these mega-sport events, sponsors are increasingly seen not only as influential stakeholders, but as actors with a degree of responsibility for the combined environmental and human rights impacts of the events they fund and support.

Moving from principle to practice

For example, Mercedes-Benz Stadium in Atlanta — home of the NFL’s Atlanta Falcons along with a venue for soccer and concerts — demonstrates that environmental performance and community impact are the same priority and can be pursued through a single design brief. Indeed, it was the first stadium worldwide to receive for zero waste, and its 2.1-million-gallon system helps prevent flooding in neighboring communities. Additionally, the stadium created targeted employment through the and delivered staff training to more than 700 people.

The same integrated logic is now being applied at the event level. Ahead of the FIFA World Cup 2026, host city organizing committees in Houston and Dallas introducedĚýthat address labor exploitation, including human trafficking risks, alongside targeted environmental measures. These measures are treated as a single procurement workstream to be addressed through an integrated response.

Leadership, legacy & the decade ahead

The organizations that will define the next decade of global sports are those that treat human rights and environmental sustainability not as parallel strategies but as two expressions of the same obligation to the people and communities on which sports depend.

This means designing facilities with both environment and humanity in mind from the outset, managing worker rights and environmental standards together across supply chains, and placing extreme heat measures, labor protections, community access, and sustainability targets within a single accountable governance framework.

Governing bodies, organizing committees, sponsors, and host cities that act on this integrated approach have the opportunity to build systems that are more responsible, more durable, and more trusted to define what credible and future-ready sports event management looks like.

You can find more about the impact of mega-sporting events on communities here

Key insights:

• • • SAR volume is significantly underreported — Continuing and amended filings add approximately 20% to the official count yet remain invisible in trend analyses.

    • Filing activity is highly concentrated — A few large financial institutions dominate SARs volume, meaning trends reflect their practices more than systemic changes.

    • Agentic AI will drive a surge in SARs — Agentic AI risks increased noise over actionable intelligence, without addressing the unresolved question of whether current filings yield meaningful law enforcement outcomes.

The Suspicious Activity Reports (SAR) that financial institutions file with the U.S. Treasury Department’s Financial Crimes Enforcement Network (FinCEN) provide valuable insight, although they may not offer a comprehensive picture.

Prior to meaningful discussions regarding the future of SARs, it is essential for the financial crime community to clarify what is being measured. In 2025, for example, SAR filings of more than 4.1 million, representing an almost 8% increase compared to the total number of SARs filed in 2024.

Every figure FinCEN has published reflects original SARs only. Continuing activity SARs, which represent roughly 15% of all filings, are submitted under the original Bank Secrecy Act (BSA) identification number and never appear as new filings. Corrected and amended SARs add another 5% on top of that. This makes the real volume of SARs activity approximately 20% higher than what is reported.

The average community bank files fewer than one SAR a week, while the largest institutions file more than 500 a day.

Recent FinCEN guidance giving financial institutions more flexibility around continuing activity SARs sounds significant on paper, but as former Wells Fargo BSA/AML chief Jim Richards points out: “It won’t change the reported numbers — because those filings were never counted to begin with.” Financial crime professionals need to keep that gap in mind every time a trend line gets cited.

2025 was steady, not spectacular

There were roughly 300,000 SARs filed every single month of 2025, and the most notable thing is that nothing notable happened. That is likely a first on the volume side and worth acknowledging, but beyond that milestone the year did not hand financial crime professionals anything noteworthy. In a space that has dealt with pandemic distortions, crypto chaos, and fraud spikes that seemed to come out of nowhere, steady volume and predictable patterns are a little surprising. A quiet data set, however, is not the same as a quiet landscape, and financial crime professionals who are reading stability as stagnation may find themselves flat-footed when the numbers start moving again.

For example, one of the most underleveraged insights in the SARs space is just how concentrated filing activity really is. The numbers are stark: The top four banks file more SARs in a single day than 80% of the rest of the banks file in 10 years, according to 2019 data from a .

The average community bank files fewer than one SAR a week, while the largest institutions file more than 500 a day. “50 a year versus 500 a day,” notes Wells Fargo’s Richards, adding that such asymmetry has real implications for how the financial industry interprets trends. Meaningful movement in SARs data, up or down, is almost entirely dependent on what a handful of mega-institutions decide to do.

Not surprisingly, money services businesses (MSBs) are the second largest filing category, and virtual currency exchanges are almost certainly driving recent growth there, even if outdated category definitions make that difficult to confirm directly. Credit unions round out the top three.

The filing philosophy hasn’t changed and shouldn’t

Regulatory noise occasionally suggests that institutions should be more selective about what they file. However, compliance and legal reality have not shifted. No institution has ever faced serious consequences for filing too many SARs, and the cases that result in enforcement actions, reputational damage, and regulatory scrutiny are consistently about missed filings or late ones.

“You’re not going to get in trouble from filing too much,” Richards says. “Nobody ever has, and I doubt if anyone ever will.” For financial crime professionals, the calculus remains exactly what it has always been — when in doubt, file. That posture isn’t going to change, and frankly it shouldn’t.

Yet, here is where the SARs space gets genuinely interesting. Agentic AI use in SARs filings — systems in which multiple AI agents work through a case from screening to decision to documentation — is beginning to move from concept to deployment. The impact on filing volume likely will be significant.

The risk is a system flooded with AI-generated SARs of variable quality, creating more noise for law enforcement to sort through rather than sharper intelligence to act upon.

Whereas a small team today might work through a handful of cases a week, AI-assisted workflows could push that into the dozens. Multiply that across institutions already inclined to file rather than miss something, and the result is a coming surge in SARs volume that could play out over the next two to four years.

“Agentic AI has the potential to be a game changer on how we do our work,” Richards explains. “But I believe it’ll guarantee that there will be more SARs filed and not necessarily better and fewer SARs filed.” Indeed, the critical point for the financial crime community to internalize is exactly that.

The risk is a system flooded with AI-generated SARs of variable quality, creating more noise for law enforcement to sort through rather than sharper intelligence to act upon. Once the largest institutions adopt agentic AI as a best practice, others will follow quickly, and regulators will likely be several steps behind.

The value question can’t wait

The has been in place since 2014. Yet after 12 years of filings, the financial crime community still lacks a clear public accounting of whether that data has produced actionable law enforcement outcomes.

So, the question Richards is asking is one the entire industry should be asking: “Has anybody asked law enforcement?”

This question reflects a larger challenge that the industry needs to confront more aggressively, especially as AI technology is set to dramatically increase filing volume across the board. Increasing the volume without improving how the information is used does not represent progress. If SARs are not generating real investigative value, the solution is not to file more of them faster — instead, the pipeline should be fixed before it grows any bigger.

You can find more about the challenges that financial institutions face in managing SARs here

Key insights:

• • • Non-compliance is significantly more expensive than compliance — Data consistently shows the cost of non-compliance can be greater than proactive compliance investments.

    • Reputational damage and hidden costs often outweigh direct fines — Beyond financial penalties, the damage from legal fees, loss of customer trust, and operational disruptions from non-compliance can inflict long-term harm.

    • Strategic investment in compliance yields a competitive advantage — A robust compliance program builds trust, attracts investors, and demonstrates greater operational resilience in a complex regulatory landscape.

There’s a persistent myth in the business world that compliance programs are a necessary burden, a line item to be minimized and managed rather than invested in strategically. The data tells a very different story, however, and it has for quite some time. For organizations still treating compliance as an overhead expense, it’s time to reconsider the math and view the broader strategic picture.

The numbers don’t lie: Non-compliance costs more

Non-compliance costs are 2.65-times the cost of compliance itself, a finding that dates back to the of multinational organizations. While the average cost of compliance for the organizations in that study was $3.5 million, the cost of non-compliance was much greater. That means simply by investing in compliance activities, organizations can help avoid problems such as business disruption, reduced productivity, fees, penalties, and other legal and non-legal settlement costs.

According to a later report from from 2017 (the most recent set of analytical data on the subject), the numbers have only grown more striking. The study showed that average cost of compliance increased 43% from 2011 to 2017, totaling $5.47 million annually. However, the average cost of non-compliance increased 45% during the same time frame, adding up to $14.82 million annually. The costs associated with business disruption, productivity losses, lost revenue, fines, penalties, and settlement costs add up to 2.71-times the cost of compliance.

And these non-compliance costs from business disruption, productivity losses, fines, penalties, and settlement costs, among others aren’t simply abstract risks. They’re real, recurring, and measurable, and they don’t stop with the fine itself.

Beyond the fines themselves, legal costs are a significant and often underestimated component of non-compliance.

This gap between compliance and non-compliance provides evidence that organizations do not spend enough of their resources on core compliance activities. If companies spent more on compliance in areas such as audits, enabling technologies, training, expert staffing, and more, they would recoup those expenditures and possibly more through a reduction in non-compliance cost.

While the math here is straightforward, the strategic case is even clearer. Compliance isn’t overhead; rather, it’s an investment with a measurable, proven return.

The hidden costs: Legal fees, fines & reputational fallout

Regulatory fines get the headlines, but they represent only part of what non-compliance actually costs an organization — a cost that has only risen over time. As of February, a total of 2,394 fines of around €5.65 billion have been recorded in the database, which lists the fines and penalties levied by European Union authorities in connection with its General Data Protection Regulation (GDPR).

Beyond the fines themselves, legal costs are a significant and often underestimated component of non-compliance. Regulatory norms are shifting constantly and navigating them requires specialized expertise. As quickly as the rules change, outside counsel and compliance specialists must keep pace, and that knowledge comes at a price. Every alleged compliance violation triggers an immediate need to engage qualified counsel, adding to a cost burden that compounds quickly and unpredictably.

Then there is reputational damage, perhaps the most enduring consequence of all. The cost of business disruption, including lost productivity, lost revenue, lost customer trust, and operational expenses related to cleanup efforts, can far exceed regulatory fines and penalties. Consider , whose compliance failures around its anti-money laundering (AML) efforts became a cautionary tale for the industry. TD Bank’s massive $3 billion in fines from US authorities wasn’t just the result of a few missteps; rather, it was caused by years of deep-rooted failures in its AML program, pointing to a culture that prioritized profit over compliance.

The findings from both the 2011 and 2017 studies provide strong evidence that it pays to invest in compliance.

TD Bank’s failure to make compliance a priority not only led to a huge fine but also seriously damaged its reputation, with revising TD’s outlook to negative in May 2024, where it remains. This is the kind of a reputational stigma that can take years to repair.

Leveraging compliance as a competitive advantage

There is also a positive side of the ledger that often goes unacknowledged. A robust compliance program signals to investors, partners, and clients that an organization is well-governed and trustworthy. That reputation doesn’t just retain market value; it actively attracts it.

Organizations that cut corners in compliance risk engaging in a short-sighted, high-risk strategy that will ultimately result in a negative outcome for the organization. Businesses that take compliance seriously tend to operate with greater predictability, fewer surprises, and stronger stakeholder confidence.

The 2017 Ponemon and Globalscape and study found that, on average, only 14.3% of total IT budgets were spent on compliance then, not much of an increase from the 11.8% reported in 2011. This clearly indicates that organizations are underspending on core compliance activities in the short term and aren’t prepared to allot further resources as the years go on. That gap represents not just risk, but a clear missed opportunity.

“The findings from both the 2011 and 2017 studies provide strong evidence that it pays to invest in compliance,” explains Dr. Larry Ponemon, Chairman and Founder of the Ponemon Institute. “With the passage of more data protection regulations that can result in costly penalties and fines, it makes good business sense to allocate resources to such activities as audits and assessments, enabling technologies, training, and in-house expertise.”

The organizations that recognize compliance as a strategic function, not a reactive one, are the ones that will earn the trust of clients, the confidence of investors, and the operational resilience to weather an increasingly complex regulatory environment. The data is clear, and the choice is a critical one.

You can find out more about the challenges faced by corporate compliance professionals here