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 highlights:听

• • • The scale of risk demands urgent attention 鈥� The World Cup’s five-week span across three nations creates a human trafficking risk profile far beyond any previous North American sporting event.

    • Geographic exposure extends far beyond host cities 鈥� Unlike the Super Bowl, where risk is concentrated in one metro area, the World Cup’s national identity-driven fan engagement means every city in the US, Canada, and Mexico is effectively a participant city.

    • Cross-sector preparation is the most critical investment 鈥� Cutting down siloed operations among law enforcement, financial institutions, and NGOs is required, that means establishing financial institution task forces, training frontline bank branch employees to recognize trafficking indicators, sharing cross-sector information, and amplifying public awareness campaigns before the tournament begins is crucial.

The 2026 FIFA World Cup will be the largest sporting event ever hosted on North American soil, a tournament with 104 matches spanning more than five weeks across three nations and drawing an estimated 6.5 million visitors from around the world. While the United States hosts large sporting events like the Super Bowl each year, the World Cup brings with it the unique challenges of length of time, fan influx from around the globe, and geographic expansion.

Assessing the scale of human trafficking risk

To understand the magnitude of the human trafficking risk involved in events such as this, it is useful to apply a framework that accounts for three variables: i) the likelihood of a trafficking event; ii) the potential extent of damage; and iii) the duration of exposure. When that framework is applied to the 2026 World Cup, the human trafficking risk associated with the event registers high due to numerous factors.

For more on this, tune into the 成人VR视频 Institute’s latest “Clarity” podcast

The most significant differentiating factor of the World Cup is its time duration. The Super Bowl is a single-day event, and the Olympics run approximately two weeks. The 2026 World Cup spans more than five weeks across three nations, a duration that has no modern sporting equivalent. The last three World Cups, held in Brazil, Russia, and Qatar, offer limited comparative value given the substantial differences in legal frameworks, cultural contexts, and infrastructure. For purposes of risk assessment, this is why the Super Bowl represents the most relevant domestic benchmark, even though it falls considerably short as a true comparison.

Human trafficking evidence from the most recent Super Bowl

The most recent Super Bowl, held in the San Francisco Bay Area in February 2026, illustrates the scale of the human trafficking challenge. A coordinated anti-trafficking campaign conducted across 11 Bay Area counties resulted in the recovery of 73 sex trafficking victims, including 10 minors, and 29 arrests, all in connection with a single-day event.

Sex advertisement data from that period further substantiates the scale of human trafficking concern. In the months preceding the event, advertisement volume rose steadily before spiking dramatically during Super Bowl weekend and declining sharply in the days that followed. Analysis that was restricted to advertisements referencing the Super Bowl by name showed trend lines that remained essentially flat until the event itself, at which point volume surged significantly.

Likewise, examination of phone numbers associated with those advertisements revealed organized and purposeful movement. Nearly 500 unique numbers that had posted sex advertisements in other states in the preceding weeks appeared in San Francisco during the event.

The risk of human trafficking expanding beyond the host city is one additional insight uncovered during the anti-trafficking operation during the Super Bowl. Advertisements referencing the Super Bowl spiked simultaneously in Boston and Seattle, the home cities of the two competing teams. In the context of the World Cup, every city in the United States, Mexico, and Canada is effectively a participant city, and national identity rather than team affiliation drives fan engagement. The geographic distribution of risk is therefore exponentially greater than anything observed around the Super Bowl.

Hotspots of sex ads

What anti-trafficking partners should do now

Those organizations and institutions that take action in advance of the World Cup will be substantially better positioned to detect exploitation and protect vulnerable individuals. More specifically, these organizations should:

• Establish financial institution task forces in advance of the event 鈥� Convening local financial institutions to align on existing practices and identify gaps will aid in ensuring all parties are on the same page. It also establishes relationships and procedures that cannot be built effectively during a five-to-six-week surge in cross-border transactions. Activating established information-sharing mechanisms, such as the processes supporting the filing of and the , will be essential for detection and pattern recognition.
• Institute branch-level employee training at local financial institutions 鈥� Frontline employees possess local knowledge that no centralized system can replicate. A branch employee in a high-traffic urban location understands the patterns of their customer base and is often the first to recognize when something is amiss. What they frequently lack is the context in which to interpret that instinct and the guidance to act upon it. Addressing that training gap before the World Cup represents one of the highest-value preparedness investments available to financial institutions at this time.
• Dismantle institutional silos 鈥� Siloed operations, in which law enforcement, financial institutions, and non-governmental organizations (NGOs) each operate independently, represent the least effective organizational posture for an event of this scale. Institutions that establish cross-sector relationships and information-sharing commitments in advance will be meaningfully better equipped to respond.
• Develop and amplify public awareness campaigns 鈥� Research demonstrates that sustained public awareness campaigns and visible law enforcement presence reduce demand. Host cities, law enforcement agencies, and NGOs should treat this as actionable guidance in planning their response strategies.

The 2026 FIFA World Cup is not simply another major sporting event. The institutions, agencies, and organizations that approach it as such will find themselves unprepared for a scale of human trafficking risk that North America has never previously encountered.

You can find more about the resources, tools, and information that cities and organizations need to address听human trafficking around large-scale sporting events at听the 成人VR视频 Institute鈥檚 Large-Scale Public Events Toolkit here

Key insights:

• • • Unauthorized practice of law rules have repeatedly come into conflict with new forms of legal self-help 鈥� Each major wave of consumer-facing legal assistance has tested the boundaries of UPL doctrine and forced courts, regulators, and lawmakers to decide where legal information ends and legal advice begins.

    • Technology has expanded access to legal information faster than regulation has adapted 鈥� LegalZoom and other justice tech companies showed that legal tools could be delivered at scale, while UPL doctrine often struggled to accommodate new models of legal assistance designed for consumers with unmet legal needs.

    • The rise of AI makes the old UPL framework increasingly inadequate 鈥� As GenAI tools provide legal research, document assistance, and guided analysis directly to the public, regulators should move beyond the LegalZoom-era battles and consider a framework focused on consumer protection, transparency, and actual harm.

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

With three-quarters of state court cases including at least one self-represented party, and with 92% of Americans with a legal problem not getting the legal help they need, it鈥檚 not surprising that the unauthorized practice of law (UPL) is a concept that鈥檚 not far from people鈥檚 minds.

It does not have to be this way, of course, and there are solutions to the thornier issues with UPL; but first, it may be helpful to understand how we got to this place and how UPL has evolved.

Legal self-help in a pre-Internet world

In the late-1800s, before UPL was formally articulated, John Wells published “Every Man His Own Lawyer”, a widely circulated guide that explained legal principles and provided practical forms. Its popularity reflected sustained public demand for accessible legal information. Around the same time, the organized bar began to emerge, along with more structured efforts to define and protect the boundaries of legal practice.

By the early-1900s, auto clubs were providing legal help to their members, demonstrating an early form of a prepaid legal services plan that exists to this day, but with typically a wider array of services. As would be the case in later years, an economic downturn soon brought a fight as lawyers used threats of UPL to fight competition. Not long after the Great Depression began, the ABA formed the Committee on Unauthorized Practice of Law, and a wave of litigation ensued to essential end the offering from auto clubs.

Similar dynamics appeared later in the 20th century. In the 1960s, soon before the recession of the 1970s, Norman Dacey鈥檚 “How to Avoid Probate!” offered readers tools to manage estate planning without engaging a lawyer. The response included investigations and attempts to suppress the book. Courts ultimately clarified that providing general legal information, even when presented in a structured and practical format, does not constitute individualized legal advice and falls within the scope of protected speech.

Tech enters the equation

By the 1990s, these ideas had moved into a digital environment. Companies such as Nolo and Parsons Technology translated legal forms and guidance into software and the Texas State Bar sued in federal court. Although the bar initially prevailed, a legislative response introduced a software exception to UPL that remains in effect today, reflecting an early acknowledgment that technology-based tools required a different regulatory lens.

By early 2000s, LegalZoom extended these concepts at scale. By automating document creation across a wide range of legal needs, it brought structured legal tools directly to consumers in a more accessible format. While not the first provider of self-help legal resources, it demonstrated how technology could move online and operationalize these services at a national level 鈥� not surprisingly, this effort would face resistance at a whole new level.

Launched in 2001, LegalZoom argued that it just represented the modern evolution of books like those written by Wells and Dacey. The response from the legal establishment was ferocious. It began with state bar inquiries trying to understand what LegalZoom was offering, and as the Global Financial Crisis began in 2007, class action lawsuits and regulatory challenges followed.

These suits sought significant damages without alleging specific consumer harm, creating substantial pressure on a still-developing sector and signaled resistance to new models of service delivery. The objections were ostensibly about consumer protection, while more reflecting concerns about changes to established structures in the legal profession.

LegalZoom won some of the class actions and settled others on friendly terms, typically agreeing to limit the use of certain words in its advertising, paying some class member claims, offering its attorney-access plans on a complimentary basis, and paying attorneys鈥� fees.

Supreme Court precedents

Two U.S. Supreme Court decisions would prove highly important to the UPL battles. The first came in in which the Court ruled that companies could include class action waivers in arbitration provisions. Soon after, LegalZoom began implementing this type of arbitration provision to coincide with the resolution of several major class actions to make sustaining a class action against it in the future more difficult.

The second Supreme Court ruling to impact UPL came in in which the Court ruled that a state occupational licensing board cannot claim state-action antitrust immunity if a controlling number of its decision-makers are active market participants in the occupation it regulates and the state does not actively supervise the board. This decision put state bars at risk.

The fight that changed the conversation was the LegalZoom lawsuit against the North Carolina State Bar (NCSB) that was modeled after the result in the Dental Board matter. LegalZoom had built a prepaid legal services plan offering attorney access to its customers 鈥� a narrower version of what the auto clubs had offered in the past. These types of plans historically were supported by the ABA and National Association of Attorneys General, but a few states pushed back on LegalZoom offering one. Most notably, North Carolina objected and LegalZoom sued the NCSB for a declaratory judgment that it was not engaged in UPL as well as on antitrust and other grounds, leading to a settlement and cooperative legislation that cleared the way for LegalZoom to continue operations, including launching its legal plan, in that state.

Upon the case’s conclusion, University of Tennessee College of Law professor , LegalZoom fought the North Carolina Bar 鈥� and LegalZoom won. Barton opined that the 鈥淪outh Carolina [where the Supreme Court had found LegalZoom practices lawful] and North Carolina precedents will likely end all state bar action on UPL.鈥� He was largely correct, as future LegalZoom and other industry skirmishes would not amount to much, allowing the industry to thrive.

The future of UPL

Today, the LegalZoom fights look quaint. It was essentially a fight over the online equivalents to form books, when a few years later AI would explode onto the scene and upend everything. We now have everything from foundation models such as ChatGPT, Claude, and Gemini to legal specialists available to the public and generating research memos at the push of a button.

This, perhaps, brings us back to where we started. And now may be the time to ask whether a new system of regulation is needed around UPL, because no other justice tech company should have to run the gauntlet of fights that LegalZoom faced.

In the next part of this blog series, we will look at how the issues raised by UPL in the AI age may require a new regulatory solution, possibly one based on a consumer protection model that would replace today鈥檚 supplier-based regulations

Key insights:

• • • Accountability, not adoption, is the central governance challenge听鈥� With many institutions using AI a variety of tasks, informal “shadow AI” use is expanding without consistent oversight.

    • Justice systems now face a parallel governance problem 鈥� They must find a way to regulate AI while using AI inside the institutions that enforce rights, while allowing responsible innovation that improves efficiency and access to justice.

    • AI needs to be integrated into broader justice reform听鈥� Without strong data governance and clear boundaries between AI assistance and legal judgment, courts risk automating inefficiency, deepening inequities, and undermining public trust.

Even as AI governance frameworks remain mired in ongoing debate, justice systems are moving ahead with implementation. Courts and dispute resolution institutions are integrating AI into their operations to more efficiently digitize records and automate workflows.

This introduces the very real challenge of parallel governance. We must now determine not only how AI should be regulated, but how it operates within the very institutions responsible for enforcing rights.

And this intersection is no longer theoretical: Does AI governance strengthen fairness, preserve independence, and expand access 鈥� or does it undermine their very foundations?

From experimentation to embedded use

Across jurisdictions, AI is often framed as an administrative tool that can handle basic tasks such as transcription, translation, case triage, and more, as well as providing analytics to identify delays or inefficiencies.

These applications respond to real constraints, such as overburdened courts, limited resources, and persistent backlogs. Similarly, dispute resolution platforms are integrating AI to guide users through processes and structure negotiations.

However, this formal adoption tells only part of the story. AI is also entering justice systems informally. Judges, clerks, and lawyers are independently using general-purpose tools in their daily work, often without guidance, oversight, or a clear grasp of the tools鈥� implications for security, confidentiality, and discoverability. As one expert observed: 鈥淪hadow AI is already happening.鈥�

The absence of governance does not prevent AI use; and, in fact, it may encourage misuse. This shadow AI simply pushes AI usage into unstructured and unmonitored areas 鈥� the risk then becomes not adoption itself, but uneven adoption that evolves beyond institutional control.

It鈥檚 no longer a question that justice systems need to engage with AI; however, that engagement has be done deliberately and in a way that allows governance frameworks to keep pace without constraining beneficial use.

While it鈥檚 no longer a question that justice systems need to engage with AI, that engagement has be done deliberately and in a way that allows governance frameworks to keep pace without constraining beneficial use.

Automating inefficiency?

Efficiency is often the entry point for AI in justice systems; but efficiency alone is not reform. And misapplied efficiency can often lead to its direct opposite: a scramble to repair broken systems or to plug technology and personnel gaps.

Many current AI initiatives remain isolated pilots 鈥� layered onto existing processes rather than integrated into broader institutional strategy. Without addressing underlying structural constraints like fragmented data, inconsistent procedures, and uneven infrastructure, AI risks automating inefficiency rather than resolving it. And without strong data governance, infrastructure, and institutional alignment, even well-designed AI tools will underperform or produce unreliable outcomes.

That means that efforts to tightly control AI deployment without addressing these foundational issues risk focusing on symptoms rather than the system itself. AI should not function as a parallel modernization effort; rather, it must align with broader justice system reform.

Clearly, the most consequential questions arise when AI tools begin to shape legal reasoning or outcomes. And while there is broad agreement that AI can support judicial work without replacing independent human judgment, in practice, however, the boundary between assistance and influence is not always clear.

Even administrative tools can shape decisions. Summaries may omit nuance, or suggested language can influence framing. Over time, reliance on system outputs can create subtle forms of dependency. In fact, this dynamic is compounded by what has been described as the myth of verification 鈥� the assumption that human oversight alone is sufficient. In reality, time constraints, cognitive bias, and limited technical fluency can make meaningful review difficult. And automation bias affects even experienced decision-makers.

Overall, these boundaries require deliberate definition. Left on their own, AI tools and their outputs will be shaped implicitly through practice rather than through principled governance.

Design determines outcome

Institutional capacity will determine how these dynamics play out because digital maturity varies widely across jurisdictions. Some courts operate advanced platforms, while others remain largely paper based. In lower-resource environments, infrastructure may not support even basic digitization. In more advanced systems, adoption may outpace governance.

Yet, one consistent challenge among all jurisdictions is reliance on external vendors. Without internal expertise, institutions risk adopting tools that meet technical requirements but fall short of rule-of-law standards, particularly in transparency, accountability, and data governance.

Justice systems are not neutral environments for technology adoption 鈥� they are the operational core of the rule of law.

Addressing this gap requires more than a procurement issue. It requires institutional literacy. Judges and administrators need a working understanding of how AI systems function, where risks arise, and how to evaluate them. Training efforts are underway, but scaling this capacity will take time. In the interim, governance gaps will persist and attempts to compensate for these gaps through overly rigid restrictions may limit adoption but do little to build the institutional capability required for effective oversight.

From adoption to accountability

Clearly, AI will not improve justice systems by default; rather its impact will be determined by institutional design, which includes clear boundaries on use, transparency around deployment, safeguards to protect independence, and mechanisms for oversight and accountability. It also requires alignment with broader justice system goals of efficiency, fairness, and accessibility.

Yet, justice systems are not neutral environments for technology adoption. They are the operational core of the rule of law. Their legitimacy depends on trust, which in turn requires accountability.

This makes the path forward not purely a technical one. It requires institutional self-assessment, alignment with human rights frameworks, and collaboration across policymakers, courts, technologists, and the public. The measure of success will not be the sophistication of the tools deployed, but whether they strengthen the system鈥檚 core functions of impartiality, accessibility, and trust.

AI tools can support those goals, of course, but only if they are designed into justice systems from the outset.

You can find other installments of听our Scaling Justice blog series here

Key insights:

• • • AI can improve officer safety听鈥� By helping them prepare for high-risk situations and make better decisions under pressure, advanced technology can enhance officer safety.

    • AI can increase operational efficiency听鈥� AI can reduce administrative burdens and improve efficiency overall, allowing officers to spend more time on police work.

    • Responsible implementation is essential听鈥� To ensure AI strengthens public trust while protecting civil liberties, proper guardrails and oversight need to be enacted.

Each year, during , we pause to honor the brave men and women in law enforcement who have made the ultimate sacrifice in service to their communities. As we pay tribute to those we have lost, we are reminded of the inherent dangers officers face every day.

In recognition of the importance of reflection and advancement, it is imperative that we examine the responsible application of emerging technologies, especially AI, to enhance officer safety, support their objectives, and reinforce overall public safety.

AI is already being integrated into public safety systems in meaningful, measurable ways. When guided by strong ethical principles, transparency, and commitment to community trust, AI can serve as a force multiplier and a protective partner for members of law enforcement. The goal is not to replace officers, of course, but to equip them with better tools, that allow them to reduce risk and return home safely after every shift.

Improving situational awareness and operational readiness

One of the most immediate benefits of AI in law enforcement is its ability to enhance situational awareness. When officers respond to a call, the first minutes on scene are often the most critical 鈥� and the most dangerous. AI can help reduce uncertainty by providing rapid access to relevant information.

For example, AI-powered systems can analyze incident data, criminal records, and community reports to give officers a clearer picture of what to expect when they arrive on scene. This includes identifying patterns of violence, recognizing repeat offenders, or flagging locations that may have a history of high-risk activity. Such insights allow for better preparation, smarter deployment, and more informed decision-making under pressure.

Additionally, AI can assist in public records and open-source searches, pulling critical data from comprehensive databases, the internet, and connected devices in seconds rather than hours. This immediate access to information enables faster, more effective responses. In short, AI can save valuable time when seconds count.

Streamlining administrative work to focus on the mission

Law enforcement officers spend a significant portion of their time on administrative duties, such as writing incident reports and managing court schedules and citations. These tasks, while necessary, take officers away from community engagement and proactive policing.

AI can help reduce this administrative burden by automating routine documentation. Natural language processing tools can draft reports based on officer input, ensuring consistency and freeing up time for frontline duties. Similarly, AI-driven scheduling systems can optimize shift assignments, account for court appearances, and manage on-call rotations. This AI-enabled administrative assistance goes a long way in ensuring that staffing levels are appropriate and that officers are not overburdened.

When guided by strong ethical principles, transparency, and commitment to community trust, AI can serve as a force multiplier and a protective partner for members of law enforcement.

By reducing the administrative load, AI allows officers to focus on what they do best 鈥� serving and protecting their communities. This not only improves job satisfaction among officers themselves but also increases operational efficiency and public safety outcomes.

Building guardrails for responsible AI use

As with any powerful advanced technology, the integration of AI into law enforcement must be guided by clear policies, oversight, and accountability. The goal is not to deploy AI indiscriminately, but rather to ensure its use enhances safety without compromising civil liberties or public trust.

This requires proactive collaboration between technologists, law enforcement agencies, policymakers, and the communities they serve. Standards must be developed for data privacy, algorithmic transparency, and bias mitigation. AI-enabled systems should undergo rigorous testing and independent review before deployment. Further, officers must be trained not only on how to use these tools, but also on the limitations and ethical implications of using these tools as well.

Finally, public trust is essential. Members of the community need to know that AI is being used to protect their safety and that of law enforcement 鈥� it is not a tool to surveil them without cause. Communicating transparently how the AI systems are designed, what data they use, and how decisions are made will be key to maintaining legitimacy and trust with the public.

A future of safer streets and stronger trust

The integration of AI into law enforcement is not about replacing human judgment 鈥� rather, it鈥檚 about augmenting officers鈥� judgment. When used responsibly, AI can reduce risk, improve preparedness, and support officers in carrying out their duties more safely and effectively.

In the years ahead, we can expect to see broader adoption of drone first responders, real-time language translation tools, and predictive systems that further help enhance officer and community safety measures. However, technology alone is not the answer. Success will depend on how thoughtfully these tools are implemented, how well citizens鈥� rights are safeguarded, and how deeply communities are involved in the process.

This week, as we honor those officers who have fallen in the line of duty, let us also commit to doing everything we can to protect those who serve today. AI, when applied with care, can be a powerful ally in their mission, keeping officers safe, allowing them to make better decisions, and together, building stronger, safer communities for all.

The data provided to you may not be used as a factor in establishing a consumer鈥檚 eligibility for credit, insurance, employment, or for any other purpose authorized under the Fair Credit Reporting Act.

You can find more on the challenges facing law enforcement here

Key insights:

• • • Real-time tax compliance has restructured the tax function 鈥� Dozens of nations now require structured invoice data in real time, with the EU mandating cross-border digital reporting by 2030. The traditional file-and-wait audit cycle is gone now, replaced by clearance regimes that can freeze multi-million-dollar invoices for nonconforming data.

    • Regulators have pulled ahead of the businesses they oversee 鈥� Tax authorities in mature CTC jurisdictions now arrive at audits with structured transaction data already processed by their own analytics. Government turnaround times that took months now take weeks, forcing multinational tax leaders to compress multi-year roadmaps into 12- and 18-month cycles to keep up.

    • The lessons travel beyond tax 鈥� There are two ways to lose this race: Outrun your own controls or surrender entirely. Both showed up in Las Vegas, and both will show up in every other regulated profession over the next decade.

LAS VEGAS 鈥� The听 sold out. A guest list that included tax directors from Amazon, Walmart, and Procter & Gamble, OpenAI’s tax department, the Big Four, 成人VR视频 and every other major tax software provider in the market spent three days at the Aria with pool deck, casino floor, and restaurants worth lingering over all a few steps away.

The room had every reason to spend its evenings somewhere else other than a sunless conference room talking about tax. Yet almost no one did. They were too busy grappling with an arms race the corporate audit side had begun to suspect it was losing.

And it鈥檚 one they cannot afford to lose.

End of the traditional model

The arms race is real-time tax compliance, and it has dramatically restructured the ground beneath the tax profession in less than a decade. By April, more than 60 jurisdictions have moved or are moving to continuous transaction controls. Italy and Hungary were early; Poland, France, Belgium, Brazil, Saudi Arabia, India, and Singapore are now operational or imminent, and countries like Spain, Germany, the United Kingdom and the United Arab Emirates are on the way. The European Union has locked onto a 2030 deadline for cross-border real-time digital reporting and a 2035 backstop for harmonizing what’s left.

The traditional model 鈥� issue an invoice, file a return weeks later, audit when the auditor gets around to it 鈥� no longer exists in those jurisdictions. Tax authorities now see the transaction as it happens, validates it in structured form, and pre-fills the return on the taxpayer’s behalf.

What this new process has done to the tax function is fundamentally alter its structure in a way leaves practitioners reeling. The job used to be a craft of Excel, judgment, and institutional memory. Now, at the high end, it has become as much a data science problem as an accounting one.

The arms race is real-time tax compliance, and it has dramatically restructured the ground beneath the tax profession in less than a decade.

Attendees at TEI鈥檚 2026 Tax Technology Seminar polled themselves on tooling, and the answers came back as a list of data pipelines that dozens of attendees seemed to favor: Alteryx, Power Platform, Snowflake, Databricks, Microsoft Fabric, & Palantir Foundry. These platforms are running agentic AI systems against historical filings, deploying validation agents to critique their own outputs, and using AI-driven image-to-text solutions to pull structured data out of state tax notices that never arrive in the same format twice. They are data integration pipelines in 15 minutes that would have sat in an IT queue for two months before being answered.

They have little choice as the stakes are far higher and the challenges far more demanding than they used to be. In a clearance regime, an invoice has no legal force until the tax authority returns its identifier. Did you submit the wrong VAT ID, malformed schema, or mismatched master data? Congratulations! Your invoice is rejected. That means the truck doesn’t move, the buyer doesn’t pay an invoice that may be in the millions of dollars and then the penalties stack on top. Italy, for instance, charges a fee of 70% of the disputed VAT.

And then there are the audits.

Outgunned

The audit isn’t an occasional event anymore. In government jurisdictions with mature continuous-transaction-control tax regimes, it is a conversation that started weeks before the auditor walked in, on data their analytics had already processed.

A speaker on a seminar panel led by Deloitte and 成人VR视频 described the dynamic plainly: Tax authorities in those jurisdictions have arrived at audits already knowing more about the transactions than the companies and their in-house audit teams sitting across the table. Not because anyone is hiding anything, but because the data arrived at the tax authority in structured form, in real time, and the authority had run its analytics on it before the meeting was even on the calendar. One panelist said this represents “a shift from us preparing returns to us answering notices on the data that’s been shared.”

What the room kept circling around, however, was that regulators have not just kept pace with their counterparties, they鈥檝e now pulled ahead. Singapore, one panelist noted, is doing more with AI than even major companies. Indeed, government turnaround times that used to take months are now closing in weeks, which is forcing multinational tax leaders to compress their multi-year roadmaps into 12- and 18-month cycles 鈥� not because they want to but because their counterparties already had.

The lesson that corporate tax functions have been forced to absorb is that there are two ways to lose this race, and both were on display at TEI鈥檚 2026 Tax Technology Seminar as cautionary tales.

This asymmetry is structural, and that is what makes it an arms race rather than a transition. There is no version of this dynamic in which the company being audited wins by being more careful, more thorough, or more well-prepared at the end of the quarter. The advantage now accrues to the side with the fastest and cleanest pipelines, that runs the smartest AI, and that understands the way these increasingly complex systems interact. Increasingly, that winning side is the government. And, more alarming, this isn鈥檛 just a problem for this particular industry 鈥� tax just happened to get here first. However, it鈥檚 coming for everyone.

Two ways to lose

The lesson that corporate tax functions have been forced to absorb is that there are two ways to lose this race, and both were on display at TEI鈥檚 2026 Tax Technology Seminar as cautionary tales. The first is to outrun your own controls. AI coding tools that let a tax analyst build a working data integration pipeline in 15 minutes are genuinely valuable; they also let that same analyst deploy something nobody else has reviewed, documented, or knows how to maintain. An OpenAI panelist conceded the point when an audience member asked about the security implications of vibe coding 鈥� clearly, a new capability is also a new problem.

The second way to lose is harder to talk about. One panelist described, to attendees鈥� general dismay, hearing of companies that have given up on compliance entirely 鈥� instead, they pad their numbers with a safety margin and treat the eventual audit as the cheaper of the two costs. The panel recoiled 鈥� one member responded with a flat “Do not do this.” However, the anecdote landed because it isn’t theoretical. When the gap between what regulators can see and what your team can produce becomes wide enough, surrender starts to look rational.

Playing to win

Of course, the attendees at TEI鈥檚 2026 Tax Technology Seminar were not surrendering. If they were, they’d have been at the pool deep into their third cocktail. Or they’d have been on the casino floor or were about to catch an afternoon show. Instead, day after day, the tables filled, the exhibit hall ran hot, and the room was buying, listening, and building.

The game has changed and the stakes have risen 鈥� and the room is dead set on playing to win.

You can find more of听our coverage of Tax Executives Institute events here

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

Key insights:

• • • AI as a design decision, not just a tech add-on 鈥� AI gives us a chance to rethink the 鈥渕achinery of justice鈥� and redesign it for today鈥檚 needs rather than simply automating existing systems and processes.

    • AI to expand access and usability, without replacing judgment 鈥� The most promising value is in reducing friction for litigants and helping people navigate the process.

    • Progress requires disciplined, court-by-court experimentation 鈥� We can start small, build AI literacy, set leadership tone, invite diverse perspectives, and address legal and ethical issues as design constraints, not deal-breakers.

Today, interest in AI across the judiciary is clearly growing, but most discussions are still constrained by certain fears:

• • • Fear that AI will replace human judgment 鈥� This concern is legitimate, but it focuses almost entirely on endpoints. Judging (and the systems around it) involve far more than final decisions. Focusing only on high-stakes endpoints misses much of what judges and courts do day-to-day.
    • Fear of hallucinations, errors, and bias 鈥� These are also legitimate fears, but there are ways to mitigate these risks, which are not new. The source may be different, but we have long needed to protect against errors, bias, and misstated law.
    • Fear of change 鈥� This is a difficult one to overcome, but a desire to protect the status quo sometimes presupposes that the system as it exists today is working exactly as it should. It isn鈥檛. At least not for everyone.

I鈥檇 like to see the narrative shift from fear of AI in courts, to the possibilities of AI in courts. AI presents a rare opportunity to upgrade the machinery of justice.

Justice as machinery

Most of us were taught to think about justice as an outcome, something the system delivers. However, justice is also the machinery we use to deliver it, and that machinery is a set of design choices. Rules, procedures, forms, hearings, briefs 鈥� we crafted these frameworks to manage conflict and produce decisions that feel fair and legitimate. Like most frameworks, they reflect the era in which they were built.

Once we start thinking about justice as something to be designed rather than simply delivered, the access-to-justice problem looks different. The question is no longer how to get more of the current system to more people; rather, it鈥檚 whether the machinery itself is still fit for its purpose.

Reimagining the machinery

The machinery has been redesigned before. Justice was once deeply human because it had to be: Law lived in minds, judges traveled from town to town, decisions were announced aloud. That system was more human and personal, but it was limited, exclusionary, and fickle. It was dependent on local norms and personal relationships. It yielded uneven outcomes.

The first great upgrade was writing, and more importantly, the printing press. It brought stability and protected litigants from arbitrary local power. But it also entrenched a new kind of authority. Yet, understanding it required literacy, training, and expertise. A professional bar emerged and ordinary people were pushed further from the center of their own disputes. Then came the digital age. It optimized the process and made more information available. But many people feel overwhelmed by the deluge of information and experience modern justice as a series of obstacles.

Does AI present a different kind of opportunity? Could it deliver an upgrade that finally closes the gap rather than widens it? I鈥檓 optimistic that the answer is yes, but our design choices matter and we have to be willing to reimagine justice from the ground up.

What if every litigant had access to an AI agent that could help them navigate forms, understand the process, and translate legalese? What if AI could take messy human stories and translate them into structured information for the court? What if courts offered AI-assisted dispute resolution in the early stages of litigation or at key milestones during the litigation? Can AI make navigating the legal system feel less like data entry and more like a conversation?

We鈥檙e not ready for giant leaps, and we can鈥檛 ignore the open questions: Unauthorized practice of law issues, privilege and work product implications, the reliability of AI-assisted work product, and more 鈥� but these are not dead ends. They鈥檙e current design constraints to account for, and they shouldn鈥檛 keep us from reimagining what鈥檚 possible.

Where do we start?

The institution of justice will not be redesigned overnight, and there is no central authority to drive change. Rather, it will be redesigned court by court. The principles below apply broadly and reflect a starting point for thinking about AI as a design decision, not just a technology decision.

Set the tone from the top听

Fear can be paralyzing, and in courts it often is. If judges and court staff are afraid to experiment, nothing moves. We need environments in which thoughtful, controlled experimentation is encouraged and supported. When more people are engaged in testing ideas and thinking about how to improve their processes, the likelihood of meaningful innovation and redesign increases.

Court leadership can create that space by setting a vision, encouraging responsible experimentation, and supporting innovative mindsets.

Build AI literacy

Encouraging experimentation is an important first step, but it can create risk if not paired with the right training and education. AI requires new competencies in prompting, guardrail development, output verification, bias awareness, iteration, context framing, documentation for audibility, fit-for-purpose judgment, and more. As tools evolve, education should evolve, too. Agentic AI, for example, will require a different set of skills and a different type of supervision than we鈥檙e accustomed to now.

For more information about toolkits and resources around AI in courts, visit

Judges and court staff do not need to become technologists, but they need enough training and education to ask the right questions, spot the right issues, and use the tools responsibly.

Rethink the systems, not just the tools

This one is critical. Currently, most conversations about AI focus on use cases, such as whether AI can assist with research or automate certain workflows. These are good questions, but the tougher questions will lead to bigger rewards. Where are our pain points? What can we do better? Which policies and processes are essential, and which have never been re-examined? Which parts of the machinery were built for a different era and have outlived their usefulness? And perhaps most importantly, who is the system failing?

We shouldn鈥檛 start with the technology and look for places to apply it. We should start with the people we serve and ask how the technology can help us serve them better.

Invite diverse perspectives

The strongest ideas emerge from the push and pull of different viewpoints. Court leadership can form committees that bring together innovators and skeptics, technologists and traditionalists, those who are excited and those who are concerned. We also need perspectives across different court functions. AI is not something to hand off to IT departments. They are essential partners, but the questions AI raises go far beyond any one department.

Outside perspectives are helpful, too. Many people across the country are already approaching this work with a multidisciplinary lens, and courts can draw on that experience.

Finally, remember to start small

It鈥檚 easy to create so much process and deliberation that progress slows. We need concrete steps that move us forward, however incrementally. Start with policies and data governance, then move to small, targeted pilots that can address low-hanging fruit. Small adjustments can help teams become comfortable with change; and early wins build confidence and create momentum.

Closing thoughts

Justice has been redesigned before, and it is on the brink of being redesigned again. AI will reshape courts whether or not we participate. However, as the people who know the system from the inside and want it to work for everyone, we may be in the best position to guide the next upgrade. The chance to build something more equitable, more accessible, and better designed for today鈥檚 world does not come around often, let鈥檚 not miss it.

You can find more insights from Judge Braswell here

Key insights:

• • • AI augments judicial judgment without replacing it 鈥� Used thoughtfully it clarifies reasoning and improves access.

    • Strict guardrails are needed 鈥� These can include structured prompts, anonymized data, and rule-based outputs helps interrupt bias and maintain integrity.

    • Judges should lead 鈥� They can do this through peer learning and education, which fosters responsible use while preserving public trust.

The integration of AI in the judiciary is gaining momentum, offering a promising solution to the growing caseloads, access-to-justice gaps, and public trust challenges faced by courts across the United States. And as the judiciary explores the potential of AI, a crucial conversation is emerging 鈥� one that highlights the importance of responsible and thoughtful adoption.

A recent webinar, , presented by the听 鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI) 鈥� shed light on the experiences of early adopters of generative AI (GenAI) in the judiciary. In the webinar, Prof. Amy Cyphert of West Virginia University and U.S. Magistrate Judge Maritza Dominguez Braswell of the District of Colorado shared their insights from their own use of AI, emphasizing the need for a deliberate and informed approach.

The role of AI in judicial decision-making

A common fear is that AI will somehow take over the position of final arbiter in court proceedings. However, judges are not interested in having AI displace their judgment; rather, they see AI as a tool that augments and helps advance justice, not a tool that replaces decision-making or human judgment.

Judges also are not rushing into AI use. Instead, they are approaching it with a deep commitment to responsible use and a desire to increase, not decrease, public trust. “Everybody on that spectrum 鈥� from ‘I’m just learning’ to ‘I want to be a power user’ 鈥� says, ‘But I want to do it right,鈥�” says Judge Braswell.

AI can also help judges close communication gaps. By taking decisions that judges have already reasoned through and converting them into accessible explanations, AI can help all litigants clearly understand the relevant legal framework, rule, or process behind the decision. This is even more impactful in cases involving self-represented litigants.

Leveraging AI to enhance judicial communication

Judge Braswell understands this well. In every case with at least one self-represented litigant, she offers a plain language summary of her written decisions. Although she does not use AI to draft those, she does use AI to translate complex legal reasoning when delivering information from the bench.

鈥淚f I have 15 minutes for a hearing and want to explain to a self-represented litigant something complex, I use AI to help me translate legal jargon into plain and simple language,鈥� she explains. 鈥淚 want the self-represented litigant to understand what I鈥檓 doing and why I鈥檓 doing it 鈥� and AI helps me translate lawyer-speak into plain-speak, quickly.鈥�

You can explore the white paper听 here

This capability is particularly valuable for judges who often struggle to find the time to connect with litigants. By leveraging AI, they can provide more personalized and informative interactions, ultimately enhancing litigants鈥� judicial experiences. In addition, some judges are using AI to create engaging content, such as avatars and videos on YouTube, to make themselves more relatable and accessible to the public; while others are using AI to help litigants navigate court processes, helping to demystify the system and reduce anxiety.

Guardrails for responsible AI use

Of course, Judge Braswell doesn’t use AI casually. She has strict policies and protocols in place, including segregation of work and personal accounts, prompt anonymization, and prohibiting her clerks from uploading sensitive information or delegating core functions and judgment to any AI tool. She also trains her chambers on high-risk and low-risk cases and emphasizes the importance of proper AI use through structured prompts, appropriate settings, standing instructions, and deliberate guardrails.

For example, Judge Braswell describes a dedicated project in which she uploaded her district’s local rules, the Federal Rules of Civil Procedure, and standing orders. She queries that project any time she needs to refresh on an applicable rule or procedure. She gave the AI tool clear instructions, such as: Don’t answer unless grounded in a rule. Cite the rule with every response. If you don’t know, say so.

While these types of practices do not make the tools risk-free, Judge Braswell notes, they do offer guardrails to help support, rather than undermine, judicial integrity.

Addressing risks and challenges

While , the deeper risks in AI use in the courts are bias, cognitive deskilling, and erosion of public trust. Judge Braswell warns that bias is harder to detect than any made-up case citation. “If you ask for a legal framework in an employment discrimination case, the system may pull more from defense-side articles because larger firms publish more content,鈥� she explains. 鈥淭he result is a subtle tilt in perspective.”

To counter this, she prompts her AI tools deliberately asking for diverse perspectives, asking the tool to gather contrary views, or telling the tool to answer only after asking follow-up questions that could identify user bias. Without this intentionality, bias can go undetected.

For judges ready to engage, visit听听to join the conversation

On the webinar, Prof. Cyphert echoed concerns about the next generation. “I worry that younger lawyers may skip critical learning processes if they rely too heavily on AI for drafting or research,” Prof. Cyphert says. “Is there a cognitive benefit to writing that we’re losing?”

The path forward through education, experimentation & transparency

During the webinar, both speakers rejected mandatory disclosure rules as counterproductive.

“It creates a chilling effect,” Judge Braswell says. 鈥淎nd we need people to engage for learning purposes.鈥� Instead, she notes that she advocates for voluntary transparency 鈥� judges explaining their use of AI in ways that build public understanding and confidence.

Prof. Cyphert agrees. 鈥淵ou can’t assess risks and benefits if you don’t understand the technology,鈥� she says, adding that she encourages judges to attend webinars, read research, and talk to peers. Similarly, Judge Braswell co-founded the , a judge-only, peer-led forum for candid discussion that exists as a safe space to share challenges, test ideas, and learn together.

As the webinar notes, the future of justice isn’t just about whether courts and judges are using advanced AI technology, it’s about how that technology should be used 鈥� with care, purpose, and always with people at the center.

For more on the impact of AI in courts, visit the听

Key insights:

• • • Protecting SNAP requires modernization and accountability 鈥� This includes providing chip-enabled cards, stronger monitoring, recipient education, retailer oversight, cross-agency coordination, and fair reimbursement for victims.

    • Skimming is a growing problem 鈥� In the context of financial fraud, skimming refers to the illegal capture of personal data, typically through concealed electronic devices placed over legitimate card readers.

    • The harm can be immediate and severe 鈥� If their food benefits are stolen through skimming, vulnerable households can lose essential food funds, deepening food insecurity in their community.

Electronic Benefit Transfer (EBT) cards serve as a critical resource for the millions of Americans who depend on the nation鈥檚 Supplemental Nutrition Assistance Program (SNAP) to keep food on the table. The typical SNAP household is low-income and often includes children, seniors, or individuals with disabilities, who have earnings that fall at or below the federal poverty level. Based on household size, income, and other qualifying factors, these families receive monthly monetary assistance to help cover basic nutritional needs at authorized retailers.

Think of an EBT card as a debit card specifically designed for food benefits. Recipients use it to access their monthly balance at approved stores, making the process straightforward and dignified. However, like any electronic payment system, EBT is not immune to exploitation. One of the most pressing threats is a type of fraud known as skimming, which puts vulnerable households at serious financial risk.

What is EBT skimming?

Skimming, in the context of financial fraud, refers to the illegal capture of personal data, typically through concealed electronic devices that are placed over legitimate card readers. In the case of EBT fraud, criminals generally install tampered card terminals to steal EBT card information, including account numbers and PINs.

Unlike most modern credit and debit cards, EBT cards still rely on magnetic stripe technology, not more secure embedded chips. This outdated system makes them especially vulnerable to cloning, or the creation of counterfeit cards that contain the victim鈥檚 account number and PIN. Once a thief captures the data, they can create these counterfeit cards and drain benefits almost immediately, often within minutes of the monthly benefit deposit.

The result is that much needed food benefits, meant to last an entire month, are stolen without warning or recourse.

Why is EBT skimming so devastating

The consequences of EBT skimming go far beyond financial loss. For recipients, the theft of SNAP benefits can have immediate and severe impacts on their household food security and well-being. Other reasons why this form of fraud is particularly harmful include:

• • • Irreplaceable funds 鈥� For low-income households, SNAP benefits represent a critical portion of their monthly food budget. Once stolen, these funds are often impossible to replace. Families may be forced to skip meals, rely on emergency food pantries, or divert money from other essential needs like rent or medicine.
    • Outdated security technology 鈥� Despite advances in payment security, most EBT cards still use magnetic stripes, which can be easily copied with inexpensive skimming devices. By contrast, EMV chip technology, standard on most consumer credit and debit cards, makes cloning significantly more difficult.
    • Speed and precision of theft 鈥� Thieves often time their attacks to coincide with the monthly benefit deposit cycle. Once benefits are loaded, stolen card data is used rapidly, sometimes within minutes, making recovery nearly impossible.
    • Targeting vulnerable populations 鈥� EBT skimming preys on some of the most vulnerable members of society, including seniors, disabled individuals, and families living paycheck to paycheck. Many recipients may not have the resources or knowledge to monitor account activity regularly or to lock their cards after use, leaving them at greater risk.

Beyond skimming: A broader challenge of fraud, waste & abuse

While skimming is a serious and visible form of EBT fraud, it is only one symptom of a larger systemic challenge that fraud, waste & abuse cause in federal benefit programs.

Other forms of fraud include: retailers trafficking in EBT benefits for cash, which is a violation of SNAP rules; misrepresentation of income or household size during application; duplicate or ineligible benefit issuance; and administrative errors that lead to overpayments.

Each instance, whether intentional or not, erodes public trust in the entire benefit system, strains limited program budgets, and diverts resources from those individuals who need them most.

With federal funding for social programs under constant scrutiny and subject to periodic budget constraints, it is imperative that every dollar is protected and used appropriately. Preventing fraud is not just about saving money 鈥� it鈥檚 about ensuring that limited public resources serve their intended purposes of reducing hunger and supporting economic stability.

How to prevent fraud, waste & abuse in SNAP

Addressing EBT skimming and broader program vulnerabilities requires a well-rounded strategy that features technology, policy, education, and oversight working together.

On the technology side, one of the most impactful steps forward would be transitioning EBT cards from outdated magnetic stripes to EMV chip technology. This upgrade alone would significantly reduce skimming risks, and federal investment in that infrastructure is a necessary part of making it happen. Alongside that, state and federal agencies should be leveraging data analytics and real-time transaction monitoring to flag suspicious activity, like multiple withdrawals across different locations within a short window of time.

Education also plays a bigger role than many people realize. A large portion of EBT users simply do not know how to protect themselves. Basic habits like covering the keypad when entering a PIN, routinely checking account balances, and reporting lost or stolen cards right away can go a long way in reducing exposure.

One of the most pressing threats is a type of fraud known as skimming, which puts vulnerable households at serious financial risk.

From an oversight perspective, the U.S. Department of Agriculture 鈥� the government agency that oversees SNAP 鈥� and state agencies need to conduct regular audits of authorized retailers and hold them accountable. Any retailer found engaging in trafficking or enabling skimming should face deauthorization and legal consequences as well. Equally important is making sure that victims of confirmed fraud are not left without recourse. Clear and consistent policies for replacing stolen benefits can help restore trust in the program and prevent the food insecurity that this type of fraud directly causes.

Finally, none of this works in isolation. Effective fraud prevention depends on strong coordination between state human services departments, law enforcement, financial institutions, and technology providers. Information sharing and joint task forces strengthen the ability to detect threats early and respond quickly when issues arise.

Protecting the safety net

SNAP is one of the nation’s most effective tools in the fight against hunger. However, its success depends on both integrity and accessibility. Skimming and other forms of fraud not only steal from individuals, but they also undermine confidence in the entire system.

Policymakers, administrators, and citizens must prioritize modernization, accountability, and victim protection. By addressing vulnerabilities like EBT skimming and reinforcing safeguards against waste and abuse, we can ensure that SNAP remains a reliable and secure resource for the millions of individuals who rely on it.

You can find out more about how public agencies are working to fight fraud in government benefit programs here