In early 2026, President Donald J. Trump formed the Task Force to Eliminate Fraud, charged with finding ways to combat fraud, waste, and abuse in America’s social-welfare and entitlement programs. In doing so, he evoked a massive Minnesota scandal: Federal prosecutors charged — and later convicted — dozens of crime ring members[1] for their involvement in a $250 million scheme[2] to defraud a federally funded, state-administered child-nutrition program. In the executive order empaneling the task force, Trump also specifically cited California, Illinois, New York, Maine, and Colorado as states with “insufficient” fraud oversight.[3]
Which agencies will fall under the task force’s purview? Not every benefit organization is directly named in Trump’s executive order. Still, it’s a good bet that task force recommendations will directly affect organizations such as the:
- United States Department of the Treasury
- Department of Agriculture (responsible for SNAP benefits)
- Social Security Administration
- Department of Health and Human Services (which oversees Medicare & Medicaid Services)
- Department of Housing and Urban Development (public and subsidized housing)
- Department of Veterans Affairs
- Department of Labor (unemployment insurance)
- State and local agencies charged with program administration
Penalties for non-compliance have yet to be announced.
New recommendations demand deployment of new technology
The Task Force to Eliminate Fraud will develop strategies to prevent the disbursement of funds to bogus applicants, payees, and vendors. Social-welfare and entitlement agencies will be required to deploy fraud prevention measures that evaluate fraud indicators, dismantle fraud networks, and ensure compliance.
This will be a significant challenge. Task force recommendations will almost certainly require that affected agencies pivot from an existing fraud-mitigation model — one that has them paying benefits only to later chase fraudsters — to a model that strives to detect potential fraud before benefits are paid. This pivot will require real-time identity risk intelligence delivered through low-friction, high-certainty processes that balance the need for security with fast service delivery.
The legacy systems and verification processes used by many government organizations cannot provide this type of intelligence. But, if task force timelines are any indication, agencies must quickly find a way to overcome legacy limitations. Trump’s executive order gives affected organizations only 30 days to submit descriptions of transactions and processes most susceptible to fraud, along with suggested improvement measures. The task force itself has 90 days to identify vulnerabilities, develop recommendations, and submit proposed plans.
Understanding the fraud landscape
With or without an executive order, financial prudence dictates that government agencies do a better job of detecting fraud than they have in the past. The U.S. Government Accountability Office reports that in FY 2024, the federal government spent $162 billion in improper benefits payments.[4] Since FY 2003, it has spent more than $2.8 trillion on these payments.[5] The vast majority of these were overpayments – including payments to the deceased and to those who no longer qualify for certain benefits.[6]
It is important to note that while all these payments have been labeled “improper,” they do not each represent a case of fraud. Administrative errors, eligibility errors, administrative lag time and more all contribute to improper payments. Still, recent technological developments present new vulnerabilities to social welfare and entitlement programs.
Sophisticated fraud rings, transnational criminal organizations, and synthetic identity networks use artificial intelligence (AI) to better exploit vulnerabilities in enrollment, eligibility verification, and payment-disbursement systems. For example, AI powers synthetic identity networks — giving them the ability to blend factual personally identifiable information (PII) with fake data. These synthetic identities far too often trick traditional Do Not Pay lists.
Different criminals and crime rings use AI to defraud different social welfare and entitlement programs.
- In the Affordable Care Act marketplace, some dishonest brokers exploit $0 premium plans — enrolling individuals without their consent, and pocketing commissions in the processes.[7]
- Medicare and Medicaid programs suffer from the emergence of durable medical equipment billing schemes, paying for equipment billed by phantom providers.[8]
- Lack of state-level auditing enables coordinated networks to divert funding from child welfare programs.[9]
- In many benefits programs, self-certification allows individuals to claim eligibility without proving their identities.[10]
How Babel Street can help
Meeting likely task force recommendations at a massive, federal scale requires automated, AI-powered identity risk intelligence for the entire fraud-prevention lifecycle: screening, verification, eligibility, enrollment, payment integrity, vendor vetting, and enhanced investigations.
The mission-grade Babel Street Risk Intelligence Platform provides this intelligence. Babel Street supplies complete, reliable views of individuals or entities applying for or receiving entitlements or social welfare benefits, along with companies and providers invoicing associated agencies. In doing so, we empower agencies to spot existing fraudsters and keep new ones from entering federal systems — often before the first dollar is moved.
Examining static government lists, traditional PII checks only tell you if a person exists. Babel Street tells you who he really is. We accomplish this by analyzing digital footprints, associations, and risk signals appearing in more than 200 languages across the global digital landscape. We pierce thinly-constructed synthetic entities that would otherwise hide safely in in the noise of large data volumes.
Fraudsters move fast. So does Babel Street. We integrate with internal systems, third-party platforms, and proprietary workflows to deliver risk signals in seconds — automating previously manual vetting and investigative processes while bypassing the costly, technologically challenging, and time-consuming process of ripping and replacing legacy systems. Empowering cross-agency sharing of risk insights and patterns, Babel Street could help the federal government spot those fraudsters who scammed one agency yesterday and are trying to hit another one today. We do this without compromising sensitive internal data. Continuous monitoring appends new information to existing records regardless of whether anyone is actively searching.
Our agentic risk intelligence workflows are uniform and transparent, ensuring fast, reliable, and secure results for both large-scale screenings and targeted investigations. We enable easy pivoting between screening and investigative processes. All workflows are rooted in federal and organizational policy, with real-life analysts in control.
These benefits are achieved through the following capabilities:
- Multi-fieldmatching . The Babel Street Risk Intelligence Platform automatically analyzes and matches names, addresses, dates, and other personal and organizational identifiers across more than 24 languages and scripts — including Arabic, Chinese, Japanese, Korean, Russian, Spanish, and Urdu. It detects and connects transliterations, spelling variations, and culturally nuanced use of language. Within these workflows, matches above and below an agency-determined threshold score are automatically accepted or rejected — leaving only a few borderline cases requiring human review.
- Real-time identity intelligence. Babel Streets scours massive amounts of multilingual, publicly available information. This information originates from social media platforms, professional certification sites, watchlists, white- pages databases, residential-ownership databases, public digital presences, dark web forums and marketplaces, and more. AI-powered analyses of this data provide agencies with the entity resolution capabilities needed to verify applicants, payees, and vendors.
- Explainable AI: This capability delineates how matches, results and insights are derived, enabling auditability and defensibility of match decisions.
- Relationship mapping. Starting from only a few keywords or known influencer accounts, Babel Street can map and evaluate social media networks to identify those who wield the most influence, and to chart their followers. This capability helps pierce fraud rings.
- Visualization tools. Babel Street offers multiple visualization tools for analyzing intelligence and detecting trends. These tools include relationship graphs, profile cards, data aggregations, heat maps, and word clouds.
Many social welfare agencies will find it all-but-impossible to meet the Task Force to Eliminate Fraud’s expected recommendations using existing technologies. Replacing those technologies is expensive and time consuming. Consider working with Babel Street instead. Our solutions for identity risk intelligence work with existing systems to provide the insight needed to detect fraud and to prevent fraudsters from accessing social welfare and entitlement systems.
Endnotes
1. U.S. Department of Justice, “Five More Plead Guilty in Minnesota Feeding Our Future Fraud Scheme,” March 2026, https://www.justice.gov/opa/pr/five-more-plead-guilty-minnesota-feeding-our-future-fraud-scheme
2. Federal Bureau of Investigation, “Dozens Charged in $250 Million COVID Fraud Scheme,” September 2022, https://www.fbi.gov/news/stories/dozens-charged-in-250-million-covid-fraud-scheme-092122
3. Executive Orders, “Establishing the Task Force to Eliminate Fraud,” March 2026, https://www.whitehouse.gov/presidential-actions/2026/03/establishing-the-task-force-to-eliminate-fraud/
4. U.S. Government Accountability Office, “Federal Government Made an Estimated $162 billion in Improper Payments Last Fiscal Year,” March 2025, https://www.gao.gov/blog/federal-government-made-estimated-162-billion-improper-payments-last-fiscal-year
5. Ibid
6. Ibid
7. Centers for Medicare & Medicaid Services, “CMS Actions to Protect Consumers and Strengthen Exchange Program Integrity,” January 2026, https://www.cms.gov/newsroom/fact-sheets/cms-actions-protect-consumers-strengthen-exchange-program-integrity
8. Healtmarkets, “Senior Healthcare Reform News Updates,” March 2026, https://www.healthmarkets.com/resources/healthcare-reform-updates-065/
9. Executive Orders, “Establishing the Task Force to Eliminate Fraud,” March 2026, https://www.whitehouse.gov/presidential-actions/2026/03/establishing-the-task-force-to-eliminate-fraud/
10. Ibid
Disclaimer:
All names, companies, and incidents portrayed in this document are fictitious. No identification with actual persons (living or deceased), places, companies, and products are intended or should be inferred.