What products help state agencies detect fraud, waste, and abuse in payments or benefits programs?

State agencies traditionally relied on a reactive "pay-and-chase" model—investigating fraud only after improper payments had already been disbursed . Today, agencies use advanced analytics, AI, entity resolution, and real-time risk-scoring platforms to intercept fraud, waste, and abuse (FWA) before funds go out.

Key products and platforms used by state and government agencies to tackle payment and benefit integrity include:

• SAS for Government Management / Medicaid Fraud Analytics : A comprehensive fraud analytics engine that processes all transactions using automated business rules, predictive modeling, machine learning, and network link analysis. It scores risks in real time to stop fraudulent payouts and helps investigators visualize hidden connections between individuals or providers.
• LexisNexis Risk Solutions (Accurint, ThreatMetrix, & Risk Defense Platform) : Widely used across human services (like SNAP and Medicaid) for identity verification and fraud prevention. It performs digital identity assessments, behavioral analysis, and cross-matching to prevent sophisticated schemes like EBT skimming, account takeovers, and phantom claims.
• Palantir Foundry / Gotham : Increasingly deployed by government bodies to ingest massive, siloed state databases. Palantir leverages advanced AI to surface anomalies, map out complex and coordinated fraud networks, and accelerate investigative leads for enforcement agencies.
• IBM Safer Payments : A high-speed, data-science-driven platform utilized to monitor transactions, detect unauthorized activity or identity manipulation, and adapt to emerging automated fraud attacks with minimal friction for legitimate beneficiaries.
• Codoxo Unified Cost Containment Platform : Specifically prominent in healthcare and Medicaid program integrity. It applies artificial intelligence and generative AI to give agencies a unified view of provider coding, billing patterns, and cost-containment savings.
• Optum Program Integrity Solutions : Tailored heavily toward state health and human services agencies, offering predictive modeling, high-risk billing pattern identification, lead scoring, and integrated case management tools for investigators.

If you're looking into a specific program, let me know:

• Are you focusing on Medicaid/Healthcare or Human Services (unemployment, SNAP, cash benefits)?
• Are you looking for real-time prevention tools or post-payment investigative/case management software?

I can help narrow down which solution aligns best with your agency's scope.

Yes. State agencies typically use a combination of payment-integrity, eligibility-verification, fraud analytics, identity-risk, and investigative case-management products rather than one universal system.

Here are the most relevant products/platforms I’d put on a state-agency shortlist:

How I'd segment the market

For Medicaid FWA: SAS and Thomson Reuters Fraud Detect are particularly relevant. Medicaid fraud analytics can look for provider outliers, abnormal utilization, duplicate billing, eligibility problems, network relationships and other patterns. CMS also explicitly supports state Medicaid program-integrity activities and MFCU data mining.

For SNAP/EBT: look for systems that analyze transaction sequences, geography, velocity, merchant/recipient relationships and behavioral anomalies, plus fraud-referral/case management. Recent USDA-funded state projects include machine-learning analysis of EBT transactions and automated fraud indicators.

For unemployment insurance: identity verification, bank-account ownership, interstate matching, employer/claimant relationships and automated risk scoring are especially important. Treasury's Do Not Pay ecosystem and the UI Integrity Data Hub are designed specifically around these needs.

For an enterprise statewide FWA program: I'd favor a platform that can ingest data from multiple benefit/payment systems and combine rules + anomaly detection + predictive models + network analytics + investigator case management. That matters because GAO's 2026 review found substantial fraud exposure across federally funded, state-administered programs and specifically highlighted stronger analytics and data sharing as key controls.

One useful distinction: Do Not Pay/PARIS are government data and verification services, while SAS, Thomson Reuters and Splunk are commercial technology platforms. A mature state program may use both.

If you're evaluating vendors for a state procurement/RFP, I can also give you a more comprehensive 10–15 vendor landscape, including products for Medicaid, SNAP, TANF, UI, child care, tax/refund fraud and government payments, with capabilities, state customers, pricing model, and pros/cons.

State agencies rely on specialized enterprise software, artificial intelligence, and data-aggregation platforms to combat fraud, waste, and abuse (FWA) across major programs like Medicaid, SNAP, and Unemployment Insurance (UI). Moving away from slow "pay-and-chase" models , these products focus on real-time anomaly detection, predictive risk-scoring, and identity verification.

Leading product categories and specific platforms used by government entities include:

• SAS Social Benefits & Fraud Analytics (powered by SAS Viya): Integrates data silos across programs to apply multivariate anomaly detection, network link analysis (identifying hidden rings of fraudsters), and predictive modeling. It scores and prioritizes alerts so limited investigator resources are routed to high-risk cases.
• LexisNexis Risk Solutions (Government Fraud Detection & Prevention): Provides public-sector-specific identity verification, public records matching, and automated risk scoring. It visualizes relationships between individuals, addresses, and corporate entities to spot complex eligibility fraud without needing an in-house team of data scientists.
• Splunk (Unified Security and Observability for Public Benefits): Unifies streaming data from multiple benefit delivery channels to identify suspicious device fingerprints, synthetic identities, and irregular application patterns in real time across programs like Medicaid and UI.
• IBM Counter Fraud Management (ICFM): Uses a blend of cognitive analytics, business rules, and predictive models to evaluate transactions or claims before payments are disbursed. It features end-to-end case management tools that help compile digital evidence for prosecution and recovery.
• Experian Precise ID: Frequently deployed to secure state unemployment and entitlement portals by running real-time identity proofing, behavioral biometrics, and multi-factor identity authentication during the initial application phase.
• Feedzai: Employs advanced machine learning to analyze transactional data streams and behavior, flagging scam rings and improper state payouts before funds leave government accounts.

Would you like to explore how these platforms handle a specific program (such as Medicaid vs. Unemployment Insurance) or look into the implementation challenges state agencies face when adopting them?

State agencies typically use a combination of payment integrity platforms, identity/eligibility verification tools, fraud analytics, case management systems, and investigative platforms to detect fraud, waste, and abuse (FWA) in programs such as Medicaid, SNAP, unemployment insurance, TANF, housing assistance, grants, and other benefit programs. Examples include:

Government payment integrity and eligibility verification products

• U.S. Department of the Treasury Bureau of the Fiscal Service —

• Helps agencies verify recipient identity, eligibility, and payment information before issuing funds.

• Supports pre-payment screening, post-payment reviews, continuous monitoring, and data matching. States administering federally funded programs can use Do Not Pay services.

• Administration for Children and Families — Public Assistance Reporting Information System (PARIS)

• Provides interstate and federal data matching for programs such as Medicaid, SNAP, TANF, and other assistance programs to identify duplicate participation or eligibility issues.

Fraud analytics and AI-driven detection platforms

• Advanced analytics, machine learning, anomaly detection, and investigation workflows.

• Common use cases include suspicious payments, provider fraud, identity fraud, and improper claims.

• Used by organizations to aggregate large volumes of operational data, identify unusual activity, and support investigations. Public-sector use cases include monitoring benefits-program data and detecting suspicious transactions.

• Data integration and analytics platform used for complex investigations, entity resolution, network analysis, and program integrity workflows.

• Machine-learning tools that agencies can use to build predictive models for risk scoring and anomaly detection.

Medicaid and healthcare program integrity products

• Analytics capabilities that can support healthcare fraud detection, claims analysis, and operational monitoring.

• Healthcare payment-integrity solutions for identifying improper claims, overpayments, and provider billing anomalies.

• Healthcare claims analytics and recovery services used by payers and government health programs.

• Provides Medicaid systems, analytics, and program-integrity solutions for state Medicaid agencies.

Identity, credential, and eligibility verification products

• Identity verification, fraud scoring, and risk analytics.

• Identity verification, public assistance fraud detection, address validation, and investigative analytics.

• Identity and fraud-risk analytics.

Case management and investigations

• Case management, workflow automation, constituent records, and investigation tracking.

• Workflow and case-management capabilities that can support investigations and compliance operations.

Common capabilities states look for

A strong FWA detection environment usually includes:

• Identity verification

• Detect stolen identities, synthetic identities, deceased-person applications, and duplicate recipients.

• Eligibility verification

• Match income, employment, residency, household, incarceration, death records, and other eligibility factors.

• Payment anomaly detection

• Flag unusual payment amounts, timing, locations, vendors, devices, or account patterns.

• Provider fraud analytics

• Detect abnormal billing, unnecessary services, suspicious provider networks, and improper claims.

• Entity resolution

• Link people, businesses, addresses, bank accounts, providers, and devices across datasets.

• Investigation workflow

• Provide investigators with alerts, evidence, audit trails, and case management.

• Cross-program data matching

• Identify fraud patterns spanning Medicaid, SNAP, unemployment insurance, grants, and other programs.

For state agencies specifically, many programs combine federal verification resources such as Do Not Pay with commercial analytics platforms and internal program-integrity systems. For example, Pennsylvania’s Department of Human Services describes using eligibility database checks and AI-enabled fraud-capture technology to identify potential Medicaid program outliers for review.

Yes. I found a fairly well-defined market of products aimed at state program-integrity, payment-integrity, and benefits-fraud teams. The strongest options fall into several categories.

Leading products worth evaluating

The interesting distinction

I would divide the market into three layers:

1. Prevent the bad payment before it happens
Identity verification, eligibility checks, bank-account validation, deceased-person checks, incarceration checks, duplicate identities, etc. Do Not Pay, Equifax and Experian are particularly relevant here. Treasury says Do Not Pay can perform real-time or batch screening and provide risk/confidence information to state agencies.

2. Detect sophisticated fraud patterns
This is where SAS and Thomson Reuters Fraud Detect stand out. Rather than simply asking "is this person eligible?", they look for behavioral anomalies, networks, unusual billing/claims patterns and relationships among people, providers and transactions. CMS itself describes behavioral, network, predictive and machine-learning analytics as tools for detecting Medicaid FWA.

3. Investigate and recover money
The more complete platforms connect alerts to investigators, cases and recovery. Thomson Reuters, for example, combines Fraud Detect with Case Tracking and specialized recovery services.

Medicaid is its own major market

For Medicaid, I'd pay particular attention to products that can analyze claims + provider + beneficiary + enrollment + ownership + utilization data rather than merely doing identity verification.

CMS specifically recommends that state Medicaid agencies develop predictive-modeling and data-analytics capabilities for program integrity, and its program-integrity organization operates systems for provider enrollment, claims/billing analytics and fraud detection.

There is also a federal/state ecosystem around Medicaid Fraud Control Units and approved data-mining programs; HHS OIG maintains a list of states with approved Medicaid data-mining applications, including Florida.

Don't overlook the government-provided tools

For a state agency, I wouldn't start by buying commercial software. I'd first map what can be obtained through Treasury Do Not Pay, CMS, PARIS, the UI Integrity Center, state data, and federal/state matching programs, then identify the gaps that commercial products need to fill.

For example, Do Not Pay's PARIS capability allows state public-assistance agencies to match information across states and programs such as Medicaid, TANF, SNAP and workers' compensation. Its UI Integrity Center integration supports interstate unemployment-fraud detection.

That's important because the commercial product may be most valuable on top of those government data sources rather than as a replacement for them.

If I were building a shortlist

For a state-wide procurement/RFI, I'd probably put these into the first competitive set:

1. SAS Payment Integrity for Social Benefits — strongest broad benefits/payment-integrity analytics candidate.
2. Thomson Reuters Fraud Detect + ID Risk Analytics/CLEAR — particularly strong for fraud analytics plus investigations.
3. Experian — strong identity/eligibility decisioning candidate.
4. Equifax — particularly compelling for unemployment insurance and identity/payment verification.
5. Treasury Do Not Pay — foundational government service to integrate regardless of which commercial platform is selected.

The broader need is significant: GAO's July/August 2026 review says roughly $1.2 trillion went to federally funded, state-administered programs in FY2025 and identifies fraud vulnerabilities across programs including Medicaid and SNAP.

If you're looking at this from a state-government procurement or market-mapping perspective, I can also build a more detailed vendor landscape of ~15–20 companies, comparing capabilities across SNAP, Medicaid, unemployment, TANF, child care, housing, payment fraud, identity fraud, AI/network analytics, case management, pricing model, and known state deployments.

Here are top web results for exploring this topic:

Splunk·https://www.splunk.com Protecting Public Benefits with Splunk: A Smarter Way to Detect ...State agencies can use Splunk to unify benefits data and detect fraud, waste, and abuse across programs like SNAP, Medicaid, and unemployment insurance. Real-time analytics help agencies identify susp Centers for Medicare & Medicaid Services | CMS (.gov)·https://www.cms.gov**Fraud** Prevention Toolkit - CMS CMS Fraud Prevention InitiativeCMS Fraud Prevention InitiativeThe federal government has made important strides in reducing fraud, waste and improper payments across the government. The Affordable Car Databricks·https://www.databricks.com Bringing real-time fraud prevention to government benefits These programs are designed to deliver aid quickly and at enormous scale. At that volume, deeply vetting every transaction before the money goes out is nearly impossible without delaying aid to the pe Thomson Reuters Legal Solutions·https://legal.thomsonreuters.com Is your government agency using these fraud -prevention tactics?There's plenty to investigate. Synthetic identity fraud has become more and more prevalent. So have bot attacks, which require agencies to ascertain whether an applicant is in fact a real person. This Cato Institute·https://www.cato.org Curbing Waste, Fraud , and Abuse in Federal Welfare Programs Waste, fraud, and abuse in federal welfare programs cost taxpayers billions of dollars each year. Medicaid, the Supplemental Nutrition Assistance Program (SNAP), child nutrition programs, Temporary As U.S. Department of Labor (.gov)·https://www.dol.gov Preventing fraud - U.S. Department of Labor As a condition of receiving these grants, states agreed to make data readily available to the DOL-OIG for the purposes of both fraud-prevention activity and audits. $600 million to modernize vulnerabl GDIT·https://www.gdit.com AI for Fraud, Waste and Abuse (AI FWA) - GDIT AI for Fraud, Waste and Abuse (AI FWA). Built to detect and stop fraud, waste, and abuse, this AI solution provides federal agencies the power to pinpoint high-risk activity, accelerate investigations EisnerAmper·https://www.eisneramper.com How to Prevent and Detect Fraud, Waste , and Abuse in Government ...Strategies to Prevent and Detect Fraud, Waste, and Abuse in Government Programs. Published: Nov 18, 2024. By: Louise Gannuch: Dana Daigle. Topics. Fraud Awareness · In this video, Dana Daigle and Loui Clearspeed·https://www.clearspeed.com**Detect Fraud**, Waste & Abuse in Government | Clearspeed Reduce fraud, waste, and abuse with real-time risk assessment. Help investigators focus resources on cases that warrant closer review. National Association of Medicaid Directors·https://medicaiddirectors.org What CMS Can Do to be of Additional Help with Fraud, Waste and ...CMS has helpfully signaled an agency-wide commitment to addressing Fraud, Waste and Abuse (FWA). Over and above the auditing processes that are currently being prioritized, CMS has additional opportun

State agencies rely on specialized software platforms, data analytics, and artificial intelligence to prevent, detect, and recover improper payments tied to fraud, waste, and abuse (FWA). These solutions generally span healthcare programs (like Medicaid), social welfare/benefits (like SNAP and unemployment), and public procurement.

Major product categories and prominent vendor solutions used by government and state agencies include:

1. Enterprise Analytics & AI Platforms

These platforms process massive datasets, run predictive models, flag anomalous billing patterns, and map complex relationships or shell companies.

• SAS Fraud Decisioning / Payment Integrity: Offers high-performance analytics on the SAS Viya platform, applying hybrid detection (business rules, anomaly detection, predictive modeling, and network link analysis) across healthcare, procurement, and tax administration.
• Deloitte Pallium: A SaaS-based program integrity solution featuring a curated library of FWA models, multi-dimensional risk scoring engines, and investigative case management designed for government agencies.
• Codoxo Fraud Scope: An AI-driven platform used by state Medicaid and healthcare agencies to surface outlier provider behavior, automate claim audits, and leverage generative AI for natural language queries.

2. Identity Verification & Eligibility Screening

Stopping fraud before benefits or payments go out the door relies heavily on robust digital identity and cross-agency data checking.

• LexisNexis Risk Solutions for Government: Uses proprietary entity-resolution technology (LexID) to aggregate vast public and private records, helping state agencies score identity risk, detect synthetic identities, and cross-check benefit eligibility.
• DataWalk Anti-Fraud Software: Connects disparate state databases (employment history, income, tax records) to perform link analysis and flag fraudulent benefit claims or shell companies.
• Healthy Together + CLEAR: Integrates secure biometric and identity verification platforms at the point of enrollment to stop ineligible or fraudulent applications before they reach downstream payment processing.

3. Healthcare & Social Services Program Integrity Tools

Designed specifically to meet federal compliance (like CMS regulations for Medicaid) and audit requirements for social safety net programs.

• Gainwell Technologies (FraudCapture & Payment Analytics): Cloud-hosted platforms tailored for Medicaid and health programs that perform post-pay data mining, clinical claim reviews, and eligibility verifications.
• Thomson Reuters Fraud Detect: Scans for fictitious businesses and improper payments in unemployment insurance and identifies complex trafficking or misuse patterns in SNAP (food assistance) benefits.
• Alivia Analytics: Employs pre-pay preventive analytics and machine learning (Alivia 360) to target the "gray zone" of subtle billing errors, waste, and policy non-compliance.

If you are looking at a specific program—such as Medicaid, Unemployment Insurance , or SNAP/TANF —let me know and I can detail which specific tools or federal data-matching frameworks are most commonly deployed for that sector.

Yes. State agencies have a fairly mature market of products for detecting fraud, waste, abuse (FWA), improper payments, identity fraud, and benefit trafficking. The strongest options tend to combine identity/data matching, rules, anomaly detection, machine learning, network analytics, and investigator case management.

Notable products

How these products map to state use cases

1. SNAP / EBT

• Detect unusual transaction patterns and possible trafficking
• Identify compromised cards/accounts
• Detect out-of-state or geographically implausible activity
• Find duplicate or cross-state participation
• Score cases for investigators

This is an especially active area: USDA's current SNAP Fraud Framework explicitly recommends analytics and technology spanning fraud detection, investigations, data management, and case prioritization.

2. Medicaid / healthcare

• Provider outliers and suspicious billing
• Impossible utilization patterns
• Provider/member relationships
• Duplicate or inappropriate claims
• Identity and eligibility anomalies

CMS itself uses behavioral, network, predictive and machine-learning analytics for Medicare/Medicaid program integrity, so these are established approaches rather than purely experimental AI applications.

3. Unemployment insurance

• Identity theft and synthetic identities
• Duplicate claims
• Suspicious addresses/bank accounts
• Fictitious employers
• Claims inconsistent with employment records

GAO found that states used controls including identity cross-checks and duplicate banking/address detection in their pandemic unemployment programs.

4. General benefits / human services The interesting category is cross-program analytics: instead of asking whether one SNAP or Medicaid transaction looks suspicious, the system looks across programs and datasets for relationships that individually look normal but collectively are unusual. LexisNexis, for example, specifically markets cross-program participation analysis and identity-risk analytics for social services.

If I were evaluating the market

I'd divide the products into four categories:

1. Payment/transaction analytics — SAS, IBM
2. Identity + external-data verification — LexisNexis
3. Investigator intelligence/case prioritization — Thomson Reuters, LexisNexis
4. Custom analytics platforms — SAS/other data-science platforms built around the state's own eligibility, claims and payment data

The important distinction is that "fraud detection" isn't one product capability. A state looking for SNAP fraud, Medicaid provider fraud, unemployment identity fraud, and improper eligibility payments may need several layers.

There is also a substantial build-vs-buy opportunity: USDA reports that states are already receiving grants to implement things such as EBT transaction analytics, predictive risk scoring, fraud-referral case management, automated fraud indicators and IP-address analysis.

If you're evaluating this as a vendor landscape, I can also give you a more comprehensive list of ~15–20 vendors, including price/contract signals, state customers, specific products, target programs (SNAP/Medicaid/UI/TANF), AI capabilities, and likely competitors.

Yes. For state agencies, the market breaks into a few product categories rather than one universal “FWA” system. The strongest options combine identity/eligibility verification, cross-program data matching, anomaly detection, network analytics, and investigator case management.

1. Treasury Do Not Pay

This is the first one I'd evaluate because it is a government-provided resource rather than a commercial product. Treasury says states administering federally funded programs can use it to verify recipient identity, eligibility and bank-account information before payment, at no cost.

It is particularly useful as a pre-payment control, while commercial fraud platforms tend to add deeper analytics and investigation capabilities.

2. Thomson Reuters Fraud Detect

This is one of the more directly comparable commercial products for state benefits programs. It provides machine-learning-based risk scoring, prioritization of potentially fraudulent participants/providers, pattern and cluster analysis, and investigation leads. Thomson Reuters specifically positions it for unemployment, SNAP/social services and healthcare/Medicaid.

There's also a particularly relevant state example: Nevada's welfare agency used Fraud Detect to improve detection of waste and fraud in SNAP.

3. LexisNexis Risk Solutions

LexisNexis has a substantial government/social-services fraud offering. Its Program Participation Analyzer focuses on visibility into benefit issuance and participation, while its broader identity/risk data can expose relationships and risk patterns across programs. LexisNexis reports that its technology helped Florida achieve $1.1 billion in SNAP cost avoidance over six years—a vendor-reported result, so I'd validate the methodology during procurement.

4. CLEAR / ID Risk Analytics

These are more useful as the investigator's intelligence layer than as a standalone payment-integrity engine. They can connect people, businesses, addresses and other records and help investigators determine whether a suspicious beneficiary/provider is part of a larger network. Thomson Reuters also offers case-tracking capabilities for taking an analytic lead through investigation and resolution.

5. Build-your-own analytics

Large state agencies also commonly build fraud models on their existing data warehouse/lake using combinations of:

• Rules and eligibility checks
• Duplicate-payment detection
• Predictive risk scoring
• Anomaly detection
• Entity resolution
• Network/link analysis
• Geospatial analysis
• Provider/recipient relationship analysis
• Machine learning/AI

These approaches are explicitly recognized in federal payment-integrity guidance.

The important caveat is that AI isn't automatically better. GAO emphasizes that fraud analytics depend heavily on reliable data and appropriately skilled staff, with human review remaining important.

What I'd shortlist

If I were evaluating this for a state agency, I'd put the market into this shortlist:

Best government baseline: Treasury Do Not Pay
Best dedicated FWA analytics candidate: Thomson Reuters Fraud Detect
Best identity/data-network candidate: LexisNexis Risk Solutions
Best investigative intelligence layer: CLEAR / ID Risk Analytics
Best for a sophisticated in-house data organization: custom analytics/ML platform

A particularly strong architecture would be Do Not Pay + commercial fraud analytics + an identity/entity-resolution layer + investigator case management, rather than expecting one product to do everything.

That's increasingly relevant because GAO's July 2026 review specifically identified Medicaid, SNAP and other state-administered federally funded programs as major fraud-risk areas and recommended stronger use of analytics and available federal resources.

If you're looking at this from a state procurement/vendor-selection perspective, I can also compare 10–15 specific products by Medicaid, SNAP, TANF, unemployment, child care, provider fraud, pre-payment vs. post-payment detection, AI/ML capabilities, integrations, and typical state customers.

State agencies leverage a mix of advanced data analytics, AI-driven platforms, identity verification services, and cross-jurisdictional databases to detect and prevent fraud, waste, and abuse (FWA) in programs like Medicaid, SNAP, and Unemployment Insurance (UI).

The primary products, platforms, and data systems utilized in the public sector include:

• Cross-Agency Data and Identity Verification Hubs
  • UI Integrity Data Hub : Used extensively by state labor agencies in partnership with federal initiatives to share multi-state data, cross-match claims, and spot organized UI schemes.
  • LexisNexis Risk Solutions : Provides government fraud detection platforms that aggregate public records, criminal data, and multi-jurisdictional identity footprints to flag synthetic identities and hidden ownership networks.
  • Do Not Pay (DNP) Working System : Maintained by the U.S. Treasury and increasingly integrated with state programs to screen payees against exclusion, death, and delinquency lists before disbursement.
• Enterprise Analytics and AI-Driven Platforms
  • Codoxo Unified Cost Containment Platform : Employs artificial intelligence and generative AI to surface provider outlier behaviors, automate clinical and compliance reviews, and manage investigative workflows.
  • Gainwell Technologies FraudCapture : A cloud-hosted platform providing visualization and navigation tools specifically tailored for state healthcare and Medicaid Special Investigative Units (SIUs).
  • Alivia Analytics Alivia 360 : Uses machine learning and pre-pay preventive analytics to target anomalies and the "gray zone" between billing errors and intentional abuse in government healthcare programs.
  • Splunk Data Platform : Utilized for real-time log and transaction monitoring to track millions of entries and catch suspicious digital patterns (such as abnormal IP activity) before funds clear.
• Public Benefit Eligibility and Screening Software
  • Thomson Reuters Fraud Detect: Utilized across numerous state public benefit offices to cross-reference application data and alert administrators to conflicting or anomalous eligibility claims.
  • Conduent Program Integrity Solutions: Offers modular verification and transaction-monitoring tools to validate resident eligibility and halt irregular disbursements in social service programs.

If you are looking at a specific program—such as Medicaid, Unemployment Insurance, or SNAP —let me know, and I can detail which specific software integrations or federal grants apply to that program.