Fictitious Employer Fraud Prevention
The article discusses the growing threat of fictitious employer fraud in unemployment insurance programs, emphasizing the need for State Workforce Agencies to shift from reactive post-payment recovery to a proactive "Pre-Payment First" approach that integrates employer and claimant data with predictive analytics—such as Catalis UI Solutions' Fictitious Employer module—to detect and prevent fraudulent ghost employer schemes before payments are made, thereby protecting trust fund balances and reducing improper payments.
Moving Beyond Reactive Defense
This is post one of a three-part series on how to combat UI fictitious employer fraud.
In 2026, the mandate for State Workforce Agencies (SWAs) has shifted from simple “claims processing” to complex “fund stewardship.” As trust fund balances face pressure from both economic fluctuations and sophisticated criminal enterprises, the traditional reactive approach to fraud is no longer sustainable. Agencies are finding that for every dollar recovered through post-payment collections, several more are lost to organized schemes that exploit the system before a single check is even cut.
The Rising Threat of “Ghost Employers”
The most insidious of these threats is the Fictitious Employer (FE) scheme. Unlike individual claimant fraud, FE fraud involves the creation of entire “ghost” companies that exist only on paper to fraudulently receive unemployment insurance funds. These coordinated attacks often utilize synthetic identities and automated systems to overwhelm agency defenses, placing an unfair tax burden on legitimate businesses whose experience ratings are impacted by fraudulent charges.
Adopting a “Pre-Payment First” Philosophy
To mitigate this, agencies must adopt a “Pre-Payment First” philosophy. This requires breaking down the historical data silos between employer tax records and claimant systems—areas that have traditionally operated in isolation. By analyzing the behavioral patterns of how an employer account is established and how its “employees” subsequently file for benefits, agencies can identify red flags long before the first payment is issued.
How can state agencies reduce UI improper payment rates?
State Workforce Agencies can lower rates of improper payments by deploying predictive analytics to score employer accounts before funds are disbursed. The Fictitious Employer module from Catalis UI Solutions identifies 100% of top-scored fictitious employers, preventing millions in trust fund losses by cross-matching claimant and tax data in real-time.
Closing the Gap: From Detection to Prevention
The Fictitious Employer module from Catalis UI Solutions is specifically designed for this high-stakes challenge. By using evolving multivariate analysis and decades of subject matter expertise, it identifies fraudulent employer accounts early. In recent deployments, this approach accurately identified 46 fraudulent employer accounts and over 400 associated claimants, allowing agencies to stop fraud at the source and ensure that benefits reach those who truly deserve them.
Explore how Catalis can help your agency implement secure, AI-driven fraud detection to future-proof your unemployment insurance platform. Schedule a demo today.
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Wyoming Partners with Catalis to Combat Unemployment Insurance Fraud
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