Fraud detection software

Improve fraud detection and prevention with Fraud Detect 

A powerful tool that utilizes expert machine-learning technology

Providing you with actionable, trusted analytics and results

Detect fraudulent behavior with confidence

Get detailed analyses to predict and detect complex schemes. You'll receive risk-scoring, stack-ranked lists of all program providers and participants based on their risk for fraud.

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Advanced prediction and machine-learning technology

Discover potential fraud through trends, patterns, clusters, and individual leads. This information is delivered through easy-to-interpret graphs, link charts, tables, and other methods to clearly display the cause for suspicion.

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Enhanced lead prioritization

With the addition of CLEAR public records, your analysts and investigators are provided with enhanced program data containing live data updates that can highlight which leads should be prioritized.

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Fraud Detect can meet your industry-specific needs


Find fictitious businesses — and improper or overpayments — while keeping unemployment benefits in the right hands. Fraud Detect is critical to maintaining program integrity, verifying information, and ensuring risks are called to attention for action.

Social Services

Detect complex SNAP fraud schemes, get a comprehensive review of fraudulent patterns in SNAP programs, and reduce the amount of improper payments by detecting bad actors misusing or trafficking benefits.


Fraud Detect can help agencies comply with CMS regulations regarding fraud, waste, and abuse oversight and reporting by revealing unlicensed providers, outlier billing, utilization spikes, and much more.

The value of Fraud Detect

$18.7 million recovered 

Fraud Detect identified $50 million in overpayments, which led to $18.7 million in recoveries by Medicaid Inspector General.

40% of top Medicaid states 

Four of the top 10 Medicaid states are utilizing Fraud Detect to prevent fraud in their programs.

$130 million identified 

The duplicate payment model identified $130 million in duplicate payments.