AI Benefits Claims Fraud Detection Agent

How Cassidy automates this using AI

Step 1: Trigger on new claim or eligibility event

The Workflow activates when a benefits claim is submitted or an eligibility change is detected pulling application data, identity verification results, and cross-program signals into a unified case record.

Step 2: Run entity resolution and identity checks

Cassidy links applicant data across systems to resolve aliases, addresses, devices, and household relationships, then validates identity against wage records, SSA data, and program-specific databases.

Step 3: Score risk with hybrid detection

The Agent applies business rules for statutory disqualifiers, runs ML models trained on confirmed fraud outcomes, and performs graph analysis to detect collusive networks or cross-program patterns generating a composite risk score with confidence bands.

Step 4: Route to prioritized triage queue

High-risk cases queue for Human-in-the-Loop review with explainability artifacts attached. Medium-risk cases trigger step-up verification requests. Low-risk cases auto-pass with sampling for quality control.

Step 5: Generate case summary and recommended action

Cassidy compiles evidence, reason codes, and network diagrams into a structured case packet, then proposes treatment actions hold payment, request additional documentation, or escalate to Special Investigations for analyst approval.

Step 6: Capture disposition and close the loop

Investigator decisions feed back into the system to retrain models, refine rules, and adjust thresholds continuously improving detection accuracy while maintaining audit-ready documentation.