Infrastructure for Claims Fraud Detection & Investigation

Fraud detection requires explicit, current documentation of fraud typologies—staged accident ring patterns, medical provider upcoding signatures, claimant red flags—so the AI applies consistent scoring logic. These patterns must be findable and up-to-date; when SIU expertise lives only in senior investigators' heads, the model can't encode evolving schemes like phantom passengers or attorney-driven soft-tissue mills.

Network analysis for detecting staged accident rings requires automated capture of entity relationships—shared phone numbers, addresses, bank accounts, attorneys, body shops—from every claim as it enters the system. Manual or periodic capture (L3) misses connection data that only becomes significant in aggregate. The fraud detection model needs event-driven ingestion of party data, social media signals, and NICB/ISO database hits at claim intake to build the network graph in real time.

Network analysis requires a formal ontology mapping entities (Claimant, Provider, Attorney, Body Shop) and relationships (shared address, co-claimant, represented-by) with typed edges and traversal rules. Without this, the AI cannot identify that three claimants share a phone number across four claims involving the same attorney and two body shops. Fraud risk scoring models need structured feature vectors derived from these entity-relationship definitions.

Fraud detection must query the claims system (party data), NICB/ISO fraud databases, social media signals, DMV records, and historical SIU case outcomes via API at the time of claim intake. Legacy claims platforms restrict real-time programmatic access, but API connections to the core fraud data sources—industry databases and internal claims history—are necessary for the model to generate actionable fraud risk scores before payment authorisation.

Fraud schemes evolve continuously—when a new staged accident corridor emerges or a provider begins upcoding, the fraud detection model must update its patterns within days, not quarters. Industry database feeds (NICB, ISO) update frequently and must propagate to the model automatically. Stale fraud indicators generate false negatives, paying fraudulent claims, and false positives, blocking legitimate claims and creating regulatory exposure.

Fraud detection requires API-based connections between the claims system, SIU case management, NICB/ISO industry databases, social media monitoring, and DMV/public records. These connections must enable the AI to assemble a complete party network per claim. Point-to-point integrations are sufficient—unified iPaaS is not required—but all key data sources must be queryable programmatically rather than through batch export.