AI Agents

Context

Built for insurance within the Fraud detection & alerting stack, this agent is used during assessment and pre-settlement to uncover risky connections among claimants, providers, repairers, adjusters, addresses, devices, and payment endpoints. Typical scenarios include repeated involvement of the same clinic or shop across disproportionate claims, contact details shared by unrelated parties, and referral loops that suggest organized activity.

What it does

The agent assembles a graph from recent and historical claim data—linking entities such as people, companies, phone numbers, emails, addresses, bank accounts, vehicles, devices, and IPs—and evaluates whether the pattern of links matches known risk motifs. It flags many-to-one hubs (e.g., a single provider touching outsized claims), tight clusters repeatedly appearing together, circular referral paths, and rapid re-use of contact or payment details across “unrelated” claims. Findings are returned as a structured pass/flag with a concise rationale (what pattern, which nodes/edges, over what timeframe) and click-through evidence so reviewers can see exactly why the network is suspicious.

Core AI functions.

Entity resolution to unify duplicates and variants; graph construction from claim, policy, provider, and payment data; community detection and centrality scoring to surface hubs and clusters; temporal analysis for bursty re-use of entities; path queries for referral and collision motifs; and reason-code generation that explains each alert (e.g., “shared bank account across 4 claimants in 30 days,” “clinic at address X linked to 7 staged-collision patterns”). Confidence scoring prioritizes material structures and routes edge cases to human review.

Problem solved

Traditional rules focus on single claims and miss cross-claim coordination. Manual link checks are slow and inconsistent, and weak entity resolution hides reused details behind minor variations.

Business impact

SIU referrals become more targeted and earlier in the lifecycle. Organized patterns are disrupted sooner, leakage drops, investigator productivity improves with evidence-linked visuals, and dispute outcomes are more defensible.

Integration and adjacent use cases

Integration is moderate: read claim, policy, provider, payment, and device/contact data from your core or warehouse; optionally ingest historical archives; write risk scores, reason codes, and node/edge evidence into the claims workflow or SIU case management—no core replacement required.

Common combinations in this stack:

• Behavioral pattern analyzer (claims SIU) (to correlate network flags with timeline anomalies),

• Image & document authenticity detector (to verify evidence from implicated nodes),

• Claim vs policy consistency checker (to reconcile coverage posture on flagged files), and

• Suspicious claim escalation agent (to route high-risk clusters with a complete evidence bundle).