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InsuranceAI implementation case study
Insurance – Regional P&C Carrier: FNOL & Triage Automation AI
AI for omnichannel FNOL, fraud scoring, adjuster assignment, and vendor dispatch for mitigation.
This case study documents a real client implementation. Identifying details may be withheld where confidentiality applies.
Key takeaway
PrimeAxiom automated first notice of loss through triage and routing with AI agents—not IVR trees.
The engagement
From operational friction to a connected AI system
01 · Context
Business overview
Industry: P&C insurance. Complexity: multi-peril policies.
02 · Challenge
Core problem
Manual FNOL, slow triage, vendor latency.
03 · Approach
Solution
PrimeAxiom deployed claims AI: conversational FNOL for voice/text, structured loss objects, fraud triage agents, adjuster routing, and vendor dispatch with SLA.
04 · Impact
Results
FNOL-to-adjuster assignment time −61%; straight-through processing +19%; fraud referral precision +33%; customer NPS +17.
System design
The full automation system
The implementation connects triggers, AI decisions, business rules, human approvals, and downstream actions in one controlled workflow.
FNOL Agent, Fraud Agent, Routing Agent, Vendor Agent.
Workflow
How the automation runs
1. Loss reported.
2. AI structures claim.
3. Fraud score.
4. Route to adjuster.
5. Mitigation dispatch.
6. Customer updates.
7. Continuous scoring.
Intelligence layer
AI agents and decision logic
Intake Agent, Score Agent, Dispatch Agent.
Under the hood
Technical architecture
Core system adapters, policy rules, LLM for narratives.
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