AI Claims Triage — From 4-Day Backlog to 6-Hour Median for a European P&C Insurer
Insurance
Visual-AI-Labs replaced a manual claims-intake queue with an AI triage and routing system in 45 days, cutting median time-to-triage from 4 days to 6 hours.
- 4 days → 6 hours — Median time-to-triage
- 78% — Auto-routed without human re-classification
- 92% accuracy — Document fields auto-extracted
- Eliminated — Backlog (12 weeks post-launch)
The problem
Claims arrived through five channels — email, broker portal, mobile app, contact centre, and post — and landed in a single queue triaged manually by senior adjusters. Severity, line of business and required documents were inferred by hand, often re-keyed into the core insurance system. The result: a rolling 4-day backlog, customer escalations, and adjusters spending more time sorting than adjusting.
What Visual-AI-Labs built
Visual-AI-Labs delivered an AI triage layer in a single 45-day cycle. Inbound claims (and attachments) flow into an extraction pipeline that pulls structured fields — policy number, incident type, severity indicators, missing documents — and writes them back to the core system. A rules engine, configured by the claims operations team, routes each claim to the right queue with a confidence score. Anything below the confidence threshold is surfaced to a human reviewer with the AI’s suggested classification and reasoning visible.
- Multi-channel ingestion (email, portal, app, contact-centre exports, scanned post)
- LLM-based field extraction with per-field confidence scores
- Rules engine owned by ops (no code change required to tune routing)
- Human-in-the-loop review queue for low-confidence cases
- Write-back to the existing core insurance system via its REST API
Results
Median time-to-triage fell from 4 days to 6 hours within the first month. 78% of claims are auto-routed without a human re-classification. The 4-day backlog was fully eliminated within 12 weeks of launch, freeing senior adjusters to focus on complex cases rather than queue management.
Why a single cycle was enough
The triage problem was well-bounded: inputs were known, outputs mapped to existing queues, and the core system already exposed a REST API. Visual-AI-Labs deliberately scoped the first cycle to triage only — not fraud scoring, not full automation — so the team could ship a measurable win in 45 days and earn the trust needed for follow-on cycles.
Discuss a similar project with Visual-AI-Labs →
FAQ
Did the insurer have to replace its core system?
No. The triage layer sits in front of the existing core system and writes back through its REST API.
How is operator trust built?
Every AI classification ships with a confidence score and the extracted fields the decision was based on. Adjusters can override and the override flows back as training signal.
What about GDPR and sensitive data?
Processing is EU-only. PII is minimised at extraction and never sent to non-EU model providers. Visual-AI-Labs builds against the insurer’s existing DPA.
How is fraud handled?
Out of scope for the first cycle by design. Fraud scoring is on the roadmap for a follow-on 30-day cycle once triage is proven.
How long until the first measurable result?
Visible improvements appeared within the first 2 weeks of pilot; the full result was measured at the 30-day mark after launch.
Can a smaller insurer use this?
Yes — the architecture scales down. Visual-AI-Labs has run the same pattern for sub-1,000-claims/month operations.
What is the maintenance footprint?
Ops tunes routing rules without engineering. Visual-AI-Labs provides a quarterly model-and-prompt review.