AI Claims Processing
Cuts Manual Work by 70%
A Nordic insurer had outgrown its manual claims workflow. We designed and deployed an agentic AI pipeline that now handles over 10,000 routine claims per month — autonomously, accurately, and at scale.
A Claims Department Drowning in Manual Work
The client processed upwards of 15,000 insurance claims per month across home, motor, and travel lines. Over 80% were routine — standard damage reports, straightforward liability cases, low-complexity travel incidents — yet every one required a human handler to open, read, classify, verify documentation, cross-reference policy terms, and approve or decline.
The team had grown year on year just to keep pace. Average handling time sat at 22 minutes per claim. Error rates on data entry and policy lookup ran above 4%, triggering costly corrections and customer complaints. During seasonal spikes — Q1 travel claims and Q3 weather events — backlogs stretched to eleven days.
The business had explored RPA for two years with limited results. Point automations existed but broke with every policy update or document format change. What they needed was something that could read, reason, and adapt — not just follow rules.
Four Phases, Sixteen Weeks
We rejected a big-bang deployment in favour of a staged build where each phase validated the next. The insurer's claims team was involved throughout — not just as requirements-providers, but as active co-designers of the system that would replace their daily workflow.
Mapped the full claims workflow through process mining and 40+ handler interviews. Identified 12 distinct claim types by complexity and automation potential.
Weeks 1–3Designed a four-agent pipeline with clear handoff logic, confidence thresholds, and human escalation paths. Defined the criteria for autonomous straight-through approval.
Weeks 4–6Built the agentic system on Azure, integrating with the client's claims platform and document store. Ran parallel processing on 2,000 historical claims to validate accuracy before go-live.
Weeks 7–13Went live with travel claims first (lowest risk), then motor, then home. Full autonomous volume reached in week 16 with a 94% straight-through rate on all eligible claim types.
Weeks 14–16A Four-Agent Pipeline That Reads, Reasons, and Decides
At the core of the solution is an agentic AI pipeline where each agent has a specific role and hands off to the next with a structured payload. The system processes PDFs, emails, and web-form submissions and makes autonomous approval decisions on standard claims within seconds of receipt.
web form
& complexity
& policy match
or escalate
high-value claims
The system doesn't just extract data from documents — it understands context. When a claim references a prior incident, the validation agent finds and cross-references it automatically. That kind of reasoning is what separates this from the RPA we tried before.
From Backlog to Same-Day Processing
Six months after go-live, results exceeded the original business case targets. The client has since expanded the system to three additional claim lines not in scope for the original engagement.
Built on Proven Enterprise Platforms
The stack was chosen for enterprise reliability, security, and compatibility with the client's existing infrastructure. No new vendors required additional procurement cycles or security review.
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