A mid-size regional insurer was drowning in volume. Every claim — from a straightforward windshield replacement to a complex liability dispute — landed in the same queue, waited for the same adjusters, and moved at the same pace. We changed that.
Same headcount. Nobody got laid off. The team handles more — and harder — claims than before.
A mid-size regional property and casualty insurer operating across six US states. The company processes more than 400 claims per day across auto, home, and commercial lines. Like most regional carriers, they'd grown their claims operation organically — adding adjusters as volume grew, with no fundamental change to how claims were triaged, routed, or resolved.
The claims team was experienced and capable. The problem wasn't the people — it was the system that treated a $200 windshield claim and a $200,000 liability dispute with exactly the same queue logic.
When we sat with the claims team for a week, the pattern was obvious. Roughly 70% of daily claim volume was what the team called "checklist claims" — events with clear coverage, documented damage, established repair networks, and no meaningful dispute. A broken windshield with photos, a policy in force, and a preferred shop on record. An appliance claim with a receipt and an appraiser quote already attached.
These claims weren't difficult. They were time-consuming. An adjuster had to open each one, verify coverage, check the documentation, confirm the repair estimate fell within guidelines, and issue payment. Every step of that process was manual, repetitive, and identical across hundreds of claims every day.
Meanwhile, the complex claims — the ones that actually needed an adjuster's judgment — waited in the same queue. There was no prioritization. A multi-vehicle liability claim sat behind seventeen windshield replacements. Experienced adjusters were spending most of their day on work that didn't need their expertise.
The business result was predictable: cycle times were long, customer satisfaction scores were mediocre, and the company kept hiring to keep up with volume growth. Each new hire added capacity but didn't fix the underlying inefficiency.
We designed the solution around a simple principle: don't try to automate everything. Automate the claims that should be automated, and make the complex claims easier for the humans who handle them.
When a claim arrives — via phone, portal, or email — the system reads the claim details, loss description, policy terms, documentation attached, and damage estimates. It classifies the claim into one of three tiers: routine (can be auto-resolved), standard (needs review, but documentation is complete), or complex (requires adjuster judgment, policy interpretation, or negotiation). This classification happens in seconds, before the claim enters any queue.
Tier-one claims move through an automated resolution workflow. The system verifies active coverage, validates the claimed amount against policy limits and preferred-provider rate schedules, checks for open litigation flags and fraud indicators, and — if all gates pass — issues payment authorization and sends the claimant a resolution notice. A supervisor dashboard shows all auto-resolved claims with the reasoning for each decision. Any supervisor can halt or override a resolution in progress.
When a claim routes to an adjuster, it arrives with a pre-prepared brief: relevant policy clauses pulled and highlighted, a summary of the claimant's prior claim history, comparable settled claims from their own portfolio, reserve recommendations based on similar losses, and a suggested next action. The adjuster still makes every decision — they just start from a position of complete information instead of an empty file.
The system integrates with their existing claims management platform via API — no replacement of core systems, no data migration. The triage classifier was trained on three years of their resolved claims, labeled by outcome and complexity tier. The auto-resolution engine operates as a rules-validated AI layer: machine learning flags candidates, a deterministic rules engine validates each resolution step against documented underwriting guidelines, and every decision generates an auditable event log.
Key design decisions that made the project work:
Up from 0% before the system. The 71% figure held steady through months 2 and 3 as claim mix remained consistent.
Across all claim types, including complex ones. Adjusters with better intake briefs move faster even on the hard stuff.
The same headcount now handles 70% more daily claim volume without adding staff. The company absorbed seasonal volume spikes that would previously have required temporary hires.
Zero regulatory findings related to the automated resolutions across two state insurance department audits during the period. The audit trail held up completely.
The hardest part of this project wasn't the technology — it was the adjuster team's initial skepticism. They'd seen other automation initiatives promise to "help" and then create more work through false positives, exceptions, and override fatigue. The credibility of the system was built claim by claim, over the first 30 days, when adjusters could see that the auto-resolved claims were genuinely routine and the routing decisions were sound.
The other learning: the desk-ready briefs for complex claims turned out to be nearly as valuable as the automation itself. Senior adjusters, in particular, noted that starting from a prepared brief reduced their cognitive load significantly — especially on high-volume days. That feature was originally a secondary deliverable. We'd recommend it as a primary one on any similar project.
[CLIENT TESTIMONIAL PLACEHOLDER — confidential, will be added upon client approval]
We'll review your current claims workflow, identify which portion of your volume is automatable, and give you a realistic estimate of what the numbers could look like — before any commitment to build.
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