Claims Agent

How an insurer used AI to streamline low-complexity claims, and freed up adjusters to focus on what matters

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Challenge

Too Many Claims, Too Little Time

A mid-sized property and casualty insurer was processing hundreds of thousands of claims per year. The majority were low-complexity (lost luggage, cracked windshields, minor water damage) but each still required manual intake, triage, and approval. With labor costs rising and customer expectations for speed increasing, the claims operation had become a bottleneck. Leadership needed a way to scale without proportionally scaling headcount.

Adjusters were overwhelmed. 70% of claims were simple but still required manual intake, routing, and approval. Resolution times were climbing. Customers were frustrated. So were employees.

Human Bottlenecks

Every claim started the same way: submitted online, emailed, or called in. A human had to triage, verify details, request documents, and push it through. Simple claims sat in the same queue as complex ones. The result: inefficiency everywhere.

What If a Bot Could Handle It?

The idea: create a digital claims agent, one that could instantly process low-risk, rule-based claims without human delay, and escalate only the ones that needed review.

Approach

So We Built an AI Adjuster

We partnered with Claims Ops, IT, and Legal to design a claims automation model. It reviewed claim type, amount, customer history, and attached documentation, then made an instant decision, with auditability built in.

Solution

We Trained It on 3 Years of History

The model was trained on 2.1 million historical claims. It learned which cases were safe to auto-approve, which required validation, and which should always go to a human. Document scans, metadata, policy flags, all were part of the decision tree.

Methodology

We used a staged methodology: (1) define eligibility rules and escalation criteria with Claims Ops and Legal; (2) extract, clean, and label historical claims data for training; (3) train and validate the classification and approval model with holdout sets and human review of edge cases; (4) integrate the model into the claims platform via API with audit logging and override controls; (5) run a controlled pilot by product line before full rollout.

Data sources

Historical claims records (2.1M) including claim type, amount, policy details, and outcome. Document attachments and extracted fields (photos, estimates, invoices). CRM and policy data for customer history and risk flags. All data was anonymized or aggregated for model training; production inference used live claim and policy data within the existing security perimeter.

We Deployed It Into Production

The AI adjuster now runs in the background, screening and approving over 38,000 claims per month. It flags anomalies, checks for fraud risk, and integrates seamlessly into the existing claims platform. Adjusters still own complex cases, but now, they’re not buried in routine ones.

“We didn’t replace humans. We removed the waste, so people could focus on judgment, not busywork.”

Director of Claims Automation

We Measured the Impact

Resolution time dropped from 5.4 days to 1.2 days for eligible claims. NPS for the auto-resolved group rose by 18 points. And adjusters gained back over 7,000 hours annually to spend on complex, high-touch cases that actually need them. Visuals included an operations dashboard showing auto vs. human resolution rates, average resolution time by claim type, and NPS trends; plus audit views for every auto-approved claim so compliance could review samples.

Implementation timeline

Weeks 1–6: Requirements, data extraction, and eligibility rules. Weeks 7–14: Model development, back-testing, and integration build. Weeks 15–18: Pilot on one product line (e.g. renters) with side-by-side human review. Weeks 19–22: Tuning and expansion to additional lines. By week 24, full production rollout was complete and the 38,000+ claims/month run rate was achieved.

Take the next step

See how Intellimark can help you build a claims agent that scales, without losing the human touch.

Outcome

The insurer automated the routine and refocused human effort on value-added claims:

Metrics / Results

38,000+ claims auto-processed per month

1.2 day average resolution time (vs. 5.4 days)

+18 NPS points for automated group

7,000+ hours returned to adjusters annually

Fin.

Some claims need empathy. Some just need efficiency. Now they could deliver both.