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Insurance

Insurance claim processing automation

How a regional insurance carrier restructured its claims workflow to support AI-assisted processing — reducing review time and improving outcome consistency.

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Results

Measurable outcomes after transformation

-60%
Average review time per claim
94%
Accuracy on structured data extraction
3×
Adjuster throughput improvement
-80%
Manual data entry effort
The challenge

A process that depended on individual expertise, not documented systems

The claims operation handled thousands of cases monthly, but the workflow depended almost entirely on adjuster experience. Decision logic was undocumented. Document formats varied widely. Volume spikes overwhelmed capacity.

Claims documents varied widely in format, completeness, and terminology across providers
Adjuster judgment was undocumented — decisions depended on individual experience
Inconsistent outcomes created disputes, rework, and compliance exposure
Volume spikes overwhelmed capacity, creating backlogs and delayed payments
Challenge diagram
The approach

Four stages to AI-supported claims processing

The transformation followed the EIC methodology: structure the process first, then the knowledge, then introduce automation incrementally.

01

Process mapping

Mapped the existing claims workflow end-to-end, documenting decision logic, exception types, and data requirements at each step.

02

Knowledge structuring

Built a structured knowledge base with claim classification rules, coverage criteria, and exception handling guidelines.

03

Assisted automation

Implemented AI-assisted document triage and extraction with adjuster oversight, validating quality before expanding scope.

04

Supervised rollout

Expanded AI execution to full claims pipeline with human review for edge cases and continuous quality monitoring.

Explore what this looks like for your operations

Every transformation starts with understanding how your specific processes work today — not a generic assessment.

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We start by mapping your actual process, not a hypothetical one
We identify what needs to be structured before automation is viable
We design a practical path that matches your capacity and risk tolerance