Insurance
Automating Claims Intake Without Losing the Human Review Step
A mid-size US insurance carrier was processing 500+ claims per week through a manual triage queue. We built the AI-assisted intake system that cut processing time by 60% — without removing the human review step where it mattered most.
Client
Insurance Carrier (Confidential)
Industry
Insurance
Services
AI Engineering, Process Automation, API Integration
Location
United States
The Challenge
Claims adjusters were spending an average of three hours per claim on data entry, document parsing, and initial triage categorisation — before any actual claims assessment had taken place. The intake process was a bottleneck: error rates in initial categorisation ran at 18%, backlogs built up during peak periods, and the compliance team spent significant time manually auditing for regulatory gaps. New adjusters needed weeks of onboarding just to learn the classification logic.
The client knew automation was the answer — but they had tried a previous RPA implementation that misclassified complex, edge-case claims and generated regulatory risk. What they needed was a system that could handle high-volume routine intake automatically while intelligently escalating ambiguous or high-value claims to a human adjuster with full context already assembled. The human had to stay in the loop — just where they actually added value.
The Solution
An AI-assisted claims intake pipeline with automated document parsing, intelligent triage scoring, and a human-in-the-loop review layer — routing straightforward claims straight through and escalating complex cases to adjusters with structured context pre-loaded.
Intelligent Document Parser
Built a document intelligence layer that extracted structured claim data from unstructured PDF submissions, emails, and photographs — eliminating manual data entry for 78% of incoming claims and feeding a normalised data model directly into the triage engine.
Triage Scoring Engine
Deployed a classification model trained on five years of historical claims outcomes to score each new claim by complexity, fraud risk, and regulatory exposure — routing low-risk claims to straight-through processing and flagging high-risk claims for priority human review.
Human-in-the-Loop Review Layer
Designed the adjuster review interface to surface AI-assembled claim summaries, extracted evidence, and scoring rationale in a structured format — so adjusters spent their time on judgment calls, not data gathering, with full audit trails for every decision.
Reduction in average claims intake processing time
Claims handled per week through the automated pipeline
Triage accuracy rate on the AI classification model
Compliance incidents in the 12 months post-launch
Estimated annual operational savings in adjuster hours