How One Insurer Cut Claims Adjudication From 45 to 12 Min

An insurer's claims team handed manual PDF and handwritten-doc review to an AI workflow that cut a 45-minute task to about 12 minutes and absorbed rising volume without new hires.

~75%

Cut from manual adjudication time

< 4 weeks

Implementation Time

Jordan Gurrieri

Co-founder & CEO

BlueLabel

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The Challenge

A mid-sized insurance organization was seeing claim volumes rise while its claims adjudication process remained heavily manual. Analysts had to review PDFs and handwritten claims documents, extract relevant information, map benefits, calculate payouts, and determine which claims required additional review. The organization needed a way to absorb increased volume without adding staff, while also introducing AI-driven document matching and decision-support capabilities that its internal IT team did not yet have in-house.

What They Built

BlueLabel designed and built an AI-enabled claims adjudication workflow that converts PDFs and handwritten claims documents into structured data, classifies the documents, and matches extracted information against policy details. The system uses OCR and large language model-based extraction to identify relevant benefits, support payout calculations, and flag low-confidence or edge-case scenarios for human review. Confidence scoring, audit logging, and escalation paths were built into the workflow so straightforward claims could move faster while regulated decisions remained transparent and reviewable. The initial use case focused on accident expense claims, then expanded into additional insurance adjudication scenarios with more complex review requirements.

BlueLabel started with the messiest part of the problem: turning inconsistent claim documents into data an adjudication engine could trust. PDFs and handwritten forms were run through OCR and large language model extraction to pull benefits, amounts, and claim details into a structured schema. Extracted fields were matched against policy details so the system could support payout calculations rather than leave them to manual lookup. Around that core, the team layered the controls a regulated process demands: confidence scoring on every decision, audit logging for traceability, and escalation paths that route low-confidence or edge-case claims to human reviewers. Clean, high-confidence claims move through automatically, while anything ambiguous stays in front of a person. The build began with accident expense claims as a contained first use case, proving the workflow before widening it. Once the pattern held, BlueLabel expanded it into additional claim types with more complex review requirements, treating the initial adjudication engine as a foundation rather than a one-off tool.

AI Role

AI performs OCR and LLM extraction on claim documents, matches data to policy benefits, supports payout calculation, scores confidence, and flags edge cases for human review.

Infrastructure

OCR pipeline for PDFs and handwritten documents • Structured claims and adjudication data store • Policy and benefit reference data • Confidence-scoring and audit-logging layer • Human-in-the-loop review queue

Integration Points

Document intake (PDF and handwritten) into OCR • Extraction outputs matched to policy details • Payout-calculation support • Confidence thresholds route to human-review escalation • Audit log captured for each decision

Impact

Up to 75% less manual adjudication time

Tasks that previously took 40-45 minutes were fully automated or reduced to about 10-12 minutes.

Working proof of concept in days

The team moved from a high-level business goal to a functioning prototype fast enough to validate feasibility and build stakeholder confidence early.

Pilot expanded into a broader adjudication program

After proving value in the initial accident expense workflow, the solution expanded into additional insurance claim types and higher-complexity use cases.

Technology Utilized

Implementation Complexity

Time Savings

Cost Reduction

Headcount Avoidance

The work required more than a simple AI wrapper because the solution had to handle messy claim documents, including PDFs and handwritten materials, and translate them into structured data suitable for adjudication. The team needed to align AI extraction, benefit mapping, payout logic, confidence scoring, and exception handling with real-world insurance workflows. Regulatory needs also shaped the architecture, requiring audit logs, transparent decision support, and human-in-the-loop review for lower-confidence scenarios.

Best Fit For

Insurance organizations and other regulated, document-heavy businesses with rising case volumes and limited room to add headcount. Especially relevant for teams that need AI to extract information from complex documents, support structured decisions, and keep human oversight for exceptions.

Jordan Gurrieri

Co-founder & CEO

BlueLabel

Co-founder & CEO of BlueLabel, leading generative AI innovation and digital transformation for enterprises across healthcare, finance, travel, and real estate.

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Industry:

Insurance

Business Function:

Operations

Legal & Compliance

Project Cost:

Cost not disclosed

AI Pattern:

Document Processing & Extraction

Decision Support & Scoring

AI-Accelerated Custom Software

Value Type:

Time Savings

Cost Reduction

Headcount Avoidance

AI Model:

Gemini

Frequently Asked Questions

How did an insurer cut claims adjudication from 45 to 12 minutes with AI?

The experts built an AI workflow that runs claim PDFs and handwritten forms through OCR and large language model extraction, structures the data, and matches it to policy benefits to support payout calculation. Clean, high-confidence claims flow through automatically while ambiguous ones route to human reviewers, cutting a task that took 40-45 minutes to about 10-12 minutes.

What AI tools and approach were used for claims adjudication?

The system combines OCR with large language model extraction to turn inconsistent documents into a structured schema, then matches fields to policy details for payout support. Around that core it adds confidence scoring on every decision, audit logging, and escalation paths that keep low-confidence and edge-case claims in front of a person.

What results did the insurer achieve?

Manual adjudication time fell up to 75%, with 40-45 minute tasks reduced to about 10-12 minutes; a working proof of concept was standing within days; and after proving out on accident expense claims the workflow expanded into additional, higher-complexity claim types.

How long did it take to see results?

The team moved from a high-level goal to a functioning prototype within days, starting with accident expense claims as a contained first use case before widening the engine to more complex claim types.

Who is this claims automation approach best for?

Insurers and other regulated, document-heavy businesses with rising case volumes and little room to add headcount, especially teams that need AI to extract information from complex documents, support structured decisions, and keep human oversight for exceptions.

Originally published by

BlueLabel