How One Insurer Cut Claims Adjudication From 45 to 12 Min

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.

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.

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.