Summary: Claims is where insurance meets its customers and its regulators at the same moment, which is why it stayed manual long after the actuarial side modernized. The blocker was never intent: it was architecture. Adjudication runs on inconsistent documents, photographic evidence, layered policy language, and decisions that must survive disputes. That is now precisely the workload policy-driven agents handle in production. Here is the anatomy of evidence-linked claims adjudication, and the honest line between what automates and what should not.
Why claims resisted automation
A claim file is an adversarial document package: ACORD forms next to handwritten statements, repair estimates in a body shop’s format, medical records in another, and a camera roll of loss photos taken in a parking lot. The rules that adjudicate it are layered: policy language, endorsements, state regulation, and the carrier’s own guidelines, often varying by jurisdiction and program.
Classic automation could route the file but not read it. Rules engines could evaluate fields someone else keyed. OCR could transcribe without understanding. And pure-LLM approaches read everything while proving nothing, which in a disputed claim is the same as reading nothing.
The evidence-linked pipeline
Read the file, keep the receipts. The document pipeline classifies every page, extracts the operative values (dates of loss, coverage limits, deductibles, line items), normalizes them across formats, and attaches an evidence pointer to each: document, page, character offset. Cross-document reconciliation catches the classic fraud and error signals: the estimate that disagrees with the adjuster’s report, the date that precedes the policy period.
Weigh the visual evidence. Photos and video are inputs, not attachments. The platform verifies the image supports the written claim (the damage is where the loss description says, the extent matches the estimate) and escalates mismatches with both artifacts side by side. In litigation-prone lines, an image the system actually analyzed and linked beats an image someone presumably glanced at.
Evaluate policy deterministically.Plain-English policies encode coverage rules, exclusions, and thresholds; deterministic evaluation runs them identically on every claim, with the why-trail recording which provision fired on which value from which page. When the policyholder’s attorney asks why the claim was denied, the answer is a specific provision, a specific value, and a specific source, not a model’s paragraph.
Route by exception, expand by evidence. Clean claims within authority clear; everything else reaches an adjuster with the evidence pre-assembled: coverage question flagged, discrepancy highlighted, provision cited. Adjusters work the 10 claims that need judgment instead of screening 100 that do not, and autonomy expands per claim category as their edit data supports it, under review gates the compliance team owns.
The honest boundary
Not everything should automate. Total-loss disputes, injury severity, bad-faith exposure: those stay human, and the agent’s job is to make the human fast and fully evidenced. The line moves over time, but it moves on measured reviewer agreement, not on vendor enthusiasm. That discipline is what makes the automated share defensible: 99%+ decision accuracy in production is a property of the whole governed system, including its refusals.
Sizing it
The claims economics compound from three directions: adjuster hours returned, cycle-time value in SLA compliance and reserve accuracy, and dispute posture from evidence-linked files. An afternoon with claim volumes and handle times in the ROI calculator produces the first-order case; the claims processing use case page covers the workflow map, and medical necessity review shows the adjacent pattern in health lines.
