Policy Document Parsing
The insurance policy as it was actually issued — schedule, wording, and endorsements, structured together.
Policy document parsing is the structuring of insurance policies into machine-usable form: the schedule (named insured, limits, deductibles, effective dates — the table-like data specific to this policyholder), the wording (the insuring agreement, conditions, and exclusions — standardized language shared across many policies of the same form), and the defined terms that give both their precise legal meaning. Parsing a policy well means recovering not just the text but the relationships — which schedule limit applies to which wording section, which defined term governs which clause, and eventually which endorsements have amended the base document, per the endorsements-extraction entry's compositional problem.
The task splits by content type, each demanding different handling. Schedule extraction is structured-field work — table and form extraction applied to the policy's declarations page, with the accuracy stakes of any financial figure (a misread limit is a misrepresented coverage amount). Wording extraction is contract analysis: clause segmentation identifying insuring agreements, conditions, and exclusions as distinct provisions; defined-term resolution linking every capitalized term ("Insured," "Occurrence," "Property") back to its definition, often in a separate section pages away; and cross-reference resolution following the wording's internal pointers ("as described in Section IV"). Because standard-form wordings recur across an insurer's book (and across the industry, via ISO and similar standardized forms), form-identification — recognizing which known wording this is — lets parsing reuse prior structural analysis rather than re-deriving it from scratch each time, a significant efficiency in high-volume lines.
The parsed policy becomes the reference object that coverage verification, claims adjudication, and certificate reconciliation all query against — which means parsing quality is upstream of every downstream insurance-AI capability this glossary catalogs. Ambiguity in wording interpretation remains a human coverage-counsel question; parsing's job is making the policy's actual text — schedule, wording, and their relationships — reliably retrievable and citable, so that whoever answers the coverage question is answering from what the policy says rather than what someone remembers it saying.
Is this actually covered? Answered by reading the policy, not by remembering it.
The policy is what the endorsements made it — extracting the amendments that change everything.
From issuance to renewal — the operational pipeline that keeps policy data current and usable.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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