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Document Extraction (IDP)

Intelligent Document Processing (IDP) is AI that reads unstructured insurance documents

technicalPublished 2026/06/05

FAQs

How is IDP different from OCR?
OCR converts images to text; IDP adds context and understanding — it knows which text is a policy limit, a loss description, or a table, producing structured data rather than raw characters.
Is document extraction accurate enough to trust?
On clean templated documents, yes; on handwritten or poorly-scanned inputs, accuracy drops, so human review of low-confidence extractions remains standard practice.

Related Terms

  • Loss Run

    A loss run is a report from an insurer detailing a policyholder's claims history over a period

  • OCR vs IDP

    OCR converts document images into machine-readable text; IDP (Intelligent Document Processing) adds AI understanding on top to extract structured, contextual

  • Straight-Through Processing (STP)

    STP is the automated handling of a transaction

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Document Extraction, often called Intelligent Document Processing (IDP), is the AI capability that turns paper and PDF chaos into structured data. Insurance runs on documents — applications, loss runs, ACORD forms, policy declarations, medical records, adjuster reports — and most arrive as unstructured files that humans have historically rekeyed by hand.

IDP goes beyond traditional OCR (which just converts images to text). It understands context: it knows that a number in a certain position is a policy limit, that a block of text is a loss description, that a table is a schedule of values. Modern IDP combines OCR, natural language processing, and machine learning to extract meaning, not just characters.

The business case is direct: manual document handling is slow, expensive, and error-prone, and it scales linearly with volume. IDP automates it, often cutting processing time from hours to seconds per document while reducing rekeying errors.

The honest caveat is accuracy on messy inputs. Clean, templated documents extract near-perfectly; handwritten notes, poor scans, and non-standard formats degrade results — which is why human review of low-confidence extractions remains standard. For insurance buyers, the key questions are: what document types does it handle, what's the accuracy, and how does it flag uncertain extractions for review.