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ACORD forms are a standardized format for exchanging insurance data—but they’re complex, dense, and often vary slightly in layout across carriers. Manually extracting structured data from these forms is tedious, error-prone, and hard to scale. Tensorlake parses ACORD forms and extracts consistent, schema-based outputs that you can write directly into your internal systems. Teams use Tensorlake to capture coverage details from ACORD submissions and certificates, store them in databases, and automate customer success and audit workflows. Tensorlake combines layout-aware parsing, schema-driven extraction, and citations so you can build reliable ingestion pipelines with traceability back to the original document.
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Try it in a Colab Notebook

Try parsing a sample ACORD form with this Colab Notebook

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What you can extract from ACORD forms

ACORD workflows vary by line of business and carrier, but most ingestion pipelines depend on the same core signals. Tensorlake can extract:
  • Policy and submission identifiers: policy number, agency, carrier, producer, NAIC, submission or reference IDs
  • Named insured and parties: named insured, additional insured, certificate holder, mailing addresses
  • Dates: effective date, expiration date, retroactive date, policy term
  • Coverages and limits: coverage type, limit amounts, deductibles, occurrence versus claims-made flags, umbrella and excess limits
  • Locations and operations: location addresses, classification codes, description of operations
  • Signatures and attestations: presence of signatures and signature blocks when applicable
The output is schema-validated JSON that you can treat as a stable interface for downstream automation.

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Common ACORD use cases

  • Coverage ingestion to database: extract coverages and limits into structured tables that power account servicing, renewals, and risk review.
  • Audit and compliance: standardize coverage evidence across customers and carriers, then flag missing fields or expired dates for follow-up.
  • Customer success automation: route requests to the right team based on coverage type, limits, and special conditions.
  • Workflow triggers: create underwriting tasks, request missing information, or open tickets when required fields are missing.
  • Downstream integrations: feed policy administration systems, CRMs, data warehouses, and reporting pipelines with normalized coverage data.

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Citations for every extracted field

Structured extraction is more useful when it is explainable. Tensorlake provides layout information for page elements, including page numbers and bounding boxes. This makes it easy to:
  • Show where a field came from in a review UI
  • Create audit trails for coverage values and attestations
  • Debug extraction quality by jumping directly to the source region on the page
If you are building an agentic workflow, citations let you keep deterministic control flow and still provide human-verifiable evidence.