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AI USE CASE

Insurance Policy Comparison Extraction Tool

Automatically extracts and compares coverage terms from insurer policy documents for broker clients.

Typical budget
€5K–€20K
Time to value
3 weeks
Effort
2–6 weeks
Monthly ongoing
€200–€800
Minimum data maturity
basic
Technical prerequisite
spreadsheet savvy
Industries
Professional Services, Finance
AI type
nlp

What it is

This tool reads PDF policy documents from 3–5 insurers and extracts key fields — coverage limits, exclusions, and excess amounts — into a structured side-by-side comparison table. Insurance brokers typically spend 2–3 hours per quote manually pulling this data; automation reduces that to under 15 minutes. Brokers handling 20+ quotes per month can reclaim 40–60 hours of staff time monthly. Client-ready comparison outputs also improve perceived professionalism and close rates.

Data you need

PDF or digital policy documents from at least 3 insurers, with consistent enough structure for field extraction.

Required systems

  • none

Why it works

  • Start with the 2–3 insurers whose documents are most consistently structured before expanding scope.
  • Define a standard extraction template (fields, labels) upfront and validate it against 20+ real past policies.
  • Build a lightweight human review step for flagged low-confidence extractions rather than removing oversight entirely.
  • Use a tool that supports iterative prompt or rule refinement so staff can fix recurring extraction issues without developer help.

How this goes wrong

  • Policy PDFs from some insurers are scanned images rather than text-layer documents, causing extraction errors.
  • Coverage terminology varies significantly between insurers, making automated field mapping unreliable without tuning.
  • Staff continue to manually verify every output anyway, negating the time savings.
  • Edge-case exclusions buried in schedules or endorsements are missed, creating liability risk for the broker.

When NOT to do this

Avoid this if your insurer document mix is mostly scanned paper or heavily non-standard layouts — without clean digital PDFs, extraction accuracy will be too low to trust without full manual review, defeating the purpose for a small team.

Vendors to consider

Sources

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