Policy Summarization with AI
A typical commercial property policy runs 60 to 120 pages between the dec page, base form, endorsements, and schedules. Personal lines policies are shorter but still dense. AI is exceptional at compressing this into something a human can actually reason about — as long as you ask the right way.
What You'll Learn
- How to use Claude and ChatGPT to summarize policies and endorsements
- The "layered summary" technique that produces both a 30-second and 5-minute view
- How to compare two policies side by side
- Common pitfalls and how to avoid them
Why This Workflow Matters
If you are a broker fielding "what's actually covered?" questions, an underwriter reviewing prior carrier paper, an adjuster confirming the operative form on a 2-year-old loss, or a risk manager comparing renewal proposals — the bottleneck is reading time. AI can give you a clean first read in 60 seconds.
The catch: AI does not always notice what is missing. A summary that lists "buildings, business personal property, and business income" might miss that the policy excludes flood and earth movement, has a 5 percent named-storm deductible, or carries a sub-limit on signs. You set the agenda by what you ask for.
The Layered Summary Technique
Ask for two summaries at once: a one-paragraph executive view and a structured deep-dive. This way you can scan the first and dive into the second only when needed.
You are an experienced commercial property underwriter.
I am pasting a small commercial property policy below.
Produce two outputs:
OUTPUT 1: 3-sentence executive summary covering insured,
total insured value, and the single most important coverage
or exclusion to be aware of.
OUTPUT 2: Structured summary with these sections:
- Named insured and additional insureds
- Effective and expiration dates
- Locations and TIV
- Coverages and limits (Building, BPP, BI, EE, sub-limits)
- Deductibles (including named-storm and special perils)
- Notable endorsements (by form number and short description)
- Notable exclusions
- Conditions or warranties that change behavior
- Three open questions I should clarify with the broker
Constraint: Do NOT invent any form numbers, limits, or dates
not present in the document. If a section is not addressed,
write "Not addressed in provided text".
Document:
[paste policy text]
This produces a deliverable you can drop into a file note in seconds.
Summarizing Endorsements
Endorsements are where coverage is actually shaped. The base form might give you fire, but an HO 04 81 endorsement adds limited theft for a seasonal rental. The trick with endorsements is to focus the prompt narrowly.
You are a personal lines underwriter. I am pasting an
endorsement. In 5 bullet points, explain:
1. What the endorsement does (in plain English)
2. What it adds to coverage
3. What it removes or restricts
4. The specific limits or sub-limits it introduces
5. The customer-facing one-line explanation I would put in
an email
Endorsement: [paste]
This is one of the highest-ROI uses of AI in personal and small commercial lines. Endorsements are dense, and pulling a clean summary used to take 15 minutes per form.
Side-by-Side Policy Comparison
Comparison is the most common question in any sales or renewal cycle. "How does our renewal compare to the prior carrier?" "How does our quote stack up against ABC Insurance?"
You are a commercial broker. I am pasting two policies — A
(renewal proposal from Carrier 1) and B (alternative from
Carrier 2). Produce a side-by-side comparison table with
these rows:
- Effective date
- Total premium
- Building limit
- BPP limit
- BI limit and waiting period
- Property deductible
- Named-storm deductible
- Equipment breakdown included? Yes/No
- Cyber coverage included? Yes/No
- Notable inclusions in A but not B
- Notable inclusions in B but not A
After the table, write a 4-sentence narrative summary aimed
at a non-technical risk manager.
Policy A: [paste]
Policy B: [paste]
Common Pitfalls
Hallucinated form numbers. Always verify ISO and AAIS form numbers against the actual document. Models sometimes invent plausible-sounding form numbers like "HO 04 90" that do not exist.
Limit confusion. When a policy has both per-occurrence and aggregate limits, the model sometimes blurs them. Always cross-check against the dec page.
Sub-limit erosion. A model may report a $1,000,000 building limit and miss that the dec page also lists a $5,000 sub-limit on outdoor signs. Ask explicitly: "List every sub-limit and its dollar amount."
Endorsement ordering. When endorsements modify earlier endorsements, the model can lose track of the final operative coverage. For complex layered endorsements, ask the model to "produce the final, post-endorsement coverage statement for [coverage]" rather than just summarizing each endorsement.
A Quick Workflow
For a typical commercial policy review, your AI-assisted process looks like:
- De-identify the policy text (remove insured name, account numbers if your tool requires it)
- Run the layered summary prompt and save the structured summary into your file note
- Run the endorsement prompt for any non-standard form
- If comparing carriers, run the side-by-side prompt
- Verify all form numbers, limits, and dates against the actual document before sharing externally
A 60-page policy review that used to take 90 minutes can now take 20.
A Note on Customer-Facing Use
These summaries are for internal use and your own analysis. If you are sending coverage explanations to a policyholder, treat the AI output as a first draft. A licensed producer or claims professional must review it. Coverage is a legal question, not a language question.
Key Takeaways
- The layered summary technique gives you a 30-second view and a 5-minute view in one prompt.
- Endorsements are the highest-ROI summarization target. Focus prompts narrowly on what each endorsement adds, removes, and limits.
- Side-by-side comparison prompts are powerful for renewal and sales cycles, but always verify form numbers and limits against the source documents.
- AI summaries are internal aids. A licensed human reviews anything that goes to a policyholder.