Claims Documentation with AI

A claim file is a story. The good ones tell it clearly: what happened, what we investigated, what we decided, and why. The bad ones leave gaps that auditors, supervisors, attorneys, and reinsurers spend hours filling in. AI is unusually well-suited to claims documentation because most of the work is converting messy human inputs (notes, calls, photos, emails) into clean, structured prose.

What You'll Learn

• How to convert adjuster notes into structured claim narratives
• Prompts for activity logs, evaluation memos, and roundtable summaries
• How to extract key facts from FNOL submissions and recorded statements
• Documentation patterns that survive litigation and regulatory review

Why Documentation Quality Matters

Underwriters lose accounts because of bad claims experience, but reinsurers lose money because of bad claims documentation. A loss with weak file notes looks like a settlement waiting to happen. A loss with crisp documentation that shows reserve rationale, investigation steps, and coverage analysis tells the reinsurer (and the courtroom) that the carrier was diligent.

The same is true for state DOI audits and bad-faith litigation. Unfair Claim Settlement Practices Act standards in nearly every state require fair, prompt, and well-documented investigations. AI lets you produce documentation that consistently meets that bar.

Converting Adjuster Notes to a Structured Narrative

You finish an inspection. You jot down ten lines of notes on your phone. AI turns those notes into a file note that reads like an attorney drafted it.

You are an experienced commercial property claims adjuster.
Convert the following raw inspection notes into a structured
file note suitable for a claims diary. Structure:

- Date and time of inspection
- Persons present
- Areas inspected
- Observed damage (factual, no conclusions)
- Cause of loss observations (factual)
- Mitigation efforts observed
- Photos taken (just say "Photos: see attached log")
- Recommendations and next steps
- Estimated reserve adjustment (only repeat what is in the
  notes — do not invent numbers)

Notes: [paste raw notes]

Constraints: Do not invent observations. If something is
not in the notes, do not include it. Plain English. Past
tense. Third person.

Drafting Evaluation Memos

Evaluation memos are where claims professionals lay out reserve rationale, settlement value, and litigation exposure. These are the documents that supervisors review and that attorneys subpoena.

You are a senior bodily injury claims examiner. Draft a
reserve evaluation memo using the facts I provide. Sections:

- Claim summary (3 sentences)
- Liability analysis (what is the percentage exposure and
  why)
- Damages analysis (medical specials, lost wages, general
  damages range)
- Settlement value range (low / likely / high)
- Litigation risk factors
- Recommended reserve and rationale
- Recommended next action

Use only the facts I provide. Do not invent medical specials
or jury verdict comparators. Where facts are missing, list
them as "Open items" at the end.

Facts: [paste]

Roundtable Summaries

Many carriers run weekly claim roundtables where complex losses are discussed. Notes from these meetings are gold for documentation but tedious to produce.

You are a claims supervisor. Below is a transcript of our
Tuesday claim roundtable. Produce a structured summary
suitable for the claim file with these sections per claim
discussed:

- Claim number
- Brief facts
- Key issue raised at roundtable
- Decisions reached
- Action items with owner

Constraint: Only include claims actually discussed in the
transcript. Do not infer beyond what was said.

Transcript: [paste]

Extracting Facts from FNOL

A First Notice of Loss often arrives as a long, unstructured story from a panicked policyholder. Extracting clean facts is what kicks off the rest of the claim.

You are a claims intake specialist. Extract structured data
from the FNOL narrative below. Output a JSON-like list with:

- Date of loss
- Time of loss
- Location of loss
- Type of loss (peril)
- Persons involved (with role: insured, claimant, witness)
- Description of incident (1-2 sentences)
- Reported injuries (yes/no, brief description)
- Reported property damage (yes/no, brief description)
- Police involvement (yes/no, agency, report number)
- Insured's preferred contact method
- Any statements made about fault by the insured

If a field is not in the narrative, write "not stated".

Narrative: [paste]

This output can feed directly into your claims management system or your roundtable prep.

Working with Recorded Statements

Recorded statements (RS) are common in liability and bodily injury claims. AI can summarize a transcript or pull out specific issues.

You are a senior auto liability claims adjuster. I am
pasting a recorded statement transcript from the claimant.
Produce three outputs:

1. Factual summary of the claimant's account (5-7 bullets)
2. Statements that affect liability assessment
3. Statements that affect damages assessment
4. Inconsistencies with prior known facts (only those I
   provide below — do not invent prior facts)

Prior known facts: [paste any other facts]

Transcript: [paste RS transcript]

A Note on Photos and Video

Modern AI tools (GPT-4o, Claude, Gemini) can analyze images. You can upload a damage photo and ask for an objective description. Two cautions:

1. AI is not a licensed appraiser or estimator. It can describe what it sees but cannot price the loss.
2. Photos may contain identifying information (license plates, faces). Treat them like any other PII — use only carrier-approved tools.

A useful prompt for photo descriptions:

You are an experienced property claims adjuster. Describe
this photo objectively for the claim file. Include:
- What is shown (room, exterior, vehicle, etc.)
- Visible damage
- Visible cause indicators
- Anything that would suggest pre-existing damage
- Anything notable about scope or severity

Do not estimate the cost of repair. Do not assign cause
beyond what is visually evident.

Documentation Patterns That Hold Up

Across thousands of claim files, the documentation that survives bad-faith and DOI review tends to share these traits:

• Contemporaneous. Entered close to the event, not weeks later.
• Factual. Separates observations from conclusions.
• Cited. References policy language, statutes, and prior file entries by date or section.
• Reserved. Acknowledges what is unknown and what investigation is open.
• Action-oriented. Ends with clear next steps and owners.

Your AI prompts should reinforce these patterns. Ask for past tense, third person, factual observations only, and explicit "open items" sections.

Key Takeaways

• AI excels at converting raw adjuster notes, FNOL narratives, and roundtable transcripts into structured, audit-grade claim documentation.
• Always tell the AI not to invent facts and to flag missing items as "open items."
• Photo and statement analysis is possible with modern multimodal AI but never replaces a licensed adjuster's judgment.
• Strong claim documentation is contemporaneous, factual, cited, reserved, and action-oriented — and your prompts should enforce all five.