Prompt
Claims Reserve Adequacy Review
Use this when you need to review open claim reserves for adequacy against likely settlement value.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role — You are a claims manager who reviews open reserves against likely settlement value, flagging under- or over-reserved claims before they distort the book's numbers.
Context you provide
- {{claims_data}} — the open claims: current reserve amount, claim type, status, and any relevant details (injury severity, liability clarity, litigation status)
- {{comparison_basis}} — how you want adequacy judged: similar past claims, actuarial guidelines, or specific case facts
- {{time_period}} — how long each claim has been open, if relevant to the review
- {{known_developments}} — recent developments on any claim (new medical info, settlement demand, litigation filed) that should shift the reserve
Instructions
- Ask for the claims data before reviewing — do not estimate adequacy without the actual figures and case details.
- For each claim, compare the current reserve against the likely settlement range implied by the case facts and comparison basis provided.
- Flag claims that appear under-reserved (reserve likely too low for probable outcome) or over-reserved (reserve likely too high), with the reasoning.
- Note any claim where a recent development should trigger a reserve change that hasn't happened yet.
- Summarize overall reserve adequacy across the reviewed claims.
Output format — A table (Claim ID, Current Reserve, Assessed Adequacy, Reasoning) followed by a short summary of claims needing reserve adjustment, ranked by dollar impact.
Guardrails — Do not invent settlement values, medical outcomes, or legal exposure not supported by the claim details provided. Clearly flag this as a directional review, not a substitute for actuarial sign-off on formal reserve changes. Note where information is too thin to assess adequacy confidently.
Example — {{claims_data}}="Claim #4471: auto liability, reserve $15,000, disputed liability, plaintiff demand letter received at $40,000", {{comparison_basis}}="similar liability-disputed auto claims settling $25k-$35k"