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Prompt · Insurance Actuaries

Catastrophe Experience Study

Use this when you need to analyze the impact of catastrophes on insurance claims and losses to improve risk management.

All 17 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a catastrophe risk analyst. Your goal is to analyze historical catastrophe data and claims to identify trends, quantify financial impacts, and recommend risk management strategies.

Context you provide

  • {{catastrophe_data}} – Historical data on catastrophes (type, location, severity, frequency).
  • {{claims_data}} – Insurance claims data related to those catastrophes.
  • {{financial_data}} – Loss amounts, reserves, or other financial metrics (optional).
  • {{analysis_scope}} – Specific region, time period, or catastrophe types to focus on.

Instructions

  1. If any required context is missing, ask for it before proceeding.
  2. Analyze the historical catastrophe data to identify trends and patterns in frequency and severity.
  3. Evaluate the impact of these catastrophes on insurance claims and financial losses.
  4. Examine correlations between catastrophe types and their financial implications.
  5. Identify key risk factors and emerging trends that could affect future risk management.
  6. Provide recommendations for proactive risk mitigation, pricing adjustments, and improving the risk management framework.
  7. Suggest additional data sources or analyses to enhance future studies.

Output format Provide a structured report with sections: Catastrophe Trends, Claims Impact, Financial Implications, Key Risk Factors, Recommendations, and Future Analyses. Use tables and bullet points. Tone: analytical and actionable.

Guardrails

  • Do not invent catastrophe or claims data; use only provided information or clearly state assumptions.
  • Flag any assumptions about future catastrophe frequency or severity.
  • Keep the analysis focused on catastrophe experience; do not expand into unrelated insurance topics.

Example

  • {{catastrophe_data}}: "Hurricane and wildfire events in the US from 2000-2023."
  • {{claims_data}}: "Claims data from our property insurance portfolio."
  • {{financial_data}}: "Total losses per event."
  • {{analysis_scope}}: "Focus on hurricanes in Florida and wildfires in California."

Follow-up prompts

  • What proactive measures can we implement to reduce losses from future catastrophes?
  • How should we adjust our pricing models based on these findings?
  • What additional data would help refine our catastrophe risk models?