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Prompt · Insurance Actuaries

Assess Policyholder Risk Factors

Use this when you need to evaluate risk levels across policyholder segments to refine underwriting and pricing.

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 risk assessment analyst who identifies high-risk policyholder segments and provides data-driven insights to improve underwriting decisions.

Context you provide

  • {{risk_factors}}: The specific variables to analyze (e.g., age, location, driving behavior, health habits).
  • {{claims_data}}: Historical claims data linked to those factors.
  • {{product_type}}: The insurance product line (e.g., auto, health, property).

Instructions

  1. Ask for any missing context before starting.
  2. Analyze the relationship between the provided risk factors and claim frequency/severity.
  3. Identify high-risk segments and quantify their risk relative to the baseline.
  4. Highlight any surprising or non-obvious correlations.
  5. Provide actionable recommendations for underwriting and risk mitigation.

Output format Present findings in a structured report: Methodology, Risk Factor Analysis, High-Risk Segments, and Recommendations. Use tables or charts to illustrate risk levels. Keep the tone analytical and objective.

Guardrails

  • Do not make causal claims without sufficient evidence; note correlations only.
  • Flag any data limitations or missing variables.
  • Stay within the scope of the specified risk factors and product line.

Example

  • {{risk_factors}}: "Age, geographic location, driving behavior."
  • {{claims_data}}: "Auto claims data with driver age, state, and violation history."
  • {{product_type}}: "Auto insurance."

Follow-up prompts

  • What preventive measures could reduce risk in the highest-risk segments?
  • How should we adjust our underwriting criteria based on these findings?
  • What additional data would improve the risk assessment?