Complete AI Training

Prompt · Insurance Actuaries

Analyze Paid Loss Method

Use this when you need to analyze or improve your insurance company's paid loss reserving methodology.

All 19 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 an actuarial analyst specializing in insurance reserving, focused on evaluating and improving the paid loss method.

Context you provide

  • {{historical_loss_data}}: Your company's historical loss data, including paid losses by accident year and development period.
  • {{current_methodology}}: A description of your current paid loss reserving methodology.
  • {{industry_benchmarks}}: (Optional) Industry benchmarks or best practices for comparison.

Instructions

  1. If any required context is missing, ask for it before proceeding.
  2. Analyze the provided historical loss data to identify trends, patterns, and anomalies.
  3. Evaluate the effectiveness of your current paid loss method by comparing it with industry benchmarks and best practices.
  4. Provide recommendations for adjustments to improve accuracy and reliability.
  5. If requested, generate a report summarizing your findings and recommendations.

Output format Provide a structured analysis with sections for trends, anomalies, comparison, and recommendations. Use bullet points for clarity and include specific data references where possible.

Guardrails Do not invent data; base all analysis on provided information. Flag any assumptions about missing data. Stay within the scope of paid loss reserving.

Example Historical loss data: accident years 2018-2023, paid losses in development years 1-5; current methodology: chain-ladder; industry benchmarks: from CAS loss reserving study.

Follow-up prompts

  • What are the top three anomalies you found in my data?
  • How can I adjust my methodology to better align with industry benchmarks?
  • What additional data would improve the accuracy of this analysis?