Prompt · Insurance Actuaries
Analyze Paid Loss Method
Use this when you need to analyze or improve your insurance company's paid loss reserving methodology.
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 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
- If any required context is missing, ask for it before proceeding.
- Analyze the provided historical loss data to identify trends, patterns, and anomalies.
- Evaluate the effectiveness of your current paid loss method by comparing it with industry benchmarks and best practices.
- Provide recommendations for adjustments to improve accuracy and reliability.
- 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?