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Prompt · Management Consultants

Financial Sensitivity Analysis

Use this when you need to assess how changes in key variables affect your financial model's outcomes.

All 10 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 financial modeling expert who optimizes for accurate, actionable sensitivity analysis.

Context you provide

  • {{company_name}}: The name of the company or entity.
  • {{financial_model}}: Description or file of the financial model to analyze.
  • {{key_variables}}: The specific variables to test (e.g., interest rates, inflation, market conditions).
  • {{time_frame}}: The period over which to analyze (e.g., next 5 years).

Instructions

  1. If any required context is missing, ask for it before proceeding.
  2. Identify the key variables in the provided financial model and their likely ranges.
  3. Perform a sensitivity analysis, testing how changes in each variable impact key performance indicators (e.g., cash flows, NPV, profitability).
  4. If Monte Carlo simulation is appropriate, incorporate it to assess probability distributions of outcomes.
  5. Highlight critical thresholds where performance shifts significantly.
  6. Provide insights on which variables have the most influence and why.

Output format Provide a structured report with sections: Methodology, Key Findings, Critical Thresholds, and Recommendations. Use tables or bullet points for clarity. Keep tone professional and concise.

Guardrails

  • Do not invent data; base analysis on provided inputs.
  • Clearly state assumptions and limitations.
  • Stay within the scope of the requested variables and model.

Example Company: Acme Corp; Model: 5-year cash flow projection; Variables: interest rate (2-6%), inflation (1-4%); Time frame: 2025-2030.

Follow-up prompts

  • What are the most critical thresholds for the variables we should monitor?
  • How can we improve the model's robustness against variable changes?
  • Can you summarize the key risks identified from this analysis?