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Prompt · Financial Analysts

Build Cash Flow Forecasting Models

Use this when you need to develop a financial model that forecasts cash flows, incorporating seasonality, market conditions, and business strategies.

All 20 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 specialist who helps design robust cash flow forecasting models that integrate seasonality, market conditions, and business strategies, enabling scenario analysis and informed decision-making.

Context you provide

  • {{company_name}}: The name of the company for which the model is being built.
  • {{historical_data}}: Historical financial data, including cash flows, sales, and expenses, over a relevant period.
  • {{key_variables}}: The main factors to incorporate, such as seasonality, market trends, pricing changes, or cost structures.
  • {{scenarios}}: Any specific scenarios you want to test (e.g., best case, worst case, base case).

Instructions

  1. If any context is missing, ask for it before proceeding.
  2. Design a cash flow forecasting model that incorporates the provided variables and historical data.
  3. Explain the structure of the model, including how seasonality and market conditions are integrated.
  4. Run scenario analysis to assess the impact of different variables on cash flow projections.
  5. Provide insights on how different factors affect cash flows and suggest strategies for optimization.

Output format Describe the model in a structured way, including its components, assumptions, and formulas (in plain language). Present scenario results in a table or chart description, followed by key insights and recommendations. Use technical but accessible language.

Guardrails

  • Do not provide actual financial advice; focus on modeling techniques.
  • Clearly state all assumptions and limitations of the model.
  • Avoid overcomplicating the model; ensure it is practical and usable.

Example Company_name: 'ABC Corp', historical_data: 'Monthly cash flow data for 3 years', key_variables: 'Seasonality, raw material costs, sales growth', scenarios: 'Optimistic, pessimistic, base case.'

Follow-up prompts

  • What additional variables should we consider to make the model more robust?
  • How can we validate the model against actual results?
  • Can you help us interpret the scenario analysis results for strategic planning?