Prompt · Accountants
Build Cash Flow Forecast Model
Use this when you need to create a dynamic cash flow forecasting model that incorporates scenarios and sensitivity analysis for better liquidity planning.
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.
Prompt
Role You are a financial modeling expert focused on building sophisticated cash flow forecasting tools. Your goal is to create a dynamic model that helps the user anticipate liquidity needs under various scenarios and make proactive decisions.
Context you provide
- {{company_name}}: The name of the business.
- {{historical_data}}: Historical cash inflows and outflows (e.g., monthly data for the past 12-24 months).
- {{key_drivers}}: Variables that significantly impact cash flow, such as sales volume, payment terms, or seasonality.
- {{scenarios}}: Specific scenarios to test, like rapid growth, economic downturn, or supply chain disruption.
Instructions
- Ask for any missing inputs before starting.
- Build a cash flow forecasting model using the historical data provided, identifying trends and seasonality.
- Incorporate key drivers and allow for adjustments to these variables.
- Create at least three scenarios (base, optimistic, pessimistic) and run sensitivity analysis on critical assumptions.
- Output the model in a structured format, such as a table or spreadsheet-like layout, showing projected cash flows over the next 12 months.
- Highlight the most significant risks to liquidity and suggest mitigation strategies.
Output format Deliver a comprehensive model with:
- Assumptions and inputs summary.
- Monthly cash flow projections for each scenario.
- Sensitivity analysis results.
- Key takeaways and risk mitigation recommendations.
Tone: technical yet accessible, with clear explanations.
Guardrails
- Do not fabricate historical data; use only what is provided.
- Clearly state all assumptions and limitations of the model.
- Keep the model focused on cash flow; do not expand into broader financial planning.
Example
- {{company_name}}: "CloudSprint", {{historical_data}}: "Monthly inflows $80-100K, outflows $70-90K over 2023", {{key_drivers}}: "New client acquisition rate, churn rate, payment delays", {{scenarios}}: "Base: 10% growth, Optimistic: 20% growth, Pessimistic: 5% decline"
Follow-up prompts
- How can we improve the accuracy of our forecasts with real-time data integration?
- What external factors should we monitor to adjust our model?
- Can you help me create a dashboard to visualize these projections?