Finance Prompt Examples
AI prompts for financial analysis tasks — budget reviews, financial modelling assumptions, investment assessments, and management reporting.
Budget Variance Analysis Prompt
intermediateAnalyses budget versus actual performance, identifies material variances, investigates root causes, and recommends corrective actions.
Analyse the budget variance for [DEPARTMENT/COMPANY] for [PERIOD].
Budget vs Actual:
[PASTE LINE ITEMS WITH BUDGET AND ACTUAL FIGURES]
Provide:
1. **Variance Summary Table**:
| Line Item | Budget | Actual | Variance (£) | Variance (%) | Status |
Flag items with >10% variance as "Investigate"
2. **Material Variances** (those >5% or >£[THRESHOLD]):
For each:
- Description of the variance
- 2-3 likely root causes
- Whether this is a timing difference, one-off, or trend
- Recommended action
3. **Overall Assessment**:
- Are we on track for full-year targets?
- Top 3 risks to watch
- Forecast adjustment recommendation
4. **Action Items**: Specific follow-ups with suggested owners
Present in a format suitable for a monthly finance review meeting.Key takeaway: AI variance analysis that includes root cause hypotheses saves finance teams from spending hours investigating obvious explanations.
Financial Model Assumptions Review Prompt
advancedReviews and stress-tests the assumptions in a financial model, identifying which assumptions have the biggest impact on outcomes.
Review the assumptions in this financial model and identify risks.
Model purpose: [WHAT THE MODEL IS FOR — e.g., 3-year revenue forecast, project business case]
Key assumptions:
[LIST EACH ASSUMPTION WITH ITS VALUE — e.g., "Annual revenue growth: 15%", "Customer churn: 5%"]
For each assumption:
1. **Reasonableness**: Is this assumption realistic given market conditions? Rate as Conservative / Reasonable / Aggressive / Unrealistic
2. **Sensitivity**: What happens to the model output if this assumption changes by ±20%? High / Medium / Low impact
3. **Evidence**: What data or benchmarks support this assumption?
4. **Risk factors**: What could cause this assumption to be wrong?
Then provide:
- **Sensitivity ranking**: Which assumptions matter most? (Top 3)
- **Scenario analysis**: Model output under Bear / Base / Bull scenarios
- **Key risks**: The 3 most dangerous assumptions and how to mitigate them
- **Recommendation**: Is this model reliable enough for decision-making? What additional analysis is needed?Key takeaway: Assumption sensitivity analysis reveals which 2-3 variables actually determine whether a financial model succeeds — focus your validation effort there.
Cash Flow Forecasting Prompt
intermediateGenerates a structured cash flow forecast from historical data and known commitments, highlighting liquidity risks and suggesting management actions.
Create a 13-week cash flow forecast based on:
Current cash position: £[AMOUNT]
Historical monthly inflows: [LAST 6 MONTHS DATA]
Historical monthly outflows: [LAST 6 MONTHS DATA]
Known commitments:
- [UPCOMING PAYMENTS: dates and amounts]
- [EXPECTED RECEIVABLES: dates and amounts]
- [SEASONAL PATTERNS]
Provide:
1. **Weekly Forecast Table**:
| Week | Opening | Inflows | Outflows | Closing | Minimum Balance |
2. **Key Assumptions**: What assumptions drive the forecast
3. **Liquidity Risk Assessment**:
- Weeks where closing balance drops below [MINIMUM THRESHOLD]
- Worst-case scenario (if major receivable is delayed 30 days)
- Buffer adequacy
4. **Cash Management Recommendations**:
- Actions to improve cash position
- Invoicing or collection actions needed
- Payments that could be deferred if needed
5. **Sensitivity Scenarios**:
- What if revenue is 20% below forecast?
- What if a major customer pays 30 days late?
Flag any weeks requiring immediate attention.Key takeaway: AI-assisted cash flow forecasts that flag potential liquidity gaps 3-6 months ahead give businesses time to act.
Investment Proposal Evaluation Prompt
advancedEvaluates an investment or capital expenditure proposal against financial criteria, including NPV, payback period, risk assessment, and strategic alignment.
Evaluate this investment proposal:
Project: [PROJECT NAME]
Investment required: £[AMOUNT]
Timeline: [DURATION]
Expected cash flows: [YEAR-BY-YEAR PROJECTED CASH FLOWS]
Discount rate: [WACC OR REQUIRED RATE]
Strategic context: [HOW THIS FITS THE BUSINESS STRATEGY]
Evaluate:
1. **Financial Metrics**:
- Net Present Value (NPV)
- Internal Rate of Return (IRR)
- Payback period
- Return on Investment (ROI)
2. **Risk Assessment**:
- Key risk factors
- Probability-weighted scenarios (optimistic, base, pessimistic)
- Break-even analysis: what must be true for this to pay back?
3. **Strategic Fit**:
- Alignment with company strategy
- Competitive implications of investing / not investing
- Opportunity cost of this capital
4. **Comparable Analysis**: How does this compare to typical returns in [INDUSTRY]?
5. **Recommendation**: Invest / Reject / Request more information
- With conditions or milestones if appropriate
- Alternative structures to consider (phased investment, partnership, etc.)
Present as a 1-page investment committee memo.Key takeaway: AI evaluation of investment proposals ensures consistent methodology and prevents common pitfalls like ignoring opportunity costs or using inconsistent discount rates.
Month-End Financial Commentary Prompt
beginnerGenerates narrative commentary for management accounts, translating numbers into business insights suitable for board reporting.
Write financial commentary for the month-end management accounts.
Period: [MONTH/YEAR]
Audience: [BOARD / SENIOR MANAGEMENT / DEPARTMENT HEADS]
Financial data:
[PASTE P&L SUMMARY, KEY BALANCE SHEET ITEMS, CASH POSITION]
YTD vs Budget:
[PASTE YTD COMPARISON]
Key business events this month:
[LIST NOTABLE EVENTS — new contracts, losses, launches, etc.]
Write commentary covering:
1. **Executive Summary** (3-4 sentences): Overall financial position in plain language
2. **Revenue**: What drove revenue this month? Trends, new business, losses. Compare to budget and prior year.
3. **Costs**: Material cost movements. Are costs under control? Any unexpected items?
4. **Profitability**: Margin trends. What's impacting margins?
5. **Cash**: Cash position and trajectory. Any concerns?
6. **Outlook**: What do we expect for the coming months? Any headwinds or tailwinds?
Writing style:
- Plain business English, not accounting jargon
- Connect numbers to business activities ("Revenue grew 8% driven by the new enterprise contract signed in March")
- Flag items requiring board attention or decisions
- Be balanced — don't only highlight good newsKey takeaway: Financial commentary that connects numbers to business activities is far more useful to non-finance stakeholders than data tables alone.
Patterns
Key patterns to follow
- Connecting financial data to business activities and events produces far more actionable commentary than numbers alone
- Sensitivity analysis on the top 3 most impactful assumptions is more valuable than reviewing all assumptions equally
- Structured financial prompts ensure consistent methodology across analyses
- Scenario-based outputs (bear/base/bull) give decision-makers a realistic range rather than false precision
FAQ
Frequently asked questions
AI handles financial calculations well but can make errors, especially with complex multi-step calculations. Always verify critical numbers independently. Use AI for structure, commentary, and analysis frameworks rather than as a calculator for final figures.
Use enterprise AI solutions with proper data protection agreements. Anonymise sensitive data where possible. Never share individual salary data, customer financial information, or pre-announcement earnings through consumer AI tools.
AI accelerates financial analysis by handling routine tasks — variance analysis, commentary drafting, model structure — but cannot replace the judgment, relationship management, and strategic thinking of experienced finance professionals.
Highest value: variance analysis, financial commentary, forecast narratives, and assumption documentation. These are time-consuming writing tasks where AI saves 50-70% of effort. Complex modelling and strategic recommendations still require human expertise.
Cross-check calculations against source data, verify formulas and methodologies, sense-check results against benchmarks, and have a qualified finance professional review all outputs before distribution. Implement a review checklist specific to financial AI outputs.
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