Prompts for financial analysis
Prompts for variance analysis, unit economics, and turning financial data into decisions.
Financial analysis is where raw statements become decisions — and the slow part is rarely the math, it's the narrative. Why did expenses come in over plan? Is this variance a timing blip or a real overspend? AI helps by drafting that interpretation quickly: it can explain budget-versus-actual variances and propose root causes to chase, write a plain-English expense narrative that separates timing from true variance, or turn a month-end close into a memo leadership will actually read.
The prompts here span the analyst's range. Some compute and interpret — unit economics, the margin impact of a price change, runway and spend-cut trade-offs. Others critically review, like stress-testing a model's assumptions for aggressiveness and internal consistency. And several are about packaging the answer: an investor update with a candid cash section, a leadership KPI dashboard with exact metric definitions, or the financial slides of a board deck.
The rule that overrides everything: these outputs are analysis aids, and the figures must be verified. A model will narrate a variance confidently whether or not its arithmetic holds, and it can't see the journal entry that explains the anomaly unless you tell it. Let it draft the story and frame the questions; you confirm the numbers and the conclusions.
What makes a good prompt for financial analysis
A strong financial-analysis prompt supplies the real figures, the comparison baseline, and the audience for the output. "Explain this variance to the board" needs different framing than the same analysis for the FP&A team. Give the model the categorized numbers, the period, and what decision the analysis feeds — then ask it to distinguish what the data shows from what it's inferring.
The best prompts build in verification and skepticism. Have the model show its formulas and intermediate steps, separate timing differences from genuine over- or underspend, and flag assumptions that look aggressive. Treat the result as a well-structured draft narrative: you keep the framing and questions, then independently confirm every figure before it informs a real decision.
Get sharper results
- 01Give the model the categorized actuals and the baseline it's comparing against; a variance narrative is only as good as the numbers behind it.
- 02Ask it to separate timing differences from true over- or underspend — conflating the two is the most common variance-analysis error.
- 03Have it show formulas and intermediate steps for any metric so you can re-derive the result before quoting it.
- 04Tell it the audience (board, investors, FP&A) up front; the right level of detail and candor depends entirely on who's reading.
Common questions
Should I verify the figures in an AI financial analysis?
Always. The output is an analysis aid, not an audited result. Language models can make calculation errors and will narrate them just as confidently as correct ones. Re-derive key metrics yourself and reconcile to source statements before the analysis informs any decision.
Can AI tell me why a variance happened?
It can propose plausible root causes worth investigating, but it can't see your transactions. Use it to generate a prioritized list of hypotheses and the questions to ask, then confirm the actual driver against your ledger and operational context.
How do I make a financial summary land with executives?
Tell the model the audience and lead with takeaways. Ask for headline-first commentary that states the result versus plan, the few drivers that matter, and the open items — then supports each with data, rather than walking through every line.
Update a 12-Month Rolling Forecast with New Actuals
Refreshes a 12-month rolling forecast using the latest closed-period actuals and updated business assumptions.
Explain Budget vs. Actual Variances for the Period
Computes and prioritizes material budget-versus-actual variances and proposes plausible root causes to investigate.
Break Down Unit Economics: CAC, LTV, and Payback
Calculates and interprets CAC, LTV, LTV:CAC, and payback period from acquisition and retention inputs.
Draft a Monthly Investor Financial Update
Turns monthly metrics, wins, and challenges into a candid, investor-grade financial update with a clear cash and runway section.
Write a Plain-English Expense Variance Narrative
Converts categorized expense variances into a readable monthly commentary that separates timing from true over- or underspend.
Model Best, Base, and Worst Cash-Flow Scenarios
Builds best, base, and worst cash-flow scenarios with period-by-period balances and flags when cash could turn negative.
Analyze Cash Runway and Model Spend-Cut Options
Calculates current runway and zero-cash date, then models how specific spend-cut options extend runway with their trade-offs.
Analyze Pricing Changes and Their Margin Impact
Quantifies the margin and break-even impact of a price change and stress-tests it against several volume scenarios.
Stress-Test the Assumptions Behind a Financial Model
Critically reviews a financial model's assumptions for aggressiveness, sensitivity, and internal consistency.
Design a Finance KPI Dashboard for Leadership
Specifies a grouped, audience-appropriate finance KPI dashboard with exact metric definitions and a layout plan.
Summarize Month-End Close Results for Leadership
Produces a skimmable month-end close memo covering results versus plan, balance-sheet moves, adjustments, and open items.
Draft the Financial Section of a Board Deck
Outlines the financial slides of a board deck with takeaway headlines, supporting data points, and anticipated-question talking points.