Best Prompts for Finance

A monthly variance report can be numerically correct and still be unhelpful. Leaders need to understand what moved, why it moved, whether the explanation is supported, and what requires follow-up. Finance teams often spend more time gathering commentary than calculating the variance itself.

AI can help structure an explanation from a controlled data pack, but it must not invent business drivers. The prompt should force a reconciliation to the supplied figures and distinguish a known cause from a plausible question for a budget owner.

Why ordinary prompting fails

“Explain these monthly variances” gives the model permission to turn patterns into causes. It may say that lower travel spend came from tighter controls or that revenue increased because of a campaign, even when the data only shows a change. It can also mix units, ignore sign conventions, or focus on immaterial lines.

A stronger prompt defines the comparison, materiality rules, accounting context, and evidence available for each explanation. It makes arithmetic traceable and sends unsupported causes back as questions.

Reusable prompt

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ROLE
You are a finance analysis assistant. Prepare a monthly variance explanation from supplied financial data and approved business commentary. Reconcile all figures and never invent a cause.

REQUIRED INPUTS
1. Entity, department, currency, period, and accounting basis
2. Actual, budget, prior month, and prior-year figures at the required account level
3. Sign convention and units
4. Materiality thresholds, both absolute and percentage
5. Approved budget-owner commentary with source and date
6. Known accruals, reclasses, one-time items, timing differences, and accounting changes
7. Mapping of accounts to reporting categories
8. Close status and data cutoff time

STEPS
1. Validate period labels, units, signs, missing values, and duplicate rows.
2. Recalculate each requested variance. Show the formula used.
3. Reconcile category totals to the supplied source totals. Stop and flag any unreconciled difference.
4. Apply the supplied materiality thresholds and rank material variances.
5. For each material variance, separate observed movement from documented cause.
6. Connect causes only to approved commentary or supplied accounting records.
7. Classify each cause as volume, rate, mix, timing, one-time item, accounting treatment, or unknown, when supported.
8. Draft concise management commentary and list follow-up questions for unknown or weakly supported drivers.
9. Identify items that may reverse or continue, but only when the supplied evidence supports that view.

OUTPUT FORMAT
A. Validation and reconciliation status
B. Material variance table: line | actual | comparator | amount | percent | favorable/unfavorable
C. Explanation table: observed movement | documented driver | source/date | classification | confidence
D. Management summary, maximum 200 words
E. Follow-up questions with suggested owner
F. Potential recurring or reversing items
G. Data limitations and unresolved differences

EVIDENCE AND UNCERTAINTY RULES
- Use only supplied figures and commentary.
- Show calculations and preserve the supplied rounding policy.
- Do not infer causation from a numeric pattern.
- Label unsupported drivers “Unknown” and convert them into follow-up questions.
- Do not describe a variance as favorable or unfavorable without the supplied convention.
- Keep actuals, budget, forecast, and prior periods distinct.
- If totals do not reconcile, do not produce final management commentary.
- Flag commentary dated before the current close as potentially stale.

What to provide

Provide a clean table with stable account identifiers, explicit periods, units, currency, and sign conventions. Include source totals so the model can reconcile the detail. State materiality rules and rounding policy. Add approved commentary from budget owners, dated and tied to specific lines where possible.

Include known accruals, reclasses, timing items, one-off transactions, mapping changes, and whether the close is final. Do not paste credentials, bank details, payroll-level personal data, or other information that is unnecessary for the analysis. Follow your organization’s rules for handling financial data.

How to review the output

Begin with validation and reconciliation. Reperform a sample of calculations and confirm that category totals match the source. Check signs carefully, since expense and revenue conventions can reverse the meaning of favorable and unfavorable. Verify that materiality was applied as instructed.

For each narrative explanation, ask: what evidence establishes this cause? A number can establish movement, not motive. Trace documented drivers to their dated commentary or accounting record. Review unknowns with budget owners and update the analysis rather than allowing a plausible sentence to stand. The finance owner should approve the final management commentary and any forward-looking interpretation.

Where it fails

The prompt will not discover a true business cause when the data pack contains only ledger movements. Poor account mappings, late entries, and inconsistent budget versions can produce misleading comparisons. It also cannot resolve accounting judgments or determine the proper treatment of a transaction without qualified review.

Do not use the output as a substitute for close controls, reconciliations, audit evidence, or approval. Forecast implications are especially fragile when based on one month. If the figures do not reconcile, fix the source or mapping before asking for a polished explanation.

Practical takeaway

The best variance prompt is strict about the difference between movement and cause. It checks the numbers, applies materiality, cites documented drivers, and turns the rest into named follow-up questions. That creates a useful draft without concealing unfinished finance work.

Try the prompt in Agentic Workers on a limited reporting category, then reconcile every total and approve every causal statement before sharing the commentary.

<!-- X derivative: The best monthly variance prompt reconciles the figures, separates movement from cause, and turns unsupported explanations into questions for the right owner. -->