Public preview
Monthly variance review
Which budget and prior-period variances actually need a decision?
Calculate monthly dollar and percentage variances, then organize timing, volume, price, mix, classification, and data-quality hypotheses for review.
Inputs
- ✓Monthly actuals
- ✓Budget
- ✓Prior-period actuals
Outputs
- ✓Material variances
- ✓Possible driver categories
- ✓Review questions
Checks performed
- ✓Account mapping consistency
- ✓Dollar and percentage variance
- ✓Small-denominator warning
Guided synthetic example
See the check from input to decision
July overhead finished above budget even though total revenue was close to plan.
This is the operating model we use at Level. The percentages are estimated shares of the work in a typical review. Actual effort varies with data quality, complexity, and the issue found.
Start with the source records
About 15% of work| Account | Actual | Budget | Variance |
|---|---|---|---|
| Software | $18,400 | $12,000 | +$6,400 |
| Vehicle expense | $9,200 | $10,000 | -$800 |
| Insurance | $14,500 | $14,000 | +$500 |
Run the deterministic check
About 20% of workCalculate dollar and percentage variance, suppress immaterial noise, and flag large percentages caused by small budget denominators.
Surface the flagged result
About 10% of workNeeds review
Software expense is $6,400, or 53%, above budget and explains most of the overhead miss.
Use recorded finance judgment to analyze possible reasons
About 25% of workAt Level, we record the review logic, known explanations, and questions our finance professionals use in this situation. AI applies that documented human judgment to organize the most plausible reasons, without pretending it knows which reason is true.
Possible reasons to investigate
The variance could reflect a new annual contract, miscoding, headcount-driven licenses, an unbudgeted tool, or timing rather than ongoing overspend.
Make the final human judgment
About 20% of workA Level finance professional validates the source evidence, challenges the AI-assisted analysis, and decides which explanation is supported.
Make the operating decision
About 10% of workTrace the software transactions, identify the owner and renewal terms, then update the forecast or correct the classification.
Use this system
Start with the example, then inspect the structure
- Download the invented input and expected output.
- Replace one field at a time with a safe test value.
- Compare the result with the expected structure.
- Have a finance professional review every exception before acting.
MCP preview
run_synthetic_example {"slug":"monthly-variance-review"}Human review is part of the system
This check organizes evidence and surfaces exceptions. A person with finance domain expertise still needs to interpret the result, validate the source records, and discuss the operating decision.
Open source does not mean open client data. We publish reusable finance tools, checks, templates, and playbooks. Client records, workpapers, credentials, communications, and private configurations remain confidential.