Do not import or rely on an AI-generated financial model until you have checked its structure, formulas, financial logic, and outputs. Treat the workbook as a draft: a polished layout or confident explanation does not show that its assumptions or calculations are sound. Excel’s error checks can flag common spreadsheet problems, but only a knowledgeable review can assess whether the model represents the business correctly.
Start by defining what the model should contain
Before opening formulas, write down the model’s intended purpose, expected schedules, assumptions, scenarios, and outputs. You need a clear picture of what a good model should look like to judge whether an AI-generated version is complete. ICAEW recommends treating AI as a collaborator whose work must be challenged, not as a substitute for modeling skill: How to identify AI errors in financial models.
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Compare the workbook with that expected structure. Depending on the model’s purpose, core sections may include a cover or summary, assumptions, scenario management, and clearly organized schedules. Check that inputs are distinguishable from calculations and outputs, so you can see what drives a result and what is merely reporting it.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Unhide and inspect sheets, rows, and columns; hidden content may contain assumptions or calculations that affect visible results.
- Review external workbook links and determine whether each is intentional. Remove links that are not required.
- Look for missing schedules, unclear scenario controls, or unexplained gaps between assumptions and outputs.
Inspect formulas and data for spreadsheet errors
Look for hard-coded values and broken patterns
Search for numbers embedded directly in formulas where a reference to an assumption cell should be used. A formula such as a growth calculation with a fixed percentage can make a later assumption change ineffective. Compare formulas across adjacent rows and forecast periods: an unexpected change in the formula pattern may indicate an omitted reference, a copied formula error, or a legitimate exception that needs explanation.
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Pay particular attention to unusually long or complex formulas in key statements. Complexity is not automatically wrong, but a formula that is difficult to inspect makes errors harder to find. Ask for a clearer breakdown when a material result depends on logic that cannot be readily traced.
Run Excel’s built-in error checks
Excel’s formula error checking can identify common issues, including cells returning errors, inconsistent formulas, omitted adjacent cells, numbers stored as text, formulas referring to empty cells, and invalid table entries. Microsoft describes these as diagnostic checks, not proof that a workbook is correct: Detect errors in formulas in Excel.
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If the workbook is set to manual calculation, recalculate it before reviewing errors. Investigate each flagged cell rather than dismissing it reflexively; ignored errors can disappear from later checks. A clean error-checking result still cannot confirm that assumptions, business logic, or financial relationships are valid.
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Challenge the financial logic, not just the formulas
Trace important outputs back to their assumptions and schedules. Test whether a change in a key assumption produces an explainable change in the result. Check the following across the entire forecast, not only in the first year:
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- Balance sheet: Confirm that assets equal liabilities plus equity without an unexplained plug. A balancing figure that forces the totals to match is not evidence that the underlying model is sound.
- Debt and cash: Verify debt balances and the completeness of the debt schedule, and assess whether projected cash needs make sense.
- Operations: Confirm that operating capacity constraints are reflected where relevant.
- Assets and liabilities: Check depreciation and investigate balances that become negative without a valid business reason.
- Internal checks: Confirm that checks work in every forecast period; a check that passes only in the first year can conceal later formula or logic breaks.
Make assumptions explicit and review how scenarios are controlled. If a material output cannot be explained from the model’s inputs and schedules, do not treat it as decision-ready.
Choose review depth according to the model’s use and risk
The more consequential the decision, the more scrutiny the model warrants. For a material use, have an independent person with appropriate financial-modeling knowledge challenge both the construction and the results. Ian Schnoor, executive director of the Financial Modeling Institute, told ICAEW: “You will not be able to get it to build a good financial model unless you already know how to.”
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U.S. banking supervisors’ 2026 interagency guidance offers useful governance context: it frames model risk in terms of assumptions, complexity, input quality, exposure, purpose, and use, and describes validation through conceptual soundness, outcome analysis, and ongoing monitoring. It expects to be most relevant to banking organizations with more than $30 billion in total assets, while recognizing that risk exposure may make it relevant to smaller organizations. The guidance excludes simple spreadsheet arithmetic from its model definition and places generative and agentic AI models outside its scope; it is not a universal legal requirement for AI-generated spreadsheets. See the Federal Reserve, OCC, and FDIC’s 2026 model risk management guidance.
Decide whether the workbook is ready to use
Import or rely on the model only after you can trace its key calculations, explain its assumptions, account for hidden content and links, resolve material formula warnings, and defend its financial logic across all forecast periods. If any material output remains unexplained, treat the workbook as unfinished and get a qualified reviewer to challenge it before using it for a decision.
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