Treat AI-generated financial-model output as a draft, not a source of truth. Check it against the model’s required structure, trace questionable values to authoritative data or approved assumptions, test the formulas and financial relationships, and leave unsupported gaps unresolved rather than silently replacing them with zero.
Start by defining what a valid model should contain
Before generation or review, write down the model contract: required and optional sections, field names, units, time periods, formats, sign conventions, acceptable ranges, and provenance requirements. Specify which assumptions need approval. There is no universal schema for AI-generated financial output; tailor these requirements to the model’s purpose and materiality.
Knowing the model’s intended ingredients and structure makes it possible to spot omissions instead of treating whatever the system returned as complete. ICAEW’s June 5, 2026 guidance recommends checking that core sections are present and closely reviewing the initial model (ICAEW guidance on identifying AI errors in financial models).
Classify each missing or invalid field
Log each defect with its cell or field address, expected rule, actual output, source of truth, materiality, and status. A practical classification includes:
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- Missing required field: a required row, column, schedule, or input is absent.
- Blank or null: the field exists but contains no value.
- Malformed value: a number, date, or period is not in the expected format.
- Wrong unit or sign: for example, a value is in thousands rather than units, or an outflow is shown with the wrong sign.
- Out-of-range or inconsistent value: a value violates an allowed range or conflicts with another schedule.
- Formula defect: a formula is missing, replaced by a hard-coded number, or inconsistent across periods.
- Unsupported value: the output has no traceable source and is not an approved assumption.
This log is a practical review aid, not a taxonomy prescribed by a regulator. It helps distinguish a formatting problem from a missing source input or a deeper modeling issue.
Choose a correction route based on evidence
Use the route that can be justified and independently checked. Compare possible fixes by whether the value is traceable to authoritative evidence, whether its financial meaning is sound, how it may affect downstream calculations, how material and reversible the change is, and whether another reviewer can verify and audit it. Guidance supports human challenge, documentation, and risk-proportionate controls; it does not establish a universal ranking of remediation methods.
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| Situation | Appropriate handling |
|---|---|
| An authoritative input exists | Retrieve or re-enter the value from that source, preserve its provenance, and recalculate affected schedules. |
| The field represents an assumption | Label it clearly and obtain the approval required by the model’s governance process. |
| No source or approved assumption exists | Keep an explicit unresolved marker and block dependent conclusions until the gap is resolved; do not invent a plausible-looking value. |
| A zero is supported | Use zero only when it is the verified input or an approved assumption for that specific field. |
Do not adopt zero-fill as a blanket rule. An IMF technical note from August 2025 describes a particular financial-data analysis prompt that instructed the model to set NaN values to zero. That task-specific instruction is not a general accounting or financial-modeling rule (IMF, Generative Artificial Intelligence for Compliance Risk Analysis).
Validate the model after the correction
Correcting a field is not enough: determine what depends on it, recalculate or regenerate the affected schedules, and rerun the model’s checks. Review the following:
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Rank #3
- Required sections, sheets, rows, and columns are present; hidden content and unintended external links are understood.
- Formulas are consistent across all forecast periods, and key calculations have not been replaced with hard-coded values.
- The balance sheet balances without an unexplained plug concealing an error.
- Debt schedules are complete, operating capacity limits are respected, and depreciation is treated consistently.
- Negative balances and asset or liability signs are explainable.
- Internal checks work in every forecast period, not only the first year.
- Long or complex formulas are reviewable and update correctly when inputs change.
These checks reflect ICAEW’s model-review guidance. Repeating or varying a prompt can be useful as an additional diagnostic, but ICAEW notes that repeated requests can produce different answers. Similar outputs from multiple generations do not prove that a value is correct.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Document the repair and escalate material defects
Keep the original output, defect log, source supporting each repair, approved assumptions, recalculation results, reviewer, and unresolved items. Escalate material errors, unclear financial treatment, and defects that cannot be tied to a reliable source to a qualified reviewer. Match the amount of independent challenge and ongoing monitoring to the model’s purpose, exposure, complexity, and materiality.
Rank #4
Regulatory guidance provides context, not a universal field-repair procedure. Revised US interagency model-risk guidance dated April 17, 2026, is risk-based and non-prescriptive; it excludes generative and agentic AI and directs organizations to use broader risk governance to determine controls for tools outside its scope (Federal Reserve supervisory guidance; see also OCC Bulletin 2026-13). The guidance is most relevant to banking organizations above $30 billion in assets, though it can also matter to smaller organizations with significant model risk.
In the UK, the Bank of England/PRA’s current version of SS1/23, published and effective April 23, 2026, sets overarching model-risk principles for banks and addresses AI risks where applicable (Bank of England/PRA SS1/23). ICAEW’s article is professional guidance for accountants and model users, not binding law. None of these sources creates one globally applicable repair rule for every company or generated spreadsheet.
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