Treat AI-generated financial-model output as a draft, not an authority. Check that required fields are present, trace questionable values to reliable source data, test formulas and financial relationships, and document every correction. If a value has no verified source or approved assumption, leave it unresolved and stop dependent conclusions rather than silently substituting zero.
Start with a clear definition of valid output
Before generating or reviewing a model, define what the output is supposed to contain. Set out required and optional sections, field names, units, periods, formats, sign conventions, acceptable ranges, and provenance requirements. Identify which assumptions need approval. There is no universal schema for AI-generated financial models, so tailor these rules to the model’s purpose and materiality.
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Knowing the intended structure also makes it easier to spot omissions. ICAEW’s June 5, 2026 guidance recommends understanding a model’s ingredients and structure and checking that its core sections are present. ICAEW’s guidance on identifying AI errors in financial models emphasizes close human review of generated work.
Classify the problem before changing a field
Log each defect with its cell or field, the expected rule or type, the actual output, the source of truth, its materiality, and its status. A practical classification includes:
#1 Best Overall
- Missing: a required field, row, or schedule is absent.
- Blank or null: a field exists but contains no value.
- Malformed: a number, date, or period does not follow the expected format.
- Wrong unit or sign: for example, a value is expressed in dollars rather than thousands, or a liability has an unexpected sign.
- Out of range: a value violates an allowed range or operating constraint.
- Inconsistent: related values disagree across schedules or periods.
- Formula defect: a formula is missing, inconsistent, or replaced with a hard-coded value.
- Unsupported: the output has no traceable source and is not an approved assumption.
Repair from evidence, not plausibility
If an authoritative input exists, retrieve or re-enter it and preserve its source. If a field represents an assumption, label it clearly and obtain the approval required by your process. If neither a source nor an approved assumption exists, mark the field unresolved and block conclusions that depend on it.
Do not use zero as a blanket substitute for missing data: zero may imply a real financial value, such as no debt or no revenue, rather than unknown information. Use zero only when it is verified or explicitly approved for that field. An August 2025 IMF technical note gives an example of a prompt instructing a model to set NaN values to zero for a particular financial-data analysis task; that task-specific instruction is not a general accounting or modeling rule. The IMF technical note should be read in that limited context.
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Validate the model after each correction
Correcting a cell is not enough: recalculate or regenerate affected schedules and check whether the change has altered dependent outputs. ICAEW’s checklist points to several areas where generated spreadsheets can fail:
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- Check that the balance sheet balances and that no plug is concealing an unexplained difference.
- Review debt schedules for completeness.
- Test capacity limits and other operating constraints.
- Check depreciation and investigate unexplained negative balances.
- Look for hard-coded numbers that prevent updates and long or complex formulas that are difficult to review.
- Inspect hidden sheets, rows, and columns, as well as unintended external links.
- Confirm that internal checks remain active and work in every forecast period.
Asking an AI system to regenerate or vary an answer can help expose inconsistencies, but ICAEW notes that repeated requests can produce different answers. Agreement between generations is not proof that a figure or formula is correct; verify it against the model specification and source data.
Rank #3
Choose a correction route you can justify
A questionable field may call for another generation attempt, source-data retrieval, a manual correction, or escalation. Compare the options on whether the value is supported by authoritative evidence, whether its financial meaning is justified, how it could affect downstream behavior, how material and reversible the change is, and whether an independent reviewer can verify and audit it. The cited guidance supports human challenge, documentation, and controls proportionate to risk; it does not prescribe a universal ranking of repair methods.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Document changes and escalate material defects
Keep the original output and a record of each defect, the source used to repair it, any approved assumption, recalculation results, reviewer, and unresolved items. Send material or unresolved defects to a qualified reviewer before relying on affected conclusions. The level of independent challenge and monitoring should reflect the model’s purpose, exposure, complexity, and materiality.
Rank #4
Regulatory guidance is jurisdiction- and organization-specific, not a universal field-repair rule. The US interagency model-risk guidance revised April 17, 2026, is most relevant to banking organizations above $30 billion in assets and may also matter to smaller organizations with significant model risk. It is non-prescriptive and expressly excludes generative and agentic AI; it says broader risk governance should guide controls for tools outside its scope. The Federal Reserve guidance and the OCC’s summary of the revised guidance describe those limits. 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, including management of AI risks where applicable. See SS1/23. ICAEW’s article is professional guidance for accountants and model users, not binding law.
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