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To keep an AI-assisted financial model auditable and reproducible, retain the exact inputs and assumptions used for each run, document sources and AI-assisted changes, identify the released workbook version, preserve test and reviewer evidence, and assign a human owner. A later reviewer should be able to trace important outputs through formulas and assumptions to their source data—and repeat the run using the retained inputs.
What should an audit-ready AI-assisted model record show?
AI can draft formulas, explain logic, or help debug a workbook, but its output is not validation. Treat generated formulas and logic as unverified until a capable person has tested and reviewed them. The record should let someone who was not involved in creating the model understand what it is for, where its material inputs came from, what changed, which tests were run, and which version was approved.
Keep a controlled model record alongside the workbook or in an approved repository. Include the following items, as applicable:
- Purpose and ownership: model owner, preparer, intended use, users, decisions supported, and outputs considered material.
- Run identity: workbook name and release version, run date, relevant reporting period, and the exact input data or controlled snapshot used.
- AI-assistance history: AI service and model version if available, date, task or prompt specification, relevant inputs, generated formulas or code, resulting workbook, and material human edits. Keep records in line with organizational retention and security policies.
- Model documentation: key assumptions, source list, units, currency and scale, sign conventions, operating instructions, limitations, and descriptions of non-obvious formulas, macros, queries, or external connections.
- Control evidence: test cases and results, exceptions and their resolution, reviewer comments, approval, and a change log describing substantive edits and their effect on important outputs.
Do not put confidential financial information into an AI service unless its use is approved under your organization’s data, security, retention, and vendor policies. There is no universal approved-service list established by the sources cited here.
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How much review does the model need?
Set review depth according to the model’s intended use, complexity, materiality, and the consequences of an error. This is a practical risk-based approach, not a universal legal checklist.
| Use case | Proportionate controls |
|---|---|
| Low-impact exploratory analysis | Document purpose and assumptions, retain input and output versions, and obtain a peer check appropriate to the analysis. |
| Reporting, financing, valuation, or another consequential decision | Use a controlled release, independently check material logic and outputs, retain detailed test and source evidence, and obtain documented review and approval before use. |
The revised US interagency Supervisory Guidance on Model Risk Management, issued by the Federal Reserve, OCC, and FDIC on April 17, 2026, is supervisory guidance for banking organizations. It supersedes the earlier SR 11-7 guidance, is risk-based, and says practices should be tailored. It expressly excludes generative and agentic AI models; it says organizations’ broader governance practices should guide controls for tools and processes outside its scope. It is not a direct set of requirements for AI-generated spreadsheets or a universal rule for every company. The NIST AI Risk Management Framework is voluntary, and ICAEW’s spreadsheet principles are professional good practice rather than a statement of law. Apply the obligations and policies relevant to your jurisdiction and organization.
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How do you preserve input lineage?
For every material input, record enough detail to identify the evidence used in the released run. A value without a traceable source, date, unit, and transformation is difficult to verify later—especially if it came from a live system or a source that can change.
- Source: identify the source system, report, file, or responsible data owner.
- Time and version: record the extraction date, reporting period, and source version where available.
- Meaning: specify units, currency, scale, and sign convention.
- Transformation: describe adjustments, filters, mapping, aggregation, or other processing between the original source and the workbook input.
- Refresh behavior: state whether a connection refreshes automatically, requires a manual action, or uses a fixed extract.
- Evidence retained: preserve a controlled snapshot or immutable reference for the released model run, subject to records and data-retention policies.
Reconcile important imported or externally sourced values to their source. If an input changes after the run, a reviewer should be able to distinguish the value used at release from the value currently visible in a refreshed workbook. ICAEW’s Twenty principles for good spreadsheet practice emphasizes input quality, source checks, and a clear separation of inputs, processes, and outputs.
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How should you structure the workbook for inspection?
Make it easy to follow the path from inputs, through calculations, to outputs. Clear structure reduces the chance that a reviewer mistakes an assumption for a formula or overlooks a calculation that affects a decision.
- Label input cells, calculation areas, and outputs; identify units, currency, scale, and sign conventions.
- Enter each assumption once where practical, and clearly identify where it is changed for a scenario.
- Use consistent, understandable formulas; prefer simpler constructions when they provide the same result and are easier to inspect.
- Explain non-obvious logic and document macros, queries, named ranges, external links, and other connections that can affect results.
- Inspect hidden sheets, rows, columns, and relevant cells, as well as formulas or links that feed material outputs.
- Include an overview sheet or controlled companion document with the model’s purpose, owner, intended use, version, assumptions, limitations, operating steps, and controls.
ICAEW notes in The auditor’s review of management spreadsheets (2024): “Unlike most IT systems, spreadsheets often lack a robust audit trail, making it difficult to track changes and understand who made them.” A well-organized workbook helps a reviewer inspect logic, but structure alone does not establish that the model is correct.
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How do you version the workbook and explain changes?
Give each released version a consistent identifier and preserve approved prior versions. Attach or embed a change log that explains the change rather than relying on a file timestamp or version-history feature to tell the whole story.
| Change-log field | What to record |
|---|---|
| Identity | Date, release version, author, and reviewer. |
| Change | Assumptions, formulas, data, logic, or other material elements changed. |
| Reason | Why the change was made and, where relevant, the source or request that prompted it. |
| Impact | Effect on important outputs, including whether key conclusions or decisions changed. |
| Disposition | Review comments, unresolved exceptions, remediation, and approval status. |
Keep scenario assumptions in a clearly identified control area and retain earlier analyses rather than overwriting them in a way that erases comparisons. Cloud version history, including features ICAEW describes for SharePoint/OneDrive and Google Drive, can help identify or restore prior versions. It does not by itself explain a change, prove correctness, or replace review. Check that any platform’s access controls, record retention, export capability, and connected-data handling fit organizational policies.
What tests should you run before releasing the model?
Test inputs, formulas, and outputs; do not treat a plausible-looking result or an AI explanation as evidence that the workbook works. The exact tests depend on the model’s use and materiality. For a consequential model, retain the exact test inputs, expected results, actual results, exceptions, and their resolution with the release record.
- Check data and refreshes. Confirm that required inputs are complete, that extracts and external links reflect the intended source version, and that transformations are understood and reconciled.
- Independently verify material calculations. Recompute key calculations or benchmark them against a trusted source or a separately constructed check. Confirm that totals, balances, and flags behave as intended.
- Run named scenarios. Record base, upside, downside, and relevant stress assumptions so a reviewer can see which inputs changed and how important outputs moved.
- Check sensitivity and direction. Change material assumptions and confirm that the resulting movement is sensible for the model’s logic. Investigate unexpected or counterintuitive results rather than assuming the AI-generated formula is right.
- Test boundaries and invalid cases. Where relevant, try extreme, negative, missing, or invalid inputs and confirm that the workbook responds safely or clearly flags the condition.
- Record and resolve exceptions. Preserve expected and actual results, document discrepancies, identify corrective changes, and rerun affected tests before approval.
ICAEW recommends testing proportionate to workbook size, complexity, and criticality, alongside peer review, controls, and alerts. Its guidance on testing assumptions in Excel describes scenario analysis as a way to make input changes and their output effects demonstrable.
Who should review, approve, and monitor it?
Assign a named human owner who is accountable for the model’s use and release. Have a suitably capable person who did not create it review material logic and supporting evidence. The preparer, independent reviewer, owner, and approver may be different people depending on the organization and the model’s risk.
- Record reviewer comments, exceptions, remediation, and the approval decision against the released version.
- Define who may change source data, formulas, assumptions, and approved releases.
- Restrict edit access to authorized users and retain prior approved versions under the organization’s record-retention process.
- Reassess the model after material changes to data, business conditions, markets, or logic.
ICAEW’s Financial Modelling Code resource describes retaining a model run and change log to support an audit trail and comparison between versions. For organizations subject to banking supervision, align governance with their applicable obligations; the 2026 interagency guidance discussed above does not formally cover generative AI.
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What is established about AI-generated model errors?
The authoritative and professional sources cited here do not provide a directly applicable named statistic for error rates or auditability of AI-generated financial models. A general AI error figure or an unrelated spreadsheet-error statistic would not establish the risk for a particular workbook. Assess the model’s own logic, inputs, use, and test results instead of relying on an unsupported universal rate.
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