Skip to main content
Human in the loop AI means a named person controls or reviews defined workflow points before AI work becomes an accepted financial output or action. In finance, people set permissions, approve consequential steps, check results, and handle exceptions.

​
Where does the human belong in the workflow?

Place human control before, during, and after the work. Give reviewers authority to reject an output, request corrections, or stop an action.
  • Before: define the task, source systems, and allowed actions. Permit reading an accounts payable export while withholding payment-release access.
  • During: require approval before consequential actions. Hold a proposed journal entry for review before posting.
  • After: accept a specific output version. Check reconciliation differences against source records before signing off.
  • At exceptions: route missing evidence and unexpected results to a named owner. Pause the affected step until that person resolves the issue.
NIST’s voluntary AI Risk Management Framework calls for clear human oversight roles and accountability structures. Turn those roles into decisions people can actually make. A useful review has three parts: evidence, authority, and a recorded decision. Assigning someone to watch the workflow without these parts leaves the responsibility unclear.
Month-end close checkpoints: the owner allows approved exports and drafts only; the agent drafts the pack, flags gaps and proposes adjustments; posting waits for controller approval, rejection or corrections; the controller reviews evidence and signs off the exact version. Athena limits access to the user’s rights, denies other access, stops at a spending or model-call cap, waits for tool approval, and logs steps, a run summary, the approver and reviewed version. At any step, missing evidence pauses affected work, gaps are logged and a named owner is asked for help; work resumes when required evidence arrives.

​
What are human in the loop AI examples in finance?

Month-end close. AI drafts a close pack from approved exports. The preparer checks completeness, and the controller reviews adjustments before approving the pack. Reconciliations. AI proposes matches between bank and ledger transactions. A reviewer checks exceptions against source records and accepts the reconciliation version used for close. Accounts payable. AI compares invoice fields with purchase orders and receipts. An accounts payable reviewer resolves mismatches, while existing approvals govern bank-detail changes and payment release. Forecasting. AI drafts a forecast from approved actuals and assumptions. The financial planning team checks changed formulas and assumptions before circulation. For each example, define what the reviewer must inspect. A forecast review might show each changed exchange rate, its source, and the affected cells. Keep proposed work separate from accepted work. Use formulas for fixed calculations and AI for interpretation, with reproducible calculations available for review.

​
How much autonomy should you grant, and when?

Grant autonomy per action. Reading an export and releasing a payment have different consequences, even when they belong to the same workflow. Start with draft-only work. Expand permissions after representative tests show that reviewers can detect errors and resolve exceptions.
  • Prepare a draft: allow automatic preparation from approved data, followed by human acceptance.
  • Edit a forecast: limit changes to agreed ranges and review before circulation.
  • Make an accounting judgment: require a named expert to check the evidence and rationale.
  • Post, pay, or send externally: require approval before execution, with the action details available to the approver.
Test ordinary cases alongside missing documents and conflicting records. Check whether the reviewer spots a known error and can correct the affected output. Use observed results to decide whether permissions should expand. Record who authorized the change and which procedure version it applies to.

​
How do you set up human in the loop AI workflows?

  1. Choose one output. Name its owner and define acceptance criteria. Start with a forecast or reconciliation your team already understands.
  2. Limit access. Specify permitted reads and writes. Test an action that should be prohibited.
  3. Place approval gates. Name a reviewer and backup. Show them the proposed action before they authorize it.
  4. Define exceptions. Set pause conditions, exception owners, and the evidence needed to resume.
  5. Test review and recovery. Check reporting periods and completeness. Insert a known error and ask the reviewer to find and reverse it.
  6. Expand deliberately. Examine completed runs before changing permissions. Record the new scope and procedure version.
Keep a decision record that another reviewer can follow:
  • Run identifier, time, human operator, and agent identity.
  • Procedure version, model configuration, and permitted scope.
  • Source records, versions, and extraction dates.
  • Proposed changes, before-and-after values, and executed actions.
  • Approver identity, decision, and exact output version reviewed.
  • Exceptions, corrections, stop events, and final outcome.
Control access to this evidence and agree retention with your recordkeeping owner. Make the accepted version easy to find alongside its approval.

​
How do ChatGPT, Copilot, Gemini and Athena compare?

Compare one task: update forecast assumptions, inspect changes, and recover an error. The table covers OpenAI’s ChatGPT Business uploaded-file analysis, Microsoft’s Copilot in Excel, Google’s Gemini in Google Sheets, and edits in Athena Sheets.
Human review in finance AI, compared by question. Where is the output? Chat assistant: Analysis and files in chat; In-app copilot: Workbook edits or sheet action previews; Athena: Edits in Athena Sheets. What can we review? Chat assistant: Inspect code and outputs; In-app copilot: Review approach or action preview; Athena: Named approval at configured steps. How do we fix an error? Chat assistant: Review calculations and assumptions; In-app copilot: Undo or restore a version; Athena: Undo just one range. What should we check? Chat assistant: Records and assumptions; In-app copilot: Records and assumptions; Athena: Records and assumptions.
OpenAI’s ChatGPT exposes generated code, outputs, and assumptions for review. It also offers Excel and Google Sheets integrations beyond the uploaded-file workflow compared here. Microsoft’s Copilot plan confirmation and Google’s Gemini action preview provide checkpoints before edits. Record the finance approver’s sign-off against the accepted output version. Athena combines configured procedure approvals with review of saved changes and approver and version records. Its difference is how these controls work together around a recurring procedure. For every product, preserve the sources and name the finance reviewer. Test recovery after a later human edit and check approval bypass prevention when a separate approver is required.

​
How does Athena keep people in control?

Use Athena’s Agent Operating Procedure editor for recurring work. Procedures are versioned and can branch by entity, region, or account. Start with sample data and follow this sequence:
  1. Describe the procedure. Enter its tools, approved sources, and output format in Athena’s workflow editor.
  2. Set the gates. Choose a trigger. Check that your workspace’s tool rules make posting, payment, and external-send tools ask for approval, so the run waits until a reviewer approves or declines the proposed action.
  3. Set the budget. When you start runs through the API, add a per-run run_budget with a dollar or model-call cap. At the cap, Athena stops the run and marks it as not succeeded. Start a new run with a larger budget to continue.
  4. Inspect the changes. Athena’s Activity Monitor shows affected ranges, human or agent authors, before-and-after values, and triggering session messages.
  5. Correct the affected range. In Athena, compare before and after side by side, then roll back selected cells while keeping the other changes.
Athena’s work-product history links the agent, sources, approver, and reviewed version. Use that record to connect a person’s decision with the work they accepted.

​
What to plan for

Give reviewers time and source access. If an accepted version changes, repeat the affected checks before circulation. An activity log cannot prove a number correct. PCAOB staff reported concerns about source reliability and generated content’s auditability in July 2024. Microsoft advises against using Copilot for decisions in sensitive areas such as finance. Google says Gemini features should not be relied on as financial advice. Athena history records each save, rather than each keystroke. When a human and agent edit the same cell within one save, the last edit wins. Google says Gemini’s last action can be undone until further edits occur. Restoring a Google Sheets version clears earlier cell edit history; copy the file if you need that evidence. Some external actions, including sent emails and committed accounting-system rows, cannot be reversed through Athena. Approve them first and confirm recovery in the destination system. Before external writes, test stopping a run. Check who can intervene and what happens to queued actions or work already underway. A spending cap limits consumption, not accounting errors. Review controls also do not determine legal liability.

​
Questions teams ask

A named person controls or reviews defined points before AI work becomes an accepted financial output. The procedure identifies permissions, approvals, and who accepts the result.
Examples include reviewing reconciliation matches, approving journal entries before posting, and checking forecast assumptions before circulation. Each needs evidence and a recorded decision.
Assign an owner and keep a record that lets another reviewer reconstruct the work. Include inputs, changes, the human decision, and the accepted version.
Define the output and permissions first. Add approvals and exception routing, then test review and recovery before expanding autonomy.
Microsoft’s Copilot in Excel can edit a forecast workbook. Plan mode lets a user confirm the approach before editing.
Athena combines configured approval steps with attribution of saved changes and rollback of selected ranges. Reviewers can inspect before-and-after values while keeping unrelated changes.
See it work