Stop Cleaning Up After AI: An Excel Checklist to Catch Hallucinations Before They Break Your Ledger

Stop Cleaning Up After AI: An Excel Checklist to Catch Hallucinations Before They Break Your Ledger

Hook: You trusted AI to speed up month-end entries — now you’re chasing phantom transactions, wrong VAT rates and supplier names that never existed. If your spreadsheets are the last line before the ledger, a single AI hallucination can cost time, reputation and compliance. This step-by-step Excel checklist — plus a ready-made workbook with built-in validation rules and conditional formatting — helps you catch likely AI-generated errors before they migrate into your live books.

Why this matters in 2026

AI adoption in back-office workflows exploded across 2024–2025. With powerful copilots embedded into Excel, Power Query and macros, teams saved hours — but also started reporting suspicious, fabricated values that slipped through traditional checks. Late‑2025 guidance from industry analysts emphasised stronger human oversight and structured validation for AI-assisted outputs. In short: AI is great for execution, but organisations that treat AI outputs as system inputs without controls are paying the cleanup tax.

“Treat AI like a junior analyst with read-only access until you build proven checks and an audit trail.”

What this checklist does (fast)

• Pre-ingest schema checks to stop bad rows arriving from AI scripts or Copilot suggestions.
• Sanity and statistical checks to flag outliers and improbable values.
• Text-pattern and provenance checks to catch hallucinated supplier names and placeholder text.
• Cross-sheet reconciliation so totals and ledgers never diverge silently.
• Audit trail and governance to show who approved or changed what and when.

How to use this guide

Follow the checklist in order. Most steps are simple Excel formulas or conditional formatting rules; a few require Power Query or a tiny macro to record change history. If you prefer, download the companion workbook that includes each rule pre-built and a dashboard showing flags in one glance.

Step-by-step checklist and implementation

1. Pre-ingest: Enforce a strict schema

Before AI or any external tool writes to your working tables, gate inputs through a staging sheet. Use Data Validation and Power Query type checks so rows that don’t match the expected schema are quarantined.

1. Create a Staging sheet with the same columns as your live table plus a Source column (manual, API, AI).
2. Apply Data Validation for each column: dates, numbers, currency codes, and pick-lists for suppliers and GL codes.

Example: for Invoice Date select Data > Data Validation > Allow: Date. For Supplier use a drop-down that references your master supplier list (use UNIQUE if dynamic). If you are automating imports or looking at invoice automation for budget operations, treat the staging sheet as your enforced gate.

2. Column-level validation rules (quick wins)

Use simple formulas and helper columns to create pass/fail flags.

• Numeric type enforcement: =IFERROR(--[@Amount],"TYPE_ERROR") — use ISNUMBER() or VALUE() depending on layout.
• Currency & VAT checks: Ensure VAT is in plausible range: =IF(AND([@VAT]>=0,[@VAT]<=[@Amount]*0.25),"OK","VAT_SUSPECT"). Link this to your tax rules; see examples in small‑business tax automation for common compliance checks.
• Date windows: Flag dates outside a rolling 2-year window: =IF(AND([@Date]>TODAY()-730,[@Date]<=TODAY()+30),"OK","DATE_SUSPECT")
• Duplicate invoice numbers: Use COUNTIFS on Invoice No + Supplier: =IF(COUNTIFS(InvoiceNoRange,[@InvoiceNo],SupplierRange,[@Supplier])>1,"DUP","OK")

3. Conditional formatting rules that scream “Check me”

Conditional formatting gives immediate visual cues. Apply these rules to your staging and ledger sheets.

• Outliers vs rolling average (3 sigma): Use a formula rule that computes z-score using AVERAGE and STDEV.P across the last N rows. If ABS(([@Amount]-avg)/stdev)>3 flag red.
• Too many decimal places: =LEN(TEXT([@Amount],"0.0000"))-LEN(INT([@Amount]))>3 — flags high-precision numbers where ledger expects whole pence.
• Text in numeric cells: =ISTEXT([@Amount]) — highlight in orange.
• Placeholder words: Search for "approx|estimate|unknown|TBD|N/A" with a REGEXMATCH (Office365) or SEARCH: =SUM(--ISNUMBER(SEARCH({"approx","estimate","TBD","N/A"},[@Notes])))>0

4. Detect likely hallucinations with pattern and novelty checks

AI hallucinations often produce plausible but novel items: supplier names that look realistic but are new, account codes that don't exist, or line items with mixed units. Use these strategies:

• Supplier novelty: Flag supplier names not in the master list: =IF(ISNA(MATCH([@Supplier],SupplierList,0)),"NEW_SUPPLIER","OK"). For processes that vet suppliers automatically, tie this into your supplier master workflow and retention policies as described in broader regulation & compliance guidance.
• Name similarity check: Use a fuzzy match via Power Query or the new Excel TEXTSPLIT + LET method to compute Levenshtein-like distance (or use Fuzzy Merge in Power Query) — highlight near-matches to catch slightly misspelled real suppliers. See examples of data‑first fuzzy tooling in edge AI and platform tooling.
• GL code validation: =IF(ISERROR(XLOOKUP([@GLCode],GLCodesRange,GLCodesRange,NA())),"GL_UNKNOWN","OK")

5. Statistical sanity checks

AI often invents amounts that don’t align with seasonality or historical ranges. Build rolling comparisons:

• Rolling monthly average: =AVERAGEIFS(AmountRange,MonthRange,ThisMonth,YearRange,ThisYear) and compare new rows to the average plus a tolerance factor.
• Median absolute deviation (MAD) robust outlier check for skewed distributions.
• Proportion checks — e.g., VAT/Net ratio must be near a standard rate: =ABS([@VAT]/[@Net]-0.20)<0.02 — otherwise flag.

6. Cross-sheet reconciliation and automated reconciles

Add reconciliation formulas that automatically verify totals before posting.

• Compare staging total to expected import batch total: =IF(SUM(StagingAmountRange)=ExpectedImport,"IMPORT_OK","TOTAL_MISMATCH")
• Use XLOOKUP to assert that every invoice in staging exists or is intended to be new: =IF(ISNA(XLOOKUP([@InvoiceNo],LedgerInvoiceNos,LedgerInvoiceNos)),"POT_NEW","EXISTS")
• End-of-period control totals: create a pivot or SUMIFS summary in the dashboard and require human sign-off if flags > 0.

7. Use Power Query as your first filter

Power Query is your best friend for pre-processing AI-generated tables. Import AI outputs into Power Query and:

1. Set Strict Data Types for each column (Date, Decimal Number, Text).
2. Replace errors and nulls with identifiable tokens like "_MISSING_" so you can filter or flag them downstream.
3. Use Fuzzy Merge to compare supplier names against master data and produce a match score column. Filter any matches below a threshold (for review).
4. Append a Source column and a TimeStamp via M code: DateTime.LocalNow() so every row arriving has provenance.

Example M snippet to add a timestamp and source column:

= Table.AddColumn(#"PreviousStep", "ImportedAt", each DateTime.LocalNow(), type datetime)
= Table.AddColumn(#"PreviousStep", "Source", each "AI", type text)

8. Build an approval workflow and audit trail

You need a simple, enforceable workflow: Staging (AI) → Review (human) → Post (ledger). Enforce this with a mandatory Status column (Pending, Approved, Rejected) and a change log that records user, datetime and reason.

Two options for audit trails:

• Lightweight VBA: Use Worksheet_Change to append change metadata to a hidden ChangeLog sheet. This records who changed Status to Approved and when.
• Enterprise: Use Microsoft 365 Version History and SharePoint flows to require approval before writing to the live workbook.

VBA example (append to ChangeLog):

Private Sub Worksheet_Change(ByVal Target As Range)
  Dim rng As Range
  Set rng = Intersect(Target, Me.Range("StatusColumn"))
  If rng Is Nothing Then Exit Sub
  Application.EnableEvents = False
  Dim c As Range
  For Each c In rng
    If c.Value = "Approved" Then
      Sheets("ChangeLog").Range("A" & Rows.Count).End(xlUp).Offset(1,0).Value = Now
      Sheets("ChangeLog").Range("B" & Rows.Count).End(xlUp).Offset(1,0).Value = Environ("USERNAME")
      Sheets("ChangeLog").Range("C" & Rows.Count).End(xlUp).Offset(1,0).Value = "Approved invoice " & c.Offset(0,-1).Value
    End If
  Next c
  Application.EnableEvents = True
End Sub

9. Create a dashboard for flags and triage

Use a PivotTable or dynamic arrays to show counts of flags by type, top offenders (by supplier or user), and latest pending approvals. Colour-code the dashboard so reviewers can prioritise high-risk items first (large amounts, new suppliers, or failing multiple checks).

10. Governance: rules, SLAs and logging

Document and enforce:

• Which AI sources are authorised to write to staging (Copilot, internal API, third-party tool).
• Required validation rules that must pass before posts to ledger.
• SLA for human review (e.g., 24 hours for critical flags, 72 hours for low-risk).
• Retention policy for ChangeLog and archived staging files for audit (recommended: 7 years for financial records in the UK).

Advanced strategies and 2026 trends

As of 2026, organisations are combining spreadsheet controls with broader AI oversight:

• Model provenance tags: Tools increasingly add model identifiers to AI outputs (model name, version, prompt hash). Ingest these into your Source/Model columns so you can correlate hallucination rates with particular models or prompts. For approaches to provenance and auditability see provenance & compliance examples.
• Automated model health metrics: Monitor error rates from AI sources. If a model’s hallucination rate exceeds your threshold, block it from writing to staging until retrained or reconfigured — treat this like any other monitoring and reliability pipeline.
• Regulatory alignment: Recent guidance (late 2025) from industry bodies emphasises explainability for automated decision systems — keep logs and prompt copies to support audits. See additional compliance thinking in regulation & compliance for specialty platforms.

Rule-of-thumb thresholds you can start with

• Flag amounts > 3x monthly rolling average.
• Flag new suppliers until manually vetted.
• Flag any row where 2 or more validation rules fail.
• Require human sign-off for any post > £5,000 (configurable).

Real-world example (short case study)

We worked with a UK SME finance team in late 2025. After adding a staging sheet, Power Query fuzzy supplier matching and a 3-sigma conditional formatting rule, the team reduced time spent on post-import clean-up by 72% in the first month. Most importantly, they caught a batch of AI-inserted duplicate invoices that would have created a £45k overstatement. The audit log and model provenance tags allowed them to trace the issue to an internal script using an outdated prompt — the script was paused, the prompt revised and the model whitelisted only after re-testing.

Checklist summary — print-and-use

1. Create Staging with Source and Status fields.
2. Apply Data Validation (types and pick-lists).
3. Import AI outputs into Power Query; enforce types and add timestamp/source.
4. Run fuzzy match on suppliers and flag low scores.
5. Apply conditional formatting for outliers, decimals and placeholders.
6. Run cross-sheet reconciliations and control totals.
7. Require human approval before posting; log approvals to ChangeLog.
8. Maintain SLAs, retention and model blacklist/whitelist.
9. Monitor AI model error rates and block sources above thresholds.
10. Keep prompts and provenance for audits.

Common questions

Can I detect AI output automatically?

Not 100% reliably. But you can build high-probability heuristics: novelty (new supplier names), improbable numerical patterns, placeholders and mismatch between declared source and expected format. Combining these heuristics with human-in-the-loop review gives strong protection; see techniques for building resilient workflows in resilient transaction flows.

Are these checks expensive to run?

No. Most are built from native Excel features and Power Query. The only maintenance cost is keeping master lists (suppliers, GL codes) up to date and reviewing flagged items.

Download the workbook

The companion workbook includes:

• Staging sheet with Data Validation and sample inputs.
• Power Query import with sample M code and fuzzy match setup.
• Conditional formatting templates for outliers, decimals, placeholders.
• ChangeLog macro and a dashboard with flag summaries.

Install, connect your master lists and customise thresholds for your business rules. If you’re automating invoice ingestion into ERP or budget tools, pair this with the approaches in invoice automation for budget operations.

Final takeaways

AI is a productivity multiplier — not a replacement for controls. By inserting a simple gating layer (staging + validation + human approval), you preserve speed while preventing hallucinations from corrupting financial records. In 2026, smart teams pair AI with robust spreadsheet governance: schema checks, Power Query pre-processing, conditional formatting, reconciliation logic and an auditable approval path.

Call to action

Want the ready‑to‑use workbook and a short walkthrough webinar? Download the template and subscribe for fortnightly Excel governance tips tailored to UK businesses. Stop cleaning up after AI — start preventing problems at source.

Related Reading

• Invoice Automation for Budget Operations: Advanced Strategies for 2026
• The Evolution of Small-Business Tax Automation in 2026
• Building Resilient Transaction Flows for 2026
• Regulation & Compliance for Specialty Platforms: Data Rules, Proxies, and Local Archives (2026)
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References & further reading:

• ZDNet, "6 ways to stop cleaning up after AI" — on balancing AI productivity with oversight (Jan 2026).
• MarTech coverage, "B2B marketers trust AI for execution but not strategy" — on how teams use AI for tactical work and still require human strategy oversight (Jan 2026).