As of 13 August 2026, AI can only partly detect fraudulent expense claims.
Most people should hand this to a purpose-built tool.
Can you do it?
15 minutesto a draft.
1 hourto something you’d act on.
Cost, all in£0
Skill neededpower-user
Who has to check ita colleague
What the alternative costsRows is an AI spreadsheet with built-in AI analysis and live data connections, but no alternative price is supplied here.
If this goes wrong, a genuine claim may be treated as misconduct or a fraudulent claim may be paid and the evidence may be mishandled.
What to actually do
Use a tool built for this
The route this page recommends
Hand it to a person
Second choiceA person who owns the outcome does this end to end, worth it when the failure is dear.
Do it yourself
The distant thirdA chat interface, power-user skill, and roughly 1 hour until you can act on the result.
How to actually do it
- Open the current expense policy and export the expense report, payment or card transactions, and receipt files for the review period.
- Remove unnecessary personal data and assign a claim ID to each expense, keeping a separate secure copy that links the ID to the employee and original documents.
- Paste the policy text and the claim data into a spreadsheet or AI analysis tool, then add the receipt fields, transaction references and any approved comparison period.
- Paste the prompt above and ask the model to produce flags, evidence, policy references and the next record needed for each review.
- Open every original receipt and payment record for a flagged claim and compare the merchant, date, amount, currency, VAT details and claimant against the model's cited evidence.
- Ask the claimant or expense owner for missing explanations and records, then record the response and supporting evidence beside each claim.
- Have a manager or finance reviewer decide whether each flag is an error, an allowable exception, or evidence requiring a formal investigation, without treating the AI output as proof of fraud.
Prompt
Analyse the expense claims and supporting documents below for signs that need human investigation. This is a UK workplace review, not a finding of fraud and not professional advice. Do not accuse anyone or infer intent. Flag only observable issues such as duplicate receipts, repeated claims for the same transaction, totals that do not match receipts, dates outside the permitted period, expenses outside the policy, unusual rounding, missing evidence, altered-looking documents, conflicting merchant or payment details, or patterns that differ materially from the comparison data. For each flag, give the claim ID, the exact evidence, the relevant policy rule, a confidence level of low, medium or high, and the next record a reviewer should obtain. Separate confirmed data mismatches from unusual but potentially legitimate claims. Do not invent missing facts, totals, policy rules or explanations. Do not recommend disciplinary action. End with a table of claims requiring review and a separate list of claims with no visible issue. Data and policy: [PASTE EXPENSE DATA, RECEIPTS OR EXTRACTED RECEIPT FIELDS, COMPARISON DATA AND CURRENT EXPENSE POLICY HERE].
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
What it gets wrong
- AI cannot know whether a genuine exception was agreed verbally or whether a claimant had a legitimate business reason that is absent from the data.
- AI cannot establish intent, which is central to calling a claim fraudulent.
- AI cannot reliably identify altered or counterfeit documents without specialist forensic checks and original-source evidence.
- AI cannot take responsibility for suspending payment, starting disciplinary action or reporting suspected fraud.
- Sensitive receipts and employee records still need controlled access and a lawful workplace process.
What caps this at PARTLY: judgement under ambiguity, private data access and stakes of error.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 2 |
| Inputs | 1 |
| Verification | 1 |
| Liability | 1 |
| Effort delta | 2 |
| Total | 7 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can AI spot fraudulent expenses?
- Partly. It can flag duplicate receipts, mismatched amounts, policy breaches and unusual patterns, but it cannot prove intent or decide that a claim is fraudulent. A finance reviewer must check the original records and investigate the explanation.
- Can AI check expense receipts for fraud?
- It can compare receipt details with expense reports, payment records and your policy, and highlight inconsistencies. It cannot reliably authenticate every document or know whether an unusual claim has an authorised exception.
- Can I use ChatGPT to investigate expense fraud?
- You can use it to organise evidence and create a review list, provided your organisation permits the data handling and you minimise personal data. Do not treat its output as proof or use it alone for disciplinary action. This is not professional advice; a serious case needs your finance lead, HR and, where appropriate, a solicitor or forensic accountant.
- What should I do if AI flags a fraudulent expense claim?
- Preserve the original claim, receipt, transaction record and audit trail, then have a finance or HR reviewer check the evidence and ask the claimant for an explanation. Do not accuse the employee solely because a model produced a flag, and do not let the model make the disciplinary or reporting decision.
Nearby answers
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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