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YES

As of 13 August 2026, AI can flag unusual business expenses.

This still needs a person who signs their name to it.

Can you do it?

5 minutesto a draft.

30 minutesto something you’d act on.

Cost, all in£0

Skill neededchat-fluent

Who has to check ityou

What the alternative costsA human alternative is a finance colleague or accountant reviewing the transactions; no price for that alternative is supplied here.

If this goes wrong, a legitimate expense may be queried unnecessarily or an unusual payment may be missed until it causes a financial or compliance problem.

What to actually do

  1. Hand it to a person

    The route this page recommends

    A person who owns the outcome does this end to end, worth it when the failure is dear.

  2. Use a tool built for this

    Second choice

  3. Do it yourself

    The distant third

    A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

    How to actually do it

    1. Open your accounting or bank-export system and download the relevant expense transactions in spreadsheet or CSV format, including dates, suppliers, descriptions, amounts, categories, cost centres and payment references.
    2. Open the current company expense policy and gather the supplier list, recurring payment list, known one-off purchases and any notes about unusual trading periods.
    3. Remove unrelated personal data and paste the policy, transaction export and business context into the prompt, keeping the column headings intact.
    4. Ask the chatbot to produce a ranked flag list and to state the exact transaction, comparison or rule behind every flag.
    5. Open the original receipts, invoices, approval records and accounting entries for each flagged transaction, then mark each flag as explained, unresolved or incorrectly flagged.
    6. Ask the chatbot to update the list using only your marked explanations, without changing transaction amounts or inventing missing evidence.
    7. Send unresolved high-priority items to the person responsible for finance or your accountant, and record the final action beside each transaction.

    Prompt

    Analyse the business expense transactions below and flag items that are unusual or need human review. Use the available transaction history as the main baseline, and also consider the expense policy and supplier information. Do not accuse anyone of fraud, and do not decide that an expense is allowable for tax or VAT purposes. For each flagged item, provide the transaction date, supplier, amount, category, the comparison or rule that triggered the flag, and the information needed to resolve it. Separate unusual amounts, new or infrequent suppliers, duplicate-looking transactions, weekend or out-of-hours payments, policy exceptions and incomplete descriptions. State when the data is insufficient to judge an item. Rank the flags as high, medium or low priority, but do not invent thresholds or missing facts. Finish with a short list of unflagged limitations and checks I should perform against the original records.

Expense policy:
[PASTE POLICY]

Transaction export, including date, supplier, description, amount, currency, category, employee or cost centre and payment reference:
[PASTE TRANSACTIONS]

Relevant supplier list, recurring payments and known one-off purchases:
[PASTE CONTEXT]

    Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.

What it gets wrong

  • AI cannot know that a large or unfamiliar payment was authorised unless you provide the approval and business context.
  • AI cannot reliably distinguish a legitimate one-off purchase from a control failure using transaction data alone.
  • AI cannot replace checks against original receipts, invoices, approvals and bank records.
  • AI can rank a flag, but it cannot decide whether an expense is allowable for UK tax or VAT purposes.
  • AI may miss an unusual payment when the transaction export is incomplete, inconsistently categorised or too short to establish a useful pattern.

Even on a YES, the friction has a name: judgement under ambiguity, verification cost 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.

AxisScore (0–2)
Output2
Inputs2
Verification1
Liability2
Effort delta2
Total9 / 10

The methodology and its thresholds are published in full.

FAQ

Can AI spot unusual expenses in my business account?
Yes. It can compare transactions with previous patterns, supplier records and your expense policy, then flag unusual amounts, new suppliers, duplicates or missing descriptions. You still need to check each flag against the receipt and approval record.
Can AI detect fraudulent business expenses?
It can identify patterns that deserve investigation, such as duplicate-looking payments or an unfamiliar supplier. It cannot establish fraud from transaction data, so do not treat a flag as an accusation and refer serious or unresolved cases to your finance lead or accountant.
What data do I need to give AI to check my expenses?
Give it a transaction export with dates, suppliers, descriptions, amounts, categories and payment references, plus your expense policy and known recurring or one-off payments. Remove unnecessary personal data and keep the original records available for checking.
Can AI decide whether a business expense is tax deductible?
It can organise transactions and identify items that need review, but it should not make the final tax or VAT decision. This is not professional advice, and a serious or uncertain case needs an accountant or other qualified tax professional.

Nearby answers

All Expenses rulings

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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