Invoice fraud: how does AI detect fake invoices?

Invoice fraud is successful when the fake invoice looks like the real thing – and that’s where the machine has an advantage, because it doesn’t look at the appearance, but at the pattern. There are four common schemes: a fake supplier invoice for a non-existent service, IBAN swapping, where the fraudster pretends to be a supplier you know and reports a „new” bank account, an almost identical duplicate invoice with a small change, and an urgent transfer order in the name of the manager. None of these are new; what’s new is that automated billing sees the background of each invoice (supplier history, accounts, amounts, rhythm) and the broken pattern pops up before payment, not during an audit.

What signals does the machine capture?

The five strongest. Changed bank account: a familiar supplier, but the IBAN is different from all previous invoices – the most dangerous and most easily identifiable signal. An amount or frequency that is different from history: a supplier who bills around a thousand euros once a month suddenly sends two invoices a week. Duplicate: the same amount, date and reference as an invoice that has already been processed. First-time supplier: not suspicious in itself, but always worth a person’s attention. And timing outside the pattern – an invoice on a Friday night before a holiday is a classic for a reason. Each signal means the same thing: the invoice does not move forward, but comes to the person with a justification, why he was highlighted.

Is AI enough for protection?

No. Three procedures that no tool can replace. Account change verification via another channel: if the supplier „reports” a new IBAN, call them on the number you already know; not the one on the invoice. Separation of rights: the one who approves the invoice should not be the only one who releases the payment; four eyes is an old rule for a reason. And limits: the amount above which payments always require a second approver. AI makes these procedures less frequent: there are fewer exceptions, but not unnecessary. The technical prerequisite for seeing a pattern is a healthy dataset: folded bank and posted invoices is the history against which each new invoice is checked.

What to do tomorrow morning?

Three steps, free of charge. Agree that bank account changes will always be verified through another channel. Review who can release payments on their own. And if your billing is already automatic, turn on exception marking: there's a pattern, use it.