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 ja 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.
FAQ
How does AI detect fake invoices?
By pattern violation: changed IBAN for a known supplier, amount or frequency different from history, duplicate, first-time supplier, unusual timing. The exception will be presented to the person with a justification.
What is IBAN exchange fraud?
The fraudster pretends to be your supplier and reports a 'new' bank account. Protection: account changes are always verified through another channel — call the supplier's known number, not the one on the bill.
Is AI enough to combat invoice fraud?
No. AI reduces exceptions, but three human rules remain: second-channel verification of account changes, separation of approval and payment rights, and amount limits with a second approver.
What is the strongest fraud signal?
A bank account changed at the expense of a familiar supplier — the most dangerous and most easily detected by the machine, because all previous IBAN history serves as a reference base.