You don't need to understand AI to use Invixa. Every invoice gets checked against known fraud patterns, your supplier history, and your wider network of suppliers — and you get a plain answer back: approve it, double-check it, or stop it. No spreadsheets, no guesswork.
Behind the scenes there's a lot going on. From where you sit, it looks like this.
These are the same red flags an experienced fraud investigator would look for — we just check them automatically, every time, without anyone getting tired or distracted by the 400th invoice of the month.
The same invoice number has shown up before — sometimes from a different supplier entirely, which is a classic sign of a cloned invoice.
The invoice is far larger than what this supplier usually charges — exactly the kind of jump a busy approver is most likely to miss.
A supplier with no history, or one that's been set up but never actually billed you before — a common pattern for fake companies.
Address or contact details that match patterns we've seen used by fake or shell companies in the past.
Invoice dates that are in the future, or unusually old — both worth a second look before anyone gets paid.
A well-established statistical test used by forensic accountants for decades, which can reveal when invoice amounts have been made up rather than genuinely billed.
Every business is different — what's a normal invoice amount for one supplier might be a glaring red flag for another. Our system builds up a picture of your normal patterns and spots when something doesn't fit, the same way an experienced member of your finance team would after a few years on the job.
We're upfront that this is still early days — we're not going to claim a made-up accuracy percentage. What we can tell you is that every decision your team makes helps the system get sharper for your business specifically.
You'll never get a mystery score with no explanation. Every result is broken down into three simple parts, written for a person — not a data scientist.
One plain-English sentence that tells you exactly what was found.
The specific reasons behind the result — fully traceable, never just "trust the algorithm."
One clear recommended action, so your team always knows what happens next.
When something needs a closer look, your team gets a Slack message or an email with the full picture already laid out. Approve, reject, or escalate — with a reason — and that decision is saved automatically, building a clean record you can hand to your accountant whenever you need one.
Most invoice-fraud tools are built and priced for large enterprise finance departments. Few explain their decisions in plain language, and fewer still defend against fraudsters who target the AI itself.
| What matters | Invixa | Xelix | Yooz | Medius | AppZen | Stampli | Resistant AI |
|---|---|---|---|---|---|---|---|
| Catches invoice fraud | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Explains every decision in plain English | ✓ | — | — | — | — | — | — |
| Defends against attacks on its own AI | ✓ | — | — | — | — | — | — |
| Keeps a tamper-proof record automatically | ✓ | ✓ | — | ✓ | — | — | — |
| Built & priced for small businessesvs. enterprise-only | ✓ | — | — | — | — | — | — |
Comparison based on publicly available information as of 2026 and our own research. We'd rather you double-check this yourself than take our word for it.
Enterprise-grade checking, without the enterprise price tag — built so it's affordable to run on every invoice you receive, not just the big ones.
Book a 20-minute call — we'll check a real invoice and a deliberately fake one, side by side, so you can see exactly how it thinks.