Invoice Fraud Detection

The challenge

A company in supply chain and procurement paid its supplier invoices largely on trust. Verification meant manual spot checks: pick a few invoices, dig up the matching orders and delivery notes, compare them line by line.

At thousands of invoices, checking everything by hand was not feasible. Some suppliers knew that.

The request was "catch bad invoices." Looking at the process reframed it. Verification was not a detection problem — it was a reconciliation problem. Every invoice should match an order and a delivery note. If the three documents agree, the invoice is fine. If they don't, someone should look.

The missing piece wasn't judgment. It was a structured way to compare 10,000+ documents that existed only as PDFs and photos.

What we built

A document reconciliation pipeline in three parts:

• OCR plus LLM extraction reads invoices, orders, and delivery notes into structured records.
• A matching engine reconciles each invoice against its order and its delivery note.
• An anomaly dashboard surfaces every mismatch — quantity, price, missing delivery — for the finance team.

100% of invoices are now processed automatically. Not a sample — every document, checked against its order and its delivery note, around the clock.

Nothing is blocked automatically: the system flags, people judge. Finance reviews the exceptions and decides.

The results

The system found real fraud in production. Invoices that had been paid on trust did not survive reconciliation. People can't cross-check thousands of documents; software can, and it doesn't get tired on document 9,000.

A document archive that was unmanageable by hand became a structured database. Verification went from spot checks to full coverage.

What you get

Up to two document types and one reconciliation flow, with the anomaly dashboard your finance team works from. Process audit first, scope in writing, and 30 days of support.

Paying invoices on trust? Start with the free AI Audit, or book a call.