When people talk about AI in finance, it often sounds like a future thing. It isn’t. AI is already embedded in invoice processing workflows at businesses of all sizes — most of them don’t even think of it as AI.
The most common use is data extraction. When a PDF or scanned invoice arrives, AI reads the document and identifies the relevant fields: supplier name, invoice number, date, line items, amounts and tax rates, without needing the document to follow a specific template, even when formatting varies between suppliers.
OCR on its own reads characters from an image. AI takes that further by understanding what those characters mean in context. It can tell the difference between an invoice number and a purchase order reference, even when they appear in similar positions on the document. Accuracy improves over time as the system processes more documents.
After AI extracts the raw data, a rules engine applies your business logic to map that data to your accounting structure. It can match a supplier name to your vendor list, map a product code to a general ledger account and apply cost center rules based on what was purchased. These rules are configured once and applied automatically to every invoice.
AI does not replace human judgment on unusual invoices. If a supplier charges for something unexpected or an amount is significantly higher than normal, that still needs a person to look at it. Good invoice automation flags those cases and routes them for review rather than processing them automatically.
When a PDF or scanned invoice arrives, Digitizer sends it to an OCR engine which reads the document and returns the fields: invoice number, date, amounts, line items, tax, and vendor details. From there, a configurable expert system takes over. It matches the supplier name to the vendor list, maps units of measure and product identifiers, and turns the raw OCR output into structured fields Unimaze can act on, using rules the customer sets up per vendor, not a black box. Anything that doesn’t match cleanly is held for review rather than guessed at. Every step is recorded in a processing trace, so you can see exactly what the OCR engine read, what the expert system did with it, and what it produced, even months later, during an audit. The result feeds straight into the approval workflow.
Not everything downstream of Digitizer is AI. Approver runs the sign-off process: department review, finance sign-off, a CFO threshold for larger amounts. Using rules a customer sets up once: auto-approve a recurring vendor under a set amount, escalate if nobody responds in time. That’s automation, not AI. No model is making a judgment call; it’s applying the rule exactly as written, every time. UnimazeGo, the mobile expense app, works the same way. An employee photographs a receipt and fills in the expense details by hand, the app doesn’t read the receipt automatically. Once submitted, the expense goes through the same rules-based pipeline as any other invoice: Booking Rules assigns the accounting codes, Approver routes it for sign-off. The AI in Unimaze’s invoice processing lives specifically in Digitizer, the step that turns a PDF or a photo into structured data. Everything after that runs on rules, configured once and applied consistently every time.
