The process starts with the incoming invoice, usually as a PDF via email or an upload portal. AI: specifically a combination of OCR (optical character recognition) and a language model: reads the invoice and extracts the relevant data: supplier name, invoice number, date, due date, amounts, VAT percentages and line descriptions. That structured data is then forwarded to the accounting or ERP system for further processing.

Multiple approaches exist for extracting data from PDF invoices. Specialist tools like Mindee, Rossum or DocuWare combine OCR with trained models for document processing. Large language models such as GPT-4 or Claude can also be used to interpret text from invoices, but are less efficient for this specific use than specialist tools. In practice, many implementations choose a specialist extraction tool plus a workflow tool for further processing and connection.

The output of the extraction step must be connected to an accounting or ERP system. Most business software packages: Exact, AFAS, Twinfield, SAP, QuickBooks: offer API access for creating invoice lines or journal entries. Via a workflow tool or direct API integration, the extracted data is automatically entered. Depending on your setup, that can be fully automatic or include an approval step for an employee.

Automatic processing is never one hundred percent error-free. Invoices with unusual formatting, handwritten notes or poor scan quality can be misread. Always build in a verification step: a dashboard where an employee can review and correct exceptions. Set thresholds for amounts above a certain level that always require manual approval. Transparency about what the system has done and why is essential for a healthy control environment.

An additional benefit of automated invoice processing is the ability to systematically check for duplicates: invoices with the same invoice number or the same combination of supplier, amount and date. Anomalous patterns can also be flagged, such as invoices from unknown suppliers or amounts far outside the expected range. This adds a layer of control that is easily overlooked in manual processing.

Beyond bookkeeping, you can automatically ensure structured archiving. Invoices are stored with metadata: supplier, date, amount, category: making them easy to search. Combine that with an AI interface that can answer questions about invoices ("What did we spend this year with supplier X?") and you have a searchable financial archive that saves hours of searching.

Automatic invoice processing works well for standardised invoices from regular suppliers. For highly variable invoice formats or exceptional invoice types, human review remains necessary. At Mach8, we set up these kinds of systems with an honest picture of what can be automated and what cannot: and we always ensure a working fallback.

AI-driven invoice processing saves considerable time and reduces manual errors. With the right technology, a good verification step and connection to your accounting package, it is a reliable solution. Want to automate invoice processing in your organisation? Get in touch with Mach8 for a concrete approach.