Document Processing with AI: Controls for Accuracy and Review

The processing pipeline

1. Intake: email attachments, scans, uploads and supplier portals.
2. Preparation: image quality checks, rotation, and splitting files that contain several documents.
3. Classification: invoice, credit note, delivery receipt, purchase order, identification document or contract.
4. Extraction: prebuilt models for common documents, custom models for your own forms, or language models for free text.
5. Validation: business rules and cross-checks against master data.
6. Human review: for low confidence or failed validation.
7. Export: to ERP, accounting or case management systems.
8. Archive: the original document linked to the extracted record.

Match the approach to the document

Confidence thresholds

Extraction services return a confidence score for each field. Use pilot data to set thresholds per field: an invoice total or bank account needs a stricter threshold than a reference note. Fields below the threshold go to review. Even when everything passes, audit a sample of automatically processed documents regularly.

Validation rules catch what confidence misses

• Line items plus tax equal the invoice total.
• Tax amounts match the applicable rate for the transaction.
• The supplier exists in the vendor master, and its tax identification number matches.
• The invoice matches an approved purchase order and, where used, a goods receipt.
• Dates are plausible and the currency is expected.
• The same supplier and invoice number have not been processed before.
• Bank details match the supplier record; any difference is flagged, never applied automatically.

Design the human review step

• Show the document next to the extracted fields, with each field highlighted where it was found.
• Explain why the item was flagged: low confidence, a failed rule or a duplicate.
• Make review fast with keyboard navigation and sensible defaults.
• Capture corrections so they can improve models and rules.
• Keep separation of duties: the person correcting data should not also approve the payment.
• Monitor queue size and waiting time.

Measure accuracy properly

• Build a test set of real documents from many suppliers, including poor scans and unusual layouts.
• Have experts record the correct values for each field.
• Measure accuracy per field, not per document, and weight fields by the cost of errors.
• Track the share of documents processed without manual intervention, the exception rate and handling time.
• Re-measure when suppliers change templates or when models are updated.

Controls specific to language models

• Require structured output that follows a defined schema.
• Instruct the model to return an empty value when a field is not present instead of guessing, and verify that extracted values actually appear in the document text.
• Treat document content as data. Text inside a document that looks like an instruction must never change processing.

Security and privacy

• Documents often contain personal and financial information. Restrict access to queues and archives.
• Encrypt documents in storage and in transit.
• Review the data processing terms of any external AI service, and redact where possible.
• Set retention periods that meet tax and legal record-keeping obligations without keeping data longer than needed.

Operations

Monitor accuracy and exception trends, keep a manual fallback for outages, and review the most common exception reasons each month. Many can be removed by fixing master data or asking suppliers for consistent documents.