Invoice OCR Fields Explained: What to Review

Diagnostic goal: Understand the merchant, invoice number, dates, tax, currency, totals, and line items commonly extracted from invoices.

Identify the failure before changing the record

For reviewers checking AI-extracted invoice data, the target is to focus attention on fields that affect matching and reporting. First determine whether the source is weak, the extracted value is uncertain, the document belongs to another transaction or the workflow context is missing. Correcting a field before identifying the cause can hide the evidence needed to prevent the same error.

Diagnostic 1: Verify supplier identity before accepting similarly named merchants

• Expected evidence: Supplier and invoice number are correct.
• Failure to rule out: Assuming every date has the same meaning.
• Resolution: Compare the untouched source with the structured record and document the smallest supported correction.

Diagnostic 2: Distinguish invoice date, due date, and payment date

• Expected evidence: Date meanings are explicit.
• Failure to rule out: Accepting a subtotal as the final total.
• Resolution: Compare the untouched source with the structured record and document the smallest supported correction.

Diagnostic 3: Check tax and total arithmetic against the source

• Expected evidence: Totals reconcile to the document.
• Failure to rule out: Missing a credit note or negative amount.
• Resolution: Compare the untouched source with the structured record and document the smallest supported correction.