The use of AI to prepare accounts-payable work, including extraction, duplicate detection, matching, classification, and approval routing.
Accounts payable starts with supplier documents and ends with an approved accounting result. AI can prepare several steps between them: read the invoice, structure its data, keep related documents together, propose a classification, and send exceptions or approvals to a person. The useful distinction is not the label. It is which parts of the workflow the system completes and what remains for review.Read the full guide to AI in accounting →
OCR vs AI
OCR converts text in an image into machine-readable data. AI can use that data with document and accounting context to prepare later workflow steps.
OCR answers a narrow question: what text appears in this file? An accounts-payable system may then use other models and configured logic to structure line items, identify duplicates, match related documents, propose accounting classifications, or route a review. Evaluate those jobs separately instead of treating “AI” as one feature.Read the OCR and AI evaluation guide →
3-way match · Three-way match · Triple reconciliation
Comparison of a supplier invoice with its purchase order and delivery notes before the invoice continues through approval and accounting.
The check compares references, quantities, prices, amounts, and taxes across the related documents. Manual matching requires a person to locate and compare each source. Automated matching assembles the set and highlights differences for review.
Intelligent invoice capture
Receiving invoices through connected channels and extracting document and line-item data into structured fields.
Capture may start with email, a shared repository, an API, a PDF upload, a scan, or a photograph. The system identifies the document and extracts headers and line items. Confidence indicators can send uncertain values to a person. Reception and extraction are separate jobs, so check both when evaluating a product.
Touchless AP · Lights-out AP · No-touch invoice processing
A category term for invoices that move through configured reception, checks, approval, accounting, and sometimes payment steps without manual handling.
The exact boundary varies by product. Some vendors stop at an approved journal entry; others include posting or payment. Ask which steps are automatic, which rules allow that path, and where exceptions return to a person.
Automated journal entry
A journal entry prepared by software from extracted invoice data and the organisation's accounting structure.
Traditionally an accountant or bookkeeper does it: read invoice, decide account, write entry. Automated: the system classifies the invoice, maps the chart of accounts, and proposes (or posts) the entry ready for review. Human review concentrates on exceptions, not data entry. More context in the pillar guide: what AI in accounting automates and what it doesn't.
A tax mechanism where the buyer (not the seller) declares and pays the tax. Applies to cross-border operations, imports, and certain B2B services.
Instead of paying the relevant tax to the supplier, the buyer accounts for it under the applicable rules. The scope and reporting treatment depend on the jurisdiction and transaction, so the accountant must review the source document and local requirements.
Bank reconciliation
The process of cross-checking bank movements against recorded invoices to confirm which invoices have been paid.
Manual reconciliation compares bank movements and accounting records one by one. Automated reconciliation proposes matches using fields such as amount, date, reference, and counterparty. Partial payments, grouped payments, fees, and currency differences usually require additional logic or review.
AI explainability in accounting · Source visibility · AI review context
The ability to review the source document, extracted values, confidence, configured logic, and human checks behind an accounting output.
Explainability helps a reviewer connect the proposed accounting result to the information that produced it. Useful evidence includes the original document, field-level values and confidence, related source documents, workflow status, and any human correction. This review context is distinct from a regulated retention system or customer-facing compliance audit log. Go deeper in the AI in accounting guide.
Get started with Calitem today.
See how Calitem is already helping finance teams save time and resources.
We use optional cookies to understand how this site is used and, with your permission, identify relevant business visitors. You decide whether to allow analytics and marketing cookies.
