PO Matching – Automate with AI

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Choosing an AI-enabled accounting suite depends on the nature of the business and the scale of operation. AO-enabled PO matching could be either a point solution or a full accounting suite, which would depend on the existing software or lack thereof. In the case of the former, it would need to communicate with existing systems, including ERP. PO Matching is available in many tools used for accounting including Nanonets AI-OCR, Oracle, Nexxonia, Intacct, MineralTree, etc.

In Oracle, Payables is the AI-enabled PO matching tool in which once an invoice is entered and matched to a PO, distributions are automatically created and the match is checked for compliance with the tolerance defined. Once matched, Payables updates the quantity billed for each matched shipment and its corresponding distribution(s) by the amount entered in the Quantity Invoiced field. Payables also updates the amount billed on the PO distribution(s).

Sage Intacct Purchasing creates structured, predefined transaction and purchase approval workflows. MineralTree, an Accounts Payable (AP) and payments automation solution provider, provides automated PO/invoice matching for Sage Intacc. In this, header and line-level details are automatically extracted using OCR technology from invoices sent by vendors to a designated email. It then automatically matches incoming invoices against purchase orders or receipts and then inserts them into the users’ internal workflows for invoice approval and payment. All data syncs with the company’s ERP for platform consistency.

Nexonia Expenses, a cloud-based web and mobile expense report management solution that has flexible approval workflows and deep integration with existing systems.

In Tipalti, all invoices go through a standard OCR, advanced data extraction, and approval workflows before payment is processed. Rules may be set to determine if an invoice is PO-backed and if it should go through the matching process. Base rules apply on supplier or bill amount and if an invoice has a purchase order, the PO bill coding data automatically pre-populates the invoice.

In DocuWare, when an invoice is captured, an AI-based, crowd-learning tool extracts all key data required for processing like Vendor Name, ID, Invoice Number, Sub-Total, Tax, Freight and Total Amount. To validate the invoice, the system confirms if they are a valid vendor, double checks for any duplicate invoice numbers, matches to purchase orders and delivery slips and recalculates the amounts.

There are many more PO matching tools available with various features to suit various applications.

Nanonets AI-OCR reads unseen, semi-structured documents that do not follow a standard template and validates the data captured from the document. The software can capture data from a variety of documents including Invoice, ID Card, Purchase Orders, Income Proof, Tax Form, and Mortgage forms.

It enables importing of data from the user’s platform and directly export the captured data to an existing workflow, without disrupting the system. Nanonets has language bindings in Shell, Ruby, Golang, Java, C# and Python. The AI engine learns and improves with use. With an intuitive web interface, it eliminates cumbersome manual processes and automates invoices, receipts, and document reviews. It is known to reduce processing time by up to 90% and save on costs by up to 50%.

Artificial intelligence is expected to play a critical role in transformation of the way accounting and PO matching is performed in the corporate world. However, it cannot eliminate human participation – technology cannot exist alone.

Artificial Intelligence will assist, not replace the Accountant. The key to successful implementation of an AI-enabled accounting system is to bring them together. The future of the use of AI in accounting and PO matching relies heavily on how humans can anchorage it to improve their capacity to deliver long-term values.

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Source: https://nanonets.com/blog/po-matching-purchase-order-matching/