Invoice processing is what many small business owners would simply call paperwork. It’s a process related to handling documents required for Accounts Payable purposes. Here’s basic information on how to handle invoice processing, and how to avoid invoice processing errors by using Optical Character Recognition (OCR) and machine learning.

What is invoice processing?

Invoice processing typically refers to all procedures associated with handling incoming invoices, from receiving documents, to posting into accounting records. Invoice processing is an essential part of Accounts Payable (AP) since it’s related to the company’s purchase of goods or services that are billed for and received before being paid for.

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The primary goal of invoice processing is to ensure incoming invoices are legitimate and contain all required information to get recorded into the books. Also, invoices must be related to the correct PO and match contract details, as well as goods or services actually received.

An Accounts Payable clerk needs to make sure several documents include corresponding information regarding the transaction. These documents include Purchase Orders (PO), receiving reports, invoices, and contracts. Typically, a lot of double checking, document verification, and data entry tasks are involved. While it does seem like a time-consuming process, you can accelerate at least one of these steps, data entry, with technologies we’ll get to in just a moment.

Steps you typically take for processing invoices

Invoice processing typically involves several steps that are highly dependent on the type of accounting, the category of goods or services, company size, and company policies. Here’s a rough example of what an invoice processing workflow may look like in a small business.

  1. Invoice arrives. Most companies can handle electronic invoices, thus streamlining the process and eliminating the need to store paper documents. However, research shows that paper invoices are still dominant.
  2. Invoice is confirmed and categorised. For example, in QuickBooks unpaid business costs are called bills while the ones already paid for are referred to as expenses.
  3. Invoice is matched with a PO (if it exists).
  4. Invoice is sent to the person responsible. Typically it’s the person who placed the order.
  5. The superior of that person may have to approve the invoice, especially when the amount exceeds a certain amount specified in the company’s policies.
  6. After confirmation and approval, the bookkeeper or assistant extracts invoice data and posts it into the accounting books.

What are some common issues and errors related to invoice processing?

Given that Accounts Payable procedures rely on several people involved, there’s a lot of room for error. The most typical issues include missing POs, too many paper invoices that fade over time or get misplaced, wrong amounts, dates, categories, and other data entry-related errors. Differences between invoice layouts, file types, languages, and currencies can be problematic as well.

According to the Forrester Research, as much as 80% of companies rely on manual data entry for handling expense documents. As a result, the number of issues around invoice processing is immense. Bloomberg’s study reported that 27.5% of companies say that human error is behind most mistakes while entering data. Other issues included accidentally removing customised Excel formulas (17%), and overwriting system data with numbers calculated somewhere else (13%).

OCR and machine learning help you avoid invoice processing errors

You can speed up how your company handles invoice processing by implementing a machine learning-enhanced OCR system. Doing so improves the most problematic step, which in most cases is manual data entry.

OCR reads data from documents and converts it to an editable format that can be copied and pasted. Smart invoice processing tools not only obtain data, but also let you import it automatically into your accounting system, such as QuickBooks. However, OCR in its standard form is prone to mistakes which need a time-consuming verification process. To speed things up, machine learning systems learn how you verify and categorise expense invoices to make assumptions based on your choices. The key here is specialisation—what you need is a system specifically built around recognising invoices, receipts, and similar documents.

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