The impact of your accounts payable team is no longer limited to processing transactions.
The performance of your AP team can be the difference between robust financial health and a higher cost of doing business. Making sure your accounts payable processes are optimized enables business growth while making financial reporting and employee retention easier. Specifically, timely and accurate payments help you manage expenses, take advantage of early payment discounts, foster positive relationships with suppliers, and control your cash flow.
However, businesses continue to process almost 80% of supplier AP invoices manually. Transferring, checking, coding, and verifying invoice data by hand is time-consuming, inefficient, expensive, and error-prone.
But by automating these payment processes, your business can avoid the risks and reap the benefits.
One solution to modernize your accounts payable process is OCR, or Optical Character Recognition. Read on to explore accounts payable OCR in more detail and find out where this technology can work for you – and where other solutions might fit better.
OCR is the technical term for text recognition technology. OCR automates extracting textual data from handwritten, typed, or printed documents such as invoices, sales orders, or photographs. Accounts payable OCR is the application of this technology to financial operations. It can be carried out in-house or contracted out to a third-party service provider.
Accounts payable OCR technology lets you:
For many companies, invoice processing is the main accounts payable process that needs to be made more streamlined.
Invoice Processing OCR systems use advanced pattern recognition and data extraction algorithms to convert paper or electronic invoices into machine-readable text and efficiently capture critical details such as invoice numbers, dates, and line item amounts.
The OCR process can be broken down into three steps: input, processing, and output.
The OCR process starts with the manual or automatic scanning and importation of a paper document into the OCR software to make a simplified digital image stored as a digital file such as a PDF or a JPEG.
Text detection is the next step and also the most important. It is crucial for distinguishing between different elements in the document and ensuring accurate recognition. There are several possible processing models, but basically, algorithms analyze the light and dark areas on the document and identify the features typical of individual characters. It also divides the text into its structural elements by recognizing features such as words, paragraphs, spaces, and line breaks.
Machine learning and artificial intelligence techniques may be employed to improve recognition accuracy by learning from a vast dataset of characters. An advanced form of OCR, known as ICR (intelligent character recognition) can even handle individual handwriting in various languages. Meanwhile, zonal OCR allows users to speed up the process and reduce errors by choosing which areas of the document are to be processed and which can safely be ignored.
Once the text is recognized, the OCR software transforms the data into a storable, searchable, and editable digital version that should be accurate to the source text and can then be transferred to a centralized system of records.
OCR technology offers many advantages when it comes to streamlining your financial processes.
By automating the extraction of data from invoices, OCR can:
While OCR technology offers substantial benefits in automating accounts payable processes, several challenges exist.
The initial setup and maintenance costs can be high, requiring organizations to invest in software, hardware, and ongoing support.
OCR can never guarantee 100% accuracy 100% of the time. Inaccuracies may occur due to lookalike characters, such as ‘5’ and ‘S,’ or ‘l’ and ‘1’, and the wide variation in typefaces and character composition. Issues may also arise when dealing with handwritten or poor-quality documents. For example, blurry, skewed, or poorly scanned invoices can impede OCR's accuracy in character recognition. It also has problems identifying Arabic and Chinese characters, which can hurt the operations of businesses that deal with these language families.
Security and data privacy concerns also come into play, as third parties may process and store sensitive financial information.
OCR systems may also encounter limitations in handling non-standard invoices or those with unconventional formats, requiring manual intervention for proper interpretation.
Another major drawback of OCR is that it does not provide an end-to-end solution for data structuring. Although it can scan, analyze, and convert unstructured data into a machine-readable format, additional reliance on other machine-learning technologies or human resources is necessary to ensure accurate data placement.
While OCR can be leveraged to extract text from images, it’s only capable of 98-99% accuracy. That might seem quite reasonable, but for every 10,000 characters, it means up to 200 errors – and in this area of your business, you need your data to be 100% accurate.
It works best when input sources are relatively standardized; for example, automatic number-plate recognition systems greatly benefit because license plates are typically all produced in the same font of the same size. Regarding OCR, employees must always review every invoice before validations occur.
On the other hand, with the Conexiom Platform for Accounts Payable, 100% data accuracy is perfectly achievable. Moreover, our solution is a full automation tool, not just a workflow tool, and goes far beyond the simple text recognition of OCR technology.
The Conexiom Platform for Accounts Payable lets your AP team:
Our automated AP invoice processing system is based on combining customizable data mapping with automated business rules to control data integration. In our data processing, we only use OCR for invoices received as images, avoiding the need for human intervention to verify extracted data before validations occur.
We can handle specific business rules and logic on the global, vendor, and product/SKU layers. Thanks to our technology and proven experience, our clients typically see ROI within as little as four weeks.
Conexiom offers two approaches to automated invoice processing.
Our experts will collaborate with your AP team during intensive workshop sessions to build templates and incorporate your business rules and logic into our system.
We use the specific maps and templates built during the workshops for high-volume vendors that make up most of your invoice volume (typically 70%). This allows our system to handle each vendor’s business rules and logic and accurately identify the required fields on incoming invoices before validating and processing them.
For low-volume vendors, Conexiom uses its network of prebuilt maps along with Machine Learning and AI to identify fields before automated data extraction and validation.
To ensure extracted data is 100% accurate, Conexiom allows the ERP to act as the source of truth, performs a two-way match between the invoice and the PO in the ERP, and validates the documents against each other.
Successfully validated invoices are delivered to the ERP for a three-way match process with the receipts. This streamlines the process and provides clear indicators of where the invoice doesn’t match the PO or where the invoice doesn’t match the receipt.
If exceptions are flagged, the AP team can resolve them before the invoice is delivered to the ERP.
This data accuracy guarantee is one of our core differentiators; if you’d like to find out what Conexiom Platform could do for your business, get in touch with us today.