Invoice and expense data processing is a common practice in accounting departments across all industries.

Some companies still do this process manually. Some decide to use different solutions to streamline their work.

One known solution to help the automation of data entry is OCR (Optical Character Recognition) technology, which is mainly used by companies that collect and process a large number of documents. 

What Exactly Is OCR Technology?

OCR is a technology that enables companies to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data. 

So let’s say you’ve got a paper document which you want to digitize.

However, just scanning the document is not enough to make this information available for editing. 

In order to extract and repurpose data from scanned documents, camera images, or PDFs, companies will need an OCR software that can identify letters on the images, put them into words and then convert them into editable data — thus enabling you to access and edit the content of the original document.

“Optical Character Recognition, often abbreviated as OCR, is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document or a photo of a document.” Wikipedia 


Data Extraction Beyond OCR 

Moving on from solutions where OCR is the primary contributor to digitization efforts, there are other technologies available that improve the data recognition and extraction capabilities across the board.

Such applications use iterative learning and business validation models to continually adapt and improve in rapidly changing conditions and give companies the ability to put their data to use.

Today, machine learning-powered OCR solutions enable companies to harness the benefits of data extraction and classification in a more sustainable manner. 

It also gives data recognition systems the ability to adapt to changing data structures and improve data classification in the process.

And these trained systems can push accuracy to a similar or higher level than human employees do.

Automated Invoice/Expense Processing and OCR

To illustrate this case in a practical example, let’s take a look at invoice and expense management. 

Invoice and expense management are some of the tasks that can benefit the most from automatic data entry.

Getting out a stack of receipts and invoices to manually copy every line item can take a while. And if you travel a lot for work or attend many meetings, chances are you need to do this task at least once a month.

In reality, companies which deal with many vendors have difficulty scaling this process because, with traditional OCR solutions, they need to set up new templates for each vendor and document format. 

This typically leads to inaccuracies because, without the correct rules in place, data will not be extracted correctly. 

In fact, the process of implementing new templates is tedious, time-consuming and expensive. Every new vendor will require a templating effort and this can typically lead to creating templates only for vendors that your company deals with regularly.

This results in a set of invoices that are handled manually as it won’t be “worth the effort” to set up a new invoice template.

Both creating and maintaining templates requires time and resources.

With a multi-layered solution such as Innovo Invoice, we harness the power of OCR in combination with machine learning algorithms and a series of validation processes to make invoice and expense processing easier.

Our image processing capability allows you to drag and drop an invoice (directly from your laptop via our web app, as well as via email), and it does not only identify the date, merchant and amount, but also the category.

Afterwards, data can be exported or integrated into the accounting or ERP system that you are using to make the entire process easier.

How Does Our Multi-Layered Process Actually Work?

• We Digitize Invoices Through OCR

While using OCR-driven solutions has its challenges, OCR is still a critical component in the classification and extraction of data. Scanned documents and images of invoices and receipts received in multiple formats are first converted to text via OCR.

This way, employees don’t have to manually enter invoice data into the system anymore. The system simply uses technology to transfer every invoice into the same standard format and capture the included information.

• We Use a Continuous Learning Process

Our self-learning system derives transaction patterns as it processes invoices automatically. The text obtained from the OCR is then fed into the machine learning models for classification and extraction.

As the system continually adds data through ongoing invoice processing, it builds insights from to drive predictive, self-correcting, and intelligent decisions.

The idea here is that the more invoices are processed, the more efficient the process becomes. And eventually, the ability to continuously self-improve will bring companies more value over time.

• We Have a Layered Business Validation Process

After the machine learning algorithms drive the relevant data classification and extraction, logical/business validation adds an extra layer of checks to ensure data is captured accurately. 

Examples of business validation include checking if a transaction date is not in the future or if the tax amount is the correct percentage of the total amount.

As business validations apply deterministic rules on the outcomes of stochastic models, the accuracy of the output is greatly increased.

• We Handle Exceptions In-House  

With OCR-driven solutions, any exceptions that arise as a result of data extraction pose a headache to the staff who process the invoice. As such, teams need to make corrections and comparisons to figure out the errors and ensure correctness.

But, with the right solution, such exceptions are handled in a sustainable way.

A robust system can handle exceptions automatically with algorithmic intervention based on insights from the collected data.

This way, invoice exceptions no longer have to delay the process and payments can get out on time.

automated invoice processing

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Posted by:Colin Anthony

Colin Anthony is the Co-Founder of Innovo42.