Why Businesses Need More than Just an OCR Tool

Luckily, automation technology has transformed many businesses and their finance functions over the years, saving finance professionals hours on laborious tasks like data entry. One such technology is optical character recognition (OCR), a tool many businesses have invested in over the last decade.

By scanning and extracting data directly from documents, such as invoices and purchase orders, OCR helps businesses cut costs, save on manual labour, and gain access to necessary information far more quickly and efficiently than the alternatives. However, OCR alone isn’t enough for a business to thrive; finance departments need more.

In this article, we’ll look in detail at how finance teams leverage OCR and what else can be done to create a more efficient and accurate finance function.

OCR Is Transforming Financial Processes

It’s not that OCR hasn’t had a profound impact on the finance sector as it is. In reality, it has transformed financial operations in several ways.

The ability to scan and extract data from receipts, invoices, purchase orders, and other crucial documents and turn that data into editable text is a game-changer. This means businesses can process large amounts of data in a much shorter timeframe without resulting to manually keying in information.

Additionally, integrating OCR into your finance processes makes it easier to run related searches and streamline access to information. It’s not just access that’s improved; data accuracy is also increased, given the reduced risk of human error derived from manually interpreting and entering data.

OCR can also help cut down on paper trails and reduce the need for manual filing. By minimising time-consuming tasks and freeing up the finance team’s schedules, team members can focus on more critical tasks, such as forecasting and budgeting.

The Limitations of OCR in Finance

Despite its benefits and ability to ease finance operations, OCR is not without its faults. As with most things, there are drawbacks to using OCR in finance, particularly when it’s used as a single tool and not part of a more extensive toolset. In most cases, OCR is limited unless paired with other technologies.

Some of these limitations include the following:

• Large and small fonts can make it harder to read and extract data. This can cause the information to be unavailable, as many of today’s OCR tools are case-sensitive.
• Different languages and special characters can be incorrectly recognised or nullified during extraction.
• Blurry and misaligned images, like a scanned photo ID or document, can cause misreads and data loss.
• Coloured and design-heavy documents can make it hard for OCR tools to read and extract data successfully.
• OCR is only fully competent when data is correctly structured for optimal extraction. This means additional technologies are required to understand the context and subtleties of different documents and forms of ID.
• Sequential errors from OCR tools mean the same errors need to be constantly edited and corrected.

These are just a few of the challenges facing OCR technology, which can lead to incorrect, inaccurate, or missing data, something finance departments cannot afford. Dirty and inaccurate data can lead to time-consuming manual processes or chasing down and re-entering certain information.

Successfully structuring and extracting data involves more than just an OCR solution. For finance teams to function effectively, they need to implement the best tech stack suited for their business.

OCR Alone Isn’t Enough

When combined with other automation tools, OCR can help streamline processes and increase efficiency across the finance department.

If extracting data using just an OCR tool isn’t enough, what is? Well, you need a combination of two powerful technologies: OCR and machine learning.

Machine learning (ML) is derived from artificial intelligence (AI). This means ML can analyse data and solve problems with little to no human intervention. This is why OCR, coupled with ML, is being applied in nearly every industry in one way or another.

An example of this in action would be if an OCR tool is extracting data from an invoice, but one of the fields is left blank, and that missing piece of information is required to process the invoice. This is where ML steps in.

Once the error has been flagged and recognised, the invoice is automatically sent to the relevant party for review. They can then rectify the mistake and resubmit the invoice.

Even with both OCR and machine learning in the mix, it’s still not the perfect solution. Today’s fast-paced businesses are looking at cloud-based payables solutions that can transform their finance function via automation.

Implementing cloud-based software to automate accounting processes provides team members with flexibility and increased visibility across all aspects of the finance function. This allows multiple simultaneous users to access the system both in and out of the office, which is ideal for today's remote and hybrid workforces.

The key to success is a combination of different technologies brought together. Therefore, careful consideration is required when choosing your organisation's relevant finance and accounting tools.

Learn more about how Tipalti can help your finance team eliminate manual data entry with advanced data extraction tools like OCR, along with other ways to streamline your invoice management processes.