What is invoice data capture – and why is it important?

Invoice data capture is an essential function of accounts payable. In today’s digital world, the success of a business often depends on how accurately they’re able to capture and handle data, including their invoice data.

To optimise their operations, many businesses are turning to automated invoice data capture solutions. Despite these solutions being readily available, however, some companies still cling to manual entry – and this doesn’t come without its challenges.

In this article, we’ll cover what invoice data capture is, how it works and the methods and technologies used to extract invoice data. We’ll also take a look at some of the reasons why automated invoice data capture is an important asset for your business.

What is invoice data capture?

Invoice data capture is the process of capturing key data from invoices – one of the first steps in the invoice approval workflow. Once the invoice data has been extracted, it’s validated against purchase orders and delivery receipts to verify the accuracy and legitimacy of the invoice.

Because of the challenges of manual entry, many businesses are turning to automated invoice data capture solutions to optimise the process and improve the efficiency of their financial operations.

How invoice data capture works

There are four primary steps in the invoice data capture process:

1. Receipt of invoice: First, the invoice is received. Invoices can come in a variety of formats, including paper, fax, e-mails with PDF attachments and other electronic formats.
2. Data extraction: Next up is data extraction. This includes information such as invoice number, date, vendor and buyer details, total amount etc.
3. Validation: When the data has been captured, it’s validated to ensure accuracy and completeness. This typically involves cross-referencing with purchase orders and delivery receipts.
4. Data integration: Finally, the validated data is entered into the company’s accounting or ERP system for further processing – e.g. payment authorisation and financial reporting.

Methods and technologies: How invoice data is extracted

Depending on the technology or systems in place, a company’s invoice data capture process can be manual, semi-automated or fully automated.

Here are four of the primary methods and technologies used to capture invoice data:

1. Manual entry
2. Optical character recognition (OCR)
3. Intelligent character recognition (ICR)
4. Machine learning and artificial intelligence

Let’s take a closer look at them.

1. Manual entry

We’ve mentioned that data extraction is traditionally done manually. Although manual entry is how most businesses start, it’s time consuming and error prone.

In addition to the time spent manually extracting data (as well as the time it takes to follow up on and correct any errors made in the extraction process), a manual workflow also often results in delayed payments.

Companies with a manual invoice workflow are also particularly vulnerable to fraudulent billing. For these reasons, manual entry tends to be more expensive than automation in the long run.

2. Optical character recognition (OCR)

Optical character recognition technology can extract characters, numbers, letters and symbols from many different invoice formats. These include unstructured data from scanned paper documents, PDFs, JPEGs, Word documents and e-mails, as well as structured data from e-invoices.

Once it has captured the invoice information, the OCR technology converts it into editable and searchable data. This significantly reduces the amount of manual work involved in the extraction process.

3. Intelligent character recognition (ICR)

Intelligent character recognition is an advanced version of OCR technology designed to recognise, decipher and process different types of fonts as well as handwritten text.

ICR technology offers advanced features that can be particularly valuable for larger companies handling a high volume of documents. However, it’s less cost effective than OCR technology, which also tends to meet the needs of smaller companies with fewer documents to process.

4. Machine learning (ML) and artificial intelligence (AI)

With ML and AI technologies, systems are able to handle complex data extraction that traditional methods might struggle with. Advanced AI models can understand the context in which data appears, which allows for more accurate extraction.

AI technologies can even handle invoices in multiple languages, making them a valuable asset for businesses dealing with international vendors and suppliers.

The benefits: Why automated invoice data capture is important for your business

We’ve mentioned that manual entry is a time-consuming process that’s prone to human errors. Despite this, some companies still cling to manual processes, and that comes with significant challenges, some of which we’ve covered above.

For that reason, many companies are turning to automated invoice data capture – and the benefits are numerous.

Here are some of the reasons why automated invoice data capture is an important asset for your business:

• Enhanced accuracy: With automation, there’s no need to worry about human errors. Through the use of technologies such as OCR and AI, automated systems accurately capture invoice data and significantly reduce the likelihood of errors.
• Increased efficiency: Automated data capture processes invoices much faster than manual entry. This reduces the time it takes for an invoice to move through your approval workflow, helping to speed up the payment process.
• Cost savings: Fewer errors mean less time and resources spent on correcting mistakes and resolving discrepancies. Automation also reduces the need for extensive manual data entry staff, lowering labour costs related to invoicing.
• Scalability: With an automated system, it’s much easier to scale to handle an increasing number of invoices. This makes it easier for you to scale operations without a proportional increase in staffing or manual labour.
• Data insights: An automated system provides real-time access to invoice data. This allows for more timely and accurate financial reporting and analysis, helping you to create more accurate financial forecasts and budgets.