Invoice Processing AI

Artificial Intelligence Based Invoice Processing

Invoice processing; It appears as a need that increases in parallel with the result of increasing trade volume. Before we move on to invoice processing, in other words, let’s take a closer look at the AI technology itself. Simply put, AI is a machine-based simulation of human thought processes. This is a broad term that we can subdivide according to the level of intelligence. In the context of automated data extraction, we are most interested in deep learning. It also enables people to reason and correct themselves. This is a highly advanced structure,
artificial neural networks
known as algorithm networks.

OCR alone is not capable of capturing data from invoices. OCR’s sole function was to convert images into text that you could work with. You need to constantly instruct traditional OCR invoice recognition software, setting new templates for each new invoice.

The combination of OCR and AI-powered data extraction frees you from this endless cycle of instructions. An OCR with artificial intelligence features can process every single text on the invoice. Deep learning capabilities enable the software to automatically recognize new forms of billing with little or no human intervention. Minimizes the need for manual control.

Invoices are complex and variable. Most companies tend to have their own invoice templates. There is no naming standard for the data fields on the invoice. For example, “Total”, “Amount Due” and “Payment in Turkish Lira”, “Amount” may have the same meaning.

invoice processing

A simple OCR program will not understand any of these differences. Lack of rules leads to an increased risk of capturing erroneous data. It may even lead to accounting errors as a result.

As a result, AI-powered OCR is taking invoice data capture to a new level.

Benefits Provided by Invoice Processing

Invoice processing automation also has an impact on compliance, security, relationships with external and internal parties, early payment discounts. It also contributes to improved workflow planning and reduced loss-related costs. It can have an immediate, direct and measurable impact on them.

1- Time Saving

First, AI-based OCR greatly reduces the time spent on invoice processing. Automation enables a company to significantly reduce the time it takes to manually process a single invoice. This means 6 times more documents can be processed simultaneously, leaving more time for higher priority tasks. Moreover, the completed training invoice system does not require any layout configuration, rule or template definition. This saves a lot of effort during the implementation phase and allows for same-day use.

2- High Data Accuracy

Automated invoice processing produces more accurate and faster results than humans under the same conditions. Reduces common human errors. As a result, it processes more invoices from users with higher precision and on time. When it comes to automated invoice processing, the more invoices processed, the better.

3- Efficiency

With AI-based invoice data capture, it is possible to perform the same tasks in less time and with fewer errors. This means less double-checking and repetitive tasks. This leads to higher efficiency and productivity. Streamlines business processes. This provides a break from tedious, manual work.

4- Low Cost with Invoice Data Capture

As there is no need to configure rules or templates, implementation costs and operating costs will be significantly reduced. With increases in system efficiency and reduction in errors, the real costs of automation will soon show declines.

5- Improvement in Process Management

Invoice processing with AI-supported OCR prevents late payments. No invoice will be forgotten as human error in the process will be eliminated. This will eliminate penalties for late payments, calls from suppliers and chaos. Ultimately, all this leads to better relationships with partners and better financial performance in the long term. In addition, invoice data capture automation shortens the invoice approval process.

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