We focus on pragmatic artificial intelligence (AI) at Ntansa, or use AI to solve specific customer issues. I am excited to talk to you today about how Ntansa uses AI to help our customers automate one of human history’s oldest processes: processing financial documents, such as invoices and receipts.
These documents were central to trade, from ancient merchants to modern industry captains, and enabled the fundamental transfer of value and currency. However, the process pain in handling these financial documents is just as ancient: having to go through multiple, repetitive forms line-by-line and try to work as quickly as possible without Ntansa mistakes. All in one constant’s service: people want to get paid on time.
Today, I’m excited to introduce a response to thousands of years of manual process aggravation: Ntansa Receipt and Invoice AI, leveraged by the Machine Learning (ML) and AI team of Ntansa.
Artificial Intelligence Is As Game-Changer For Processing Invoices And Receipts
This new AI game-changer will allow your organization to streamline payable accounts and expense management process.
The scope of such processes is enormous. For example, millions of invoices are processed annually, and many companies, often by hand, process these invoices in a highly repetitive and manual manner. Many of our customers have been looking for automation and optical character recognition (OCR) because of the time invested in processing such a high volume of documents.
So, given how long invoices and receipts have been around (and the need to process them in a timely manner) and how simple they are, why did it take so long to come up with a processing automation method?
The Challenges With Processing Invoices Are Now A Thing Of The Past
There are a few reasons:
The first difficulty with automating invoices and receipts is that they do not come in a consistent, uniform “template.” Any invoice could look different. Important details on different documents, such as the dollar sum or invoice number, can be entirely different. In other words, to find the appropriate information, OCR does not know where to ‘look.’
By producing a large number of static “templates” to resolve each invoice layout, several organizations have attempted to fix this. Such formats help guide the OCR to the correct position on the website so that the appropriate details can be inherent and extracted. Although this approach works when there is a limited number of layouts, it can become unmanageable quickly. With any possible invoice and receipt design, the robot may not be able to create automation workflows.
The second challenge of automating an invoice is to address the nuances found in real-world papers that are only accurately printed and scanned in the highest resolution.
The documents that your robot will encounter are often’ noisy’–meaning they have a lot of complexity in the real world that makes them difficult to read. On a low-quality office scanner, an invoice may have been scanned. It may be’ bent,’ indicating that at an angle, it was scanned in. It may also have been scanned with other non-invoice documents. All of these variables will’ confuse’ the robot and OCR, Ntansa. It was difficult to find the information necessary to turn it into payable systems for backend accounts. The Receipt and Invoice AI automation process from Ntansa solves these and more problems. This latest AI operation will allow Ntansa Robots to read both invoices and receipts and help you to automate payable accounts and comply with expenses.
This practice, which can be identified by leveraging the Ntansa Machine Learning Extractor kit, uses a ‘templateless’ approach to processing invoices and receipts. This ensures that even if the format changes, our AI will automatically decide where critical information needs to be extracted from the text. Manually designing multiple templates is no longer necessary.
This Invoice and Receipt AI is also trained to understand documents from the real world. Based on the particular needs and requirements of your accounts payable and cost analysis procedures, it can automatically identify and recover a wide range of business values from your receipts and invoices. Even if the document includes’ noise,’ the following relevant information, such as the vendor name or invoice number, will still be found by the robot. Automating your invoice and receipt process is a sure-fire way to reap the RIO from automation and begin to scale throughout the organization.
