Many of the technology innovations that will carry Accounts Payable (“AP”) departments and ePayables solutions into the future have already largely arrived. AP and finance leaders that keep an eye on the horizon will most likely already recognize some of the advancements that are pushing AP automation solutions into the future, and create ever-greater efficiencies and opportunities to add strategic value for AP teams. Last week we discussed one of them, Robotic Process Automation (RPA), this week we take a look at another, Machine Learning.
Machine Learning is an outgrowth of pattern recognition and computational learning theory from computer science. Machine learning technology enables solutions that can be “taught” how to recognize specific data and automatically classify it. Machine Learning employs algorithms to study user data and find patterns after just a few transactions.
Machine Learning has been leveraged by many ePayables solutions for a number of years, quite probably without you even knowing it was being used. Machine learning is a function of powerful data engines, advanced algorithms, and user behavior that combine to take processes, user interfaces, and user experiences to the next level. It also enables AP solutions to be “self-taught” how to recognize specific data and automatically classify it.
The area where machine learning is most frequently used today is with invoice receipt via Scan & Capture solutions, which are the most common, and generally, among the easiest solutions to deploy. These solutions are designed to take paper-based, and/or PDF invoices and related data, and transform them into a usable electronic format. Eventually, tools enabled with machine learning algorithms will be able to anticipate organizational needs to help load balance the flow of invoices and better manage them in real time. It will also be used to increase the drive for more straight-through processing of invoices by automating routine decision-making and transactions, and enabling AP staffers to take on more strategic work. Machine Learning can also “learn” to recognize when data is missing and automatically route invoices with incomplete or erroneous data back to the submitting supplier or individual for correction/completion before the invoice ever gets submitted to AP for approval workflow. This enables AP to minimize the amount of time it spends on tracking down accurate data from suppliers.
Machine Learning can be transformative in the invoice management process because these solutions can learn specific business rules based upon supplier and user behavior, enabling faster and more accurate invoice processing with fewer errors and exceptions. Leveraging Machine Learning, along with Robotic Process Automation and Artificial Intelligence (which we will discuss in more detail next week) will go a long way in helping AP to create a purely touchless environment, eliminate tactical tasks, and become a more strategic, value-adding resource for the enterprise.
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