In the past couple of years, the phrases “machine learning” and “Artificial Intelligence” have become a greater part of the vernacular surrounding finance software. If you work in the finance industry, sooner or later you will come across this technology. There’s no better time to learn about the benefits it offers for both the individual employee and organization as a whole.
One of the roles that will be highly impacted by machine learning is Accounts Payable. While automation has been the recent darling of AP by eliminating the more manual aspects of the job, it’s quickly taking a back seat to Artificial Intelligence and Machine Learning. Here’s what Forbes says about the topic:
“It is high time for every accountant to reflect on their job, identify the opportunities machine learning could offer to them, and focus less on the tarks[sic] that can be automated and more on those inherently human aspects of their jobs.”
In this article, you’ll learn why the focus in Accounts Payable technology is shifting from automation to artificially intelligent technology, and how that leads to an improvement in the “inherently human” aspects of the role.
Will AP Automation Become Antiquated in 2018?
There’s no doubt that automation has had a strong impact on the Accounts Payable process. Regimented approval workflows, Optical Character Recognition (OCR) technology, and automated invoice processing have removed many manual aspects of the job. However, due to the nature of their implementation, they are essentially static solutions. Once implementation is complete, the workflows are in place, and AP staff are trained on the process, the solution has reached its highest potential for improving business operations. And for many companies, that is just not good enough.
Artificial Intelligence in Accounts Payable
Accounts Payable software that incorporates machine learning is capable of learning the structure of your invoices, and the patterns of manual data entry. The beauty of machine learning is that the logic and suggestions improve as you go. This eliminates the lengthy implementation project required by automation solutions.
Here are just a few examples of how Artificial Intelligence is being employed today in Accounts Payable software:
Faster invoice field entry with smart suggestions. Machine learning capabilities allow the system to recognize patterns, such as when a certain General Ledger (GL) code is applied to a certain vendor. Over time, the machine learns these patterns through advanced data analysis. Artificial Intelligence begins suggesting this GL code whenever an invoice from that vendor is being processed. As you can imagine, this greatly reduces the amount of time AP spends keying data.
General Ledger and Vendor connections. Connections between your vendors and their corresponding GLs can be learned by just a couple of entries. This allows for instant field population the next time based on your previous entry, and a lower margin of error.
No implementation project. Automation solution implementation can take months to complete. Much of that time is spent constructing the logic for automatic workflows. This means a laborious, time-consuming “project” on the part of AP before they begin to see results. Artificially intelligent systems do not require this project because the logic is learned as AP works in the system. The system’s initial learning curve is quickly balanced out by smoother, faster invoice processing.
Duplicate entry identification. An artificially intelligent algorithm compares at least four invoice fields, resulting in a more thorough duplicate catching record.
In a nutshell, Artificial Intelligence and Machine Learning builds on the “automation” foundation of making the more mundane aspects of the AP role more manageable. It takes this “automatic workflow” and simply makes it better and faster. To return to the Forbes reference above, this allows Accounts Payable to turn their attention to the more human (and time consuming) aspects of their job – managing the communication around invoice approvals.
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