We’ve seen the value that machine learning brings to finance, and how it has become an integral part of modern AP automation solutions and other finance systems. Thanks to embedded machine learning capabilities, AP solutions can automate invoice processing by using the invoice history. While this capability is highly beneficial, it represents just the tip of the iceberg when it comes to the advancements that machine learning and artificial intelligence can provide.
So, what’s holding us back from reaping even greater benefits? It’s our limitation in imagining new ways to apply machine learning to finance, but once we can do that, there’s no telling what we can accomplish. As motivational writer William Arthur Ward once said, “If you can imagine it, you can create it.”
Now’s the time to think about innovations that will move us forward. Toward that end, I’ll share my thoughts about the future. I envision machine learning will be used to remove some of the financial risk CFOs are currently struggling with and give them a clearer picture of their finances. CFOs need certainty when it comes to their organizations’ finances – both now and in the future – and while they are a long way from having that now, I expect machine learning will be able to help them accomplish that in the not-too-distant future.
Finance Today Relies on Guess-Work
Unfortunately, there is often too much uncertainty in the finance process today. Take, monthly closes for example. Right now, they’re really based on a “best guess” basis. For the most part, finance departments operate with 29 days of uncertainty, and on the day they close, they can develop a general understanding of where the company stands.
But even the close at the end of the month doesn’t provide total certainty. Sometimes, it’s not until a couple of weeks after the close when you can look back and really know what happened. That’s because it’s difficult to know the true state of where the organization stands at any given moment in time. Which invoices are you missing? Which projects are coming up? What payments are still missing? Best guesses are not nearly good enough. In order to run optimal finance departments and make better business decisions, you really need to know more, and you need to know it sooner.
Machine Learning Will Let You Close Daily
This is where I believe machine learning will continue to make significant improvements in data collection and accuracy. It can help you fill in the gaps, so you can get a better picture of where you really are, and where you are headed. One way machine learning can do this is by using past data and patterns to understand the present and predict the future.
Where we’re headed in the future of finance, is the ability “to close” daily – to have every day reflect an accurate financial picture. Consider the challenge noted above about trying to determine which invoices, projects, or payments might be coming. When you think about it, there are clues about this data that you already have access to. You have POs, contracts, subscriptions, emails, and travel plans that can provide insight into that information and help you fill in some of the gaps you have in your financial picture.
There’s another way I believe machine learning can find that key information – by predicting human behavior. For example, which vendors typically send their invoices late? Which buyers typically pay late? Machine learning technology would be able to factor in that information to predict expenses, income and cash flow. Armed with that information, you can decide which action to take. For example, when it comes to a late invoice, should you do an accrual for that? Or can you do a self-billing on it or proactively remind the vendor to send it?
When you’re predicting future income and expenses, you can also include data from outside sources. For example, if your company provides products for outside services, such as landscaping or construction, you might look at weather predictions to see how that might impact sales within a given period.
The insight you gain from machine learning could also help you plan staffing and improve processes. You can take a look at the invoices you expect to come in within a certain period and see if you have enough AP specialists to process them by their due dates or if you need to make staffing adjustments (which wouldn’t be necessary with proper AP automation????). Machine learning technology can also be used to remove redundant work, improve the process and find patterns in exceptions that can be defined and standardized. This will enable even greater efficiencies.
Enabling Timely Payments
Cash is still king, and having total, accurate insight into finances enables more efficient cash management, helping CFOs maintain the tenuous balance between risk and liquidity. With that information, they can plan timely payments to maximize their cash flow, while taking advantage of early payment discounts and other opportunities.
In addition to knowing where your finances are now, you need to be able to project where they will be in the future. Machine learning is a powerful tool that creates certainty in an uncertain financial world by filling in the data gaps. Now, let’s go create the machine learning features we know will simplify and speed up our financial processes.
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