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Artificial Intelligence: Treasury Use Cases

  • By Andrew Deichler
  • Published: 11/22/2019

Artificial Intelligence

There are many treasury use cases for artificial intelligence (AI) and machine learning (ML), and even more will likely be revealed as practitioners familiarize themselves with the technology and what it can do.

ACTUAL RECONCILIATION

AI and ML have incredible potential for cash management and forecasting, particularly when reconciling prior day bank files with yesterday’s expected cash position. “This is one of the first cash management processes performed each day,” said Bob Stark, vice president of strategy for Kyriba. “And for some organizations, the volume of transactions is so big that it can take hours and multiple people to do that reconciliation.”

ML can be used to identify and resolve those discrepancies on its own. “In the simple scenario where the prior day file reports a $1 million wire and we thought it was going to be $900,000, the cash manager will know through their experience what explains that $100,000 difference and what to do about it,” Stark said. “Machine learning will learn from the user’s manual reconciliation, so next time it will reconcile those transactions without human intervention.”

Added Stark: “The tests that we did with our clients reconciled 99% of previously unmatched transactions, just by looking at the past six months of data. With more data, the accuracy approaches 100%. The key, however, is the amount of data. Machine learning cannot ‘learn’ from one or two scenarios a day; it needs a large sample size of information in order to find meaningful conclusions.”

But AI and ML can do more than detect anomalies—they can recognize when an exception isn’t actually a problem. For example, your company might make a regular monthly payment to a supplier of approximately $10,000. However, a recent payment made at the end of the current month is $15,000. With rules-based automation or even RPA, you likely have a payment control that flags that 50% variance from the normal monthly amount, quarantining that payment for further review. But ML can recognize that this particular payment is part of a larger pattern where the last monthly payment in each quarter is substantially higher than the average. “ML looks at historic data to determine what the true pattern really is to reduce potential false positives,” Stark said. “But the AI process needs to have access to all historic payment patterns in order to make that determination, much like an individual would use their own experience to make that assessment.”

STREAMLINING COLLECTIONS

AI and machine learning can also be used in the actual AR process as well. Shankar Bellam, manager, solution engineering for HighRadius, noted that one of the biggest challenges for AR teams is the huge amount of customer transactions that they have to manage. “In the B2B space, where you have hundreds of thousands of customers, you have millions of invoices and you really have to go after every transaction,” he said. “So that’s where a lot of time is being spent by having AR teams manually going after invoices and trying to get paid or trying to figure out why the customer has not paid on something.”

While RPA allows AR to automate the repetitive, manual tasks, machine learning algorithms can be used for more intricate work, like identifying patterns in transactions. This can help your organization in its decision-making and really tailor the behavioral change that you want to bring about in your customers, Bellam explained. “Maybe you want to change the way you collect from the customer, or maybe change the amount of manpower that you assign to a certain segment of customers,” he said.

HighRadius works with a number of top-tier companies, like Nike, Adidas, Johnson & Johnson, Walmart, and Proctor & Gamble. These companies each manage a massive amount of transactions, going across currencies in different regions. “So you do not have a ‘one-size-fits-all’ solution for disputes, credits or cash application,” Bellam said. “You really have to use machine learning algorithms to identify each customer’s different payment patterns and deductions behaviors that they’ve had in the past.”

Improving efficiencies in the collections space can have a positive effect on cash flows. “Because of the sheer volume of these organizations and the magnitude of the size—even if they’re bringing in one day of [days sales outstanding (DSO)] improvement—it’s huge in terms of dollar values for these companies,” he said. 

DATA CONSOLIDATION

AI can also help treasury as it consolidates copious amounts of data from ERP systems, TMS and other bespoke sources when doing cash forecasting. “Everyone cares about the accuracy in the end, but the process to get there is quite cumbersome in most cases—to get your hands on the data, then to mix all the data, and to make something out of it that makes sense,” said Nicolas Christiaen, CEO and co-founder of Cashforce, who discussed AI and ML in an AFP 2019 session. “And then there are the people involved; you don't want to have 60 people tripping over each other to make that forecast.”

To create the forecast, treasury needs to consolidate the data correctly to make sure it is getting the right data sources from the TMS and ERP systems. To improve the forecasting process for its clients, Cashforce looks at historical GL (general ledger) data. “We try to analyze the data historically to come up with insights; if you augment on top of the data you already have, you can make this smarter,” Christiaen said.

He added, as an example, that attempting to forecast based on due dates for payments is pointless, as customers rarely pay precisely when you require them to. Hence why it is so important to look at the historical trends and patterns, if available.

The final step in Cashforce’s process is what is called its back-testing algorithm. Based again on historical data, Christiaen’s team looks at how good a system-based forecast measures itself against the actuals. “We’re trying to understand the historical deviation of your system’s forecastable data versus the actuals to come up with a segmented variance, and then inject that on top of the current forecast,” he said. “To summarize, I would say it’s using different smart data sources and different smart algorithms which will optimize the accuracy of your forecast. With the back-testing algorithm, we’ve just scratched the surface, in my opinion, of what we can potentially do.”

For more insights, download the AFP Executive Guide on Emerging Technologies, Part 2: Artificial Intelligence and Machine Learning, underwritten by Kyriba.

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