Cash Flow Forecasting That Sees The Forest From The Trees

There’s the forest and there’s the trees — and a lack of ability to see both at the same time can often lead to disaster. The same principal holds true when it comes to forecasting cash flow. Indeed, some 91 percent of corporate treasurers lack visibility over payments — hardly a statistic that breeds optimism.

In a new PYMNTS interview, Jessica Cheney, vice president, product management and strategic solutions at Bottomline Technologies, talked about the importance of improving that cash flow situation, and the role intelligent technologies can play. The traditional task of cash flow forecasting supplemented by machine learning and artificial intelligence, “can provide users with more accurate  visibility into future cash liquidity positions and suggested actions to take based on those predictions,” she said.

Cash Flow Changes

Financial Institutions are experiencing significant changes (and potential improvements) when it comes to managing and forecasting cash flow, with new tools and technologies entering the market — with many designed to appeal to small and medium-sized businesses (SMBs). As that happens, SMBs are shifting their cash management strategies. Indeed, amid talk of declining optimism and an impending recession in the U.S., the latest evidence has suggested that entrepreneurs are holding onto their financial confidence — though it may be changing the ways they manage company cash.

After all, as Cheney pointed out, no business should leave excess cash just sitting around in bank accounts, doing meager work toward the bottom line. Nor should companies, whether SMBs or bigger, miss chances for new opportunities because they have limited views into their future cash flow.

That’s where such technologies as machine learning (ML) and artificial intelligence (AI) come in. Instead of company-crafted spreadsheets — which take what she called a “ limited linear, historical view,” and can be a very manual-intensive process, ML and AI can spot often hard-to-detect patterns that impact cash flow. “They can also take into account inconsistent things like seasonality,” she said, and offer additional analysis based on global economic and political conditions — all factors that play a  role in all kinds of industries, including retail, manufacturing and agriculture.

Not only that, but intelligent cash flow forecasting can look at “things like typical outstanding invoices,” she said, and using the standard metric of days sales outstanding can determine the impact of how businesses get paid or influence the behavior of how and when businesses make their own payments.

Cash Flow Complications

The volumes of data sources used in cash flow forecasting do add to its complexity.

When it comes to cash flow in general, “Companies find it hard to to get a consolidated picture of their receivables information,” Cheney told PYMNTS. As well, “It’s a little difficult to accurately predict when payments will arrive if you are offering  your customers multiple ways to pay. Each payment type has its own time frame associated with available funds, in addition to payment types that differ in the amount of remittance information that can be sent with the payment, so the matching of the payment to the outstanding invoice is more complex.”

Multiple bank relationships — 32 percent of treasurers report six or more banking relationships — can also cause the process of cash flow forecasting to be time consuming.

But intelligent cash flow forecasting can, in her telling, “automate the gathering of information” from a variety of sources. “That’s part of the value  of an intelligent solution versus a static spreadsheet,” Cheney said.

The use of machine learning and artificial intelligence, at least in general, still has much room to grow when it comes to all types of financial and banking services, a truth that especially applies to AI, as demonstrated by PYMNTS research. And that can certainly have an impact on improving cash flow forecasts — not only making them more intelligent, but also quicker. (In fact, Cheney said there is a “close relationship” between intelligent and instant cash flow forecasting, though she said the intelligent part was more important.)

In the future, though, she said “smart banks will look for ways to help business become smarter” about cash flow and its impact on their business decisions. The digital world is all about data and intelligence and automated analysis — and that as true for cash flow forecasting as it is for anything else in commerce and payments these days.