Forecasting and managing cash flow and liquidity is a high priority challenge for any organization–and is particularly critical for small and midsize businesses (SMBs), which tend to have more limited reserves from which to pay bills and fund operations. 

But whatever the size of the company, an effective cash and liquidity management strategy must include an accurate and efficient method of ingesting and processing financial outflows in the form of employee expenses and other B2B purchases. 

Despite the importance of having a clear, complete view of such spending, many companies–small and large alike–remain mired in outdated, manual expense reconciliation processes that are rife with inefficiencies, prone to error and occlude valuable data that otherwise could offer key business intelligence.

Amid the wider context of a rapidly digitizing payments and financial services landscape, this legacy expense management model–which often requires employees to self-report spending and complete tedious, time-consuming expense reports–is growing increasingly unsustainable, noted Albert Bodine, director of commercial and enterprise payments for Javelin Strategy & Research.

“For CFOs across the board, from small companies all the way up to the Fortune 500, the number one most difficult thing for them to do is to forecast cash flow and liquidity,” Bodine said during a recent webinar hosted by PaymentsJournal. “So to even think about attempting to do that without proper analytics–I would defy anybody to be able to do that in this day and age.”

How SMBs are Leveraging Technology for Better Liquidity Management

Businesses that still rely upon outdated legacy expense management systems may do so for a variety of reasons, depending on the size of the organization, added Galileo Financial Technologies head of B2B Aaron Bright. 

“You have small businesses that are just trying to ramp up and start to understand how to manage their business,” Bright noted during the webinar. “And then you have really large businesses that may have a legacy financial institution doing their treasury services, that sits atop a legacy platform that may not be able to automate and provide data in a way that’s ingestible in real-time.”

In contrast, Galileo’s position as an issuer processor for payments enables the company to transmit card purchase data directly into companies’ expense management or enterprise resource management (ERP) systems, added Bright. 

“We’re directly connected to the [card] network data from transactions, so we can automate that data to flow into an expense management platform, which helps improve accuracy, reduce the risk of error, speeds up the processing of expenses and helps with transaction reconciliation,” Bright said.

[Webinar] Unlock the Power of Data and Automation to Optimize Expense Management

Accurate, complete expense data is a highly valuable resource to companies, providing useful spending insights that can be leveraged to reduce cost overruns, negotiate better deals with suppliers and drive other efficiencies that enable an organization to unlock additional funds and improve regular cash flow, Bright added. 

To further enable this enhanced spending data visibility, Galileo recently began offering client fintechs the ability to connect their business customers to Mastercard Smart Data, an expense reporting and analytics suite automatically ingests detailed purchase information into ERP and expense management systems. 

Looking ahead, Bodine cited rapid advances in Artificial Intelligence, which promise new and powerful ways of forecasting cash flow and guiding other liquidity planning for businesses. But these AI-based tools will only be as effective as the expense data being fed into them–further emphasizing the need for that data to be complete and accurate. 

“The advent of AI will help support companies’ expense management in a greater way, with historical transactional data able to provide insights to potentially reduce costs and improve efficiencies and offer other cash flow management solutions,” Bodine noted. “But it’ll [require]  starting with a basis of good transaction data.” 

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