SMEs Turn to Predictive Analytics to Improve Accuracy of Cash Flow Projections

 

Predictive analytics is redefining the nature of financial modeling. CFOs from SMEs have discovered that algorithms that help model customer behavior and mine financial data are particularly valuable for making cash flow projections.

The Basis for Using Predictive Analytics for Cash Flow Projections

Every CFO has a number of responsibilities. One of the most important elements in their job description is making cash flow projections. Cash flow projections are important for a number of reasons:

  • Cash flow projections help with internal planning and budgeting. Every organization depends on cash flow projections to estimate staffing and inventory needs.
  • Cash flow projections are an important barometer for future market prices. Many investors will be more optimistic about taking a long position with a company if cash flow projections are higher than anticipated. On the flipside, market prices will fall if actual revenue falls short of projections.
  • Cash flow projections are essential for setting timetables for making capital expenditures.

Although cash flow projections are vital to the fiscal stability of any organization, they are also difficult to make. According to one survey of treasure professionals, only about a third of projections are on target. Approximately 8% are regarded as “very inaccurate.”

SMEs are looking for more reliable tools to improve the accuracy of their cash flow projections. New advances in predictive analytics are making this job easier. They are helpful because they:

  • Can make more accurate cash flow projections by forming a better understanding of customer behavior
  • Can update projections in real-time to reflect new developments and a better understanding of the correlation between cash flow and certain variables that go into their revenue models
  • Identify risks with historical data that can help key decisionmakers implement better strategies that lead to better cash flow outcomes

The need for predictive analytics to change cash flow projections in real-time is especially important. An unexpected change in the market can render previous projections useless. Many experts believe that the demand for VPNs will increase by 200%, which is a much more optimistic forecast than they initially set. Part of this change stems from a growing number of cybersecurity threats.

Lucas Johnson of PrivacyAustralia.org, a grassroots effort to increase cybersecurity for Australians, spoke about why VPN use is set to skyrocket. “The Wannacry ransomware attack in 2017 was a wakeup call for lots of people,” Johnson stated. “This malware hit 230,000 computers in 150 countries. It’s no coincidence that VPN use ticked up noticeably after that and continues to climb.”

There are numerous benefits, but there are still some challenges that they must overcome. Despite the limitations, CFOs have been able to create highly effective predictive analytics methodologies to forecast cash flow.

What is the state of predictive analytics in forming cash flow projections?

Predictive analytics is used for a variety of purposes from assessing future market demand to responding to security threats. Predictive analytics algorithms can be applied for modeling future cash flow estimates.

Numerous organizations have found ways to manage their cash flow models with predictive analytics. However, their technology is not nearly as holistic as they would like. Earlier this year, Gourang Shah, the Head of APAC Solutions and Outbound Sales Treasure Services for J.P. Morgan discussed some of the struggles and opportunities that predictive analytics offers for many organizations that depend on it for setting financial projections.

The biggest challenge is dealing with non-standard data types. Most predictive analytics modeling platforms require uniform data types. They have difficulty processing unstructured data. Hadoop-based systems that could standardize their unstructured data are available, but not easily accessible to SMEs on smaller budgets. They must turn to other solutions that are more cost-effective.

Shah writes that these organizations can still develop new predictive analytics solutions to manage cash flow modeling. Rather than depend on internal data lakes that are composed of both structured and unstructured data sets, they try to find a more seamless approach to model it.

“But the process of creating a data lake – a storage repository that holds a vast amount of raw data – to run analytics tends to be costly and onerous for many organizations. A more immediate step may be to leverage the analytics services offered by banks on transactions already flowing through their networks. This not only provides meaningful business insights to treasury, but also helps companies visualize and plan for longer-term data strategy.”

Shah stated that a growing number of organizations have started using interactive transaction analytics technology to improve their financial modeling. Treasury professionals still feel that it would be ideal to create a more automated modeling option, but the costs are still too prohibitive for smaller organizations.  Setting up interactive transaction analytics dashboards is a simpler and more reliable approach.

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