FinanceJuly 28, 2020

4 sure-fire ways predictive analytics improves cash analysis

Cash flow analysis in the time of COVID-19 has been … interesting.

Hands up if, in the last year, you’ve:

  • Worried a customer wouldn’t be able to pay
  • Hounded a customer whose payment was delayed
  • Delayed paying a bill yourself
  • Ensured your financing is still viable
  • Experienced production delays could impact revenue
  • Forecasted drops in demand that would impact revenue
  • Revisited variable costs 

Whether for the positive or negative, cash flow has been a hot burner issue throughout the pandemic. Keeping the lights on often meant taking swift executive action based on a one-dimensional understanding of its cash impacts.  

And there was nothing anyone could do about it.  

Come mid-March 2020, it was too late to implement a predictive analytics program that could act as an emergency generator, shining light on our true cash positions. Through the dark of the pandemic we went, and here we are, finally in a place where we can do something.

How predictive analytics for cash analysis has changed 

While predictive analytics capabilities adopted in 2019 would have been a critical aid in navigating 2020’s tumult, just two years ago, predictive technology was still in development. It required IT specialists to use, and couldn’t underlie existing finance systems. 

But as the saying goes: chaos is the birthplace of innovation. 

When it comes to cash flow, today’s leading-edge predictive analytics technology is now sophisticated enough to: 

  • be used without a data scientist
  • learn from your available data sets
  • improve data breadth and precision over time
  • underlie the software solutions you’re already using 

Here’s how leading-edge predictive analytics can improve cash analysis

Predictive analytics connects underlying correlations between KPIs

Predictive analytics uses artificial intelligence (AI) algorithms and machine learning (ML) models to identify correlations than run deeper and farther than the human eye can see. When it comes to cash flow, predictive analytics can tap into sales data, customer data, SKU data, CAPEX data, transactional data, and other granular data to create predictions that can re-direct, inform, and clarify your cash flow direction. 

Learn how predictive analytics work. Be sure to read 8 frequently asked predictive analytics questions — and misconceptions.

Here are a few examples of the cash flow predictions that predictive analytics technology can produce:

  • An increase or decrease in cash and cash equivalents
  • Cash flow from operating activities
  • Income for the period
  • An increase or decrease in working capital
  • Cash flow from investing activities
  • Capital expenditure
  • Cash flow from financing activities

Similarly, here are a few examples of scenarios you can explore using predictive analytics technology: 

  • Payment predictability: You can more reliably predict vendor payments. This provides you with the insight to identify collection actions and accounts to spend your time on. The result is more visibility into future payments and cash flow — the lifeblood of your business.
  • Payment behavior analysis: You can monitor your customers’ internal credit scoring designations based on past payment performance. This allows you to identify trends in customer behaviors and could indicate the need to dedicate more collector time and attention to accounts that are falling behind on payments. These insights provide your business yet another tool that can help you determine the best credit terms to extend to a business. 
  • Smart collections policy: With the results of your predictions, you can implement collection policies across the business more accurately. As a result of this predictability, you can standardize the cash collection process across all channels, business units and sectors. 

Predictive analytics will help you understand the factors driving cash flow 

The best predictive analytics technology doesn’t just provide you with a prediction; it also tells you why it made the prediction it did. In other words, it tells you what’s responsible for the prediction. 

For example: If the predictive analytics technology indicates that an increase in working capital is the predicted result, it would also indicate that, say, an increased growth rate and a more aggressive collection policy, are what led the AI to this conclusion.

That’s the power of “explainable machine learning.” You can learn more about explainable machine learning here.  

A note: we highly recommend investing in predictive analytics solutions that give you this transparency. The predicted result is only half the value of a predictive analytics solution. The true value comes in understanding what’s driving your numbers — so that you can push these drivers towards more fruitful outcomes.  

When the predicted logic is locked away in a black box, you’re blindly trusting a prediction with no real understanding of how the system came to its conclusion, and no power to take the reins and improve your outcome.  

You’ll conduct more probable scenario analysis

Once predictive analytics give you an understanding of the factors driving your cash outcomes, you hold the power to change the future by finding ways to influence your cash drivers. For this reason, having predictive analytics built into the mechanics of your planning software is incredibly beneficial. 

Using what-if scenario analysis, you can test various courses of action and play with your drivers in order to influence your end result. In effect, you can take control of your performance, and change the future. 

Here’s an example of how this works. 

Let’s say the predictive analytics software predicted that free cash will plummet cash flow as a result of financing activities and capital expenditures, the drivers responsible for the prediction.

You can then experiment with various financing and capital expenditures scenarios, like reducing CapEx or renegotiating your existing loans, to determine the best future outcome. 

Planning during a crisis is hectic. Here are 3 Practical Tips for Budgeting, Planning and Forecasting During Black Swan Events

It’s easier to champion a cash culture 

When you have predictions, cash drivers, and scenario analysis — all underpinned by AI and ML — it’s easier to put cash at the center of your decision making. Automation makes predictive forecasting tasks routine

Strategic action is no longer a shot in the dark, but the result of statistical probability and true cash drivers. No matter what black swan events the future holds or the limitations of your data, you’ll set your organization up for cash flow positivity, quick informed pivots, and competitive superiority by implementing a predictive analytics program today. 

Still finding predictive analytics overwhelming? Unsure if the effort is worth the reward? Read the blog, Myths and misconceptions that hinder predictive analytics' power.

Francesco Morini
Director - Global Services - Analytics and Innovation - CCH Tagetik

Francesco joined Tagetik in 2012. He has always worked in supporting Finance BI area with Deloitte first and HP later with a deep focus in Banking Industry, acquiring strong skills and competencies in most of the BI and Analytics market leader products from multiple vendors.
He was the QlikView Focal Point for HP Italy.