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Using Predictive Analytics for Cash Flow Forecasting

04.08.2020

Medium- and long-term cash forecasting is only as good as your business sales and operation planning. The inputs into this model are typically gathered through a planning process where the sales and operations teams define long-range forecasts, and then the finance team translates into financial statements. Cash flow is no exception to this process, and it often requires intervention by financial analysts due to lack of data 

As a company develops its cash flow model, it may rely heavily on historical information to explain what has happened in the past (descriptive analytics). As the tool becomes more mature, insights from the model can show why something happened and define whether that event is likely to happen again or be resolved. For example, missing a debt repayment because a large customer didn’t pay on time could be the rationale. The action from the insight may be that the company must retain a greater cash balance, choose to implement a late payment penalty, or negotiate completely new terms with their supplier. 

Predictive analytics enables the cash flow model to move to the next maturity level. This type of analysis allows for lessons learned from past collection and payables behavior and can account for economic impacts such as interest rate changes or supply chain shocks. A strong model can show various assumptions in a timely and straightforward way. Paired with data visualization tools, a cash flow model with predictive analytics can be a power differentiator. 

Implementing predictive analytics may not be as difficult as it sounds. Artificial intelligence and specialized software may be more than what your business needs to start the journey. Key steps to getting started include: 

  • Gain a deep understanding of existing data, cleansed and streamlined as much as possible 
  • Build a historical model to track the results and begin analyzing as a comparison to the forecast 
  • Determine critical trends, clients and economic assumptions to be modeled 
  • Choose the most appropriate statistical model 
  • Develop a dashboard that can illustrate changes and bring insights to leadership 
  • Champion a cash culture and routine forecast review 

If you are interested in learning more about how to use predictive analytics in your cash flow model, please contact Amy Julian, Director, Advisory Services, at ajulian@bswllc.com or 314.687.2314. 

Case Study: 

As part of the cash forecasting task force at a Fortune 500 firm, our team built a model that utilized contract commodity escalation factors to predict cash flow related to payables. Each month, the commodity received a prior period adjustment based on volume and actual commodity prices. That was offset by any hedge activity that the Treasury team had undertaken. The model used several regression factors to determine correlation, and then used industry and economic forecasts to predict historical values. As part of a wider cash management strategy, the initiatives enabled the variance to forecast to be reduced from 10% to under 5%. 

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