Discover the four blind spots in cash forecasting. Learn the best practices to capture those blind spots to boost cash forecast accuracy.
Blind spots in cash forecasting are areas that treasury teams often miss out on capturing due to limitations in data, technology, or drill-down capabilities. If the blind spots are not incorporated into the cash flow forecasts, it leads to:
Cash forecasts that are subpar or incomplete.
Cash forecasts that are low on visibility and accuracy.
The examples of blind spots in cash forecasting are as follows:
Numerous factors make it difficult to incorporate blind spots in cash forecasts. The reasons are stated below:
Without having a historic analysis of the customer’s past payment patterns, it is difficult to predict payment due dates. Using spreadsheets to track customer payment dates is inefficient as it limits the use of multiple customer-level variables. Sometimes payment behaviors can’t be tracked accurately through the average days to pay or ADP approach, since the accounts might have nuances that are too complex to capture. The repercussions of not tracking customer data accurately are:
Black swan events can lead to a cash crunch without proactive planning.
The best practices to capture the blind spots are as follows:
1. Customer data: Customer data can be gathered automatically using technology. The payment patterns can be captured with the help of Artificial Intelligence. AI performs a historical analysis and a regression analysis to understand the historical patterns associated with complex cash flow categories such as A/R and A/P and predict payment dates.
The figure below shows two curves, the one on the top right shows a curve that is spread out due to inefficient modeling. The curve below is less spread out and is sharper due to efficient modeling.
2. External data: Incorporating the external data into AI-powered forecast models refines the forecasts, thereby enabling CFOs to take proactive measures.
The figure below shows two graphs. The graph on the top right shows the forecast without incorporating the influence of key raw material prices. The other graph shows improvement in the forecast by incorporating the influence of key raw material prices.
3. Seasonality: Seasonality can be incorporated into the forecasts through time series algorithm and predictive analytics. The time series algorithm prioritizes the recent trends over historic trends to build accurate cash forecasts.
Through accurate time series modeling, treasury can comprehensively understand seasonal fluctuations. This makes it easier to capture market trends, maximize profits, stock-up for peak seasons, or prevent cash crunches through proactive borrowing during the off-seasons.
This is how the seasonality curve looks like in cash forecasts:
4. Variance: AI helps in identifying variance between forecasts and actuals for long-term and short-term. Moreover, variance analysis can be performed for multiple durations across the entity, region, and cash flow category levels.
The closed-feedback loop reduces variance to achieve accurate forecasts by allowing AI systems to understand errors in past forecasts, and adjusting the parameters to perform better in the future.
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