Insight & Analysis

Automating the cash forecast

Published: Nov 2022

Effective cash flow forecasting is particularly important in a challenging economic environment – so how can treasurers improve the forecasting process?

In today’s environment, effective cash forecasting is more critical than ever. While the importance of forecasting for different firms may differ, in general “we tend to see clients prioritise and emphasise the importance of liquidity forecasting in times where interest rates are rising, inflation is high and there is a continued strain on supply chains,” says Niklas Bergentoft, US and Global Treasury Leader and Deloitte Risk & Financial Advisory principal, Deloitte & Touche LLP.

He adds that many of the organisations he works with are looking to maximise the use of internal liquidity, reduce external borrowing, and maximise the cash deployed towards short- or longer-term investments. “Based on what we can observe in many economies around the world, it’s likely we will see a continued focus on organisations prioritising enhancement of their liquidity management capabilities, including cash forecasting,” he predicts.

On another note, Jon Paquette, VP of US Client Solutions at TIS, observes that the strategic value of the treasury function has increased significantly in the eyes of business leaders and CFOs in recent years. “In large part, this strategic value is being derived through treasury’s ability to produce highly accurate and up-to-date cash forecasts,” he says. “However, seasoned practitioners know from experience that the accuracy and effectiveness of their forecasts depends largely on the quality of the data being used to prepare them.”

Forecasting challenges

In practice, effective forecasting is not always straightforward, as the recent Deloitte Global Treasury Survey highlighted. According to the report, half of respondents have no systems in place or use spreadsheets for cash flow forecasting, while the accuracy of the forecast is hindered by poor quality data (60%) a lack of effective tools (50%) and a lack of incentive for business units to report on forecasts (44%). A lack of the necessary skillset was also cited by 20% of respondents.

“Common challenges to achieve accurate forecasts are often related to data quality and timing of data aggregation or modelling along with lack of effective tools and the right operating model to drive forecast accountability,” comments Bergentoft. To drive a successful forecasting programme, he says, “organisations need to have the right operating model in place where all forecast input providers are incentivised and measured on the forecast timeliness and results.”

This requires cross-functional alignment and buy-in – but as Bergentoft points out, technology also has a role to play: “treasurers have a great opportunity to either leverage a TMS (for businesses where forecasting is more predictable) or build forecasting solutions that leverage data sourcing, intelligent automation, machine learning and visualisation technologies to build effective forecasts (for more complex business models).”

Advanced technology and the importance of data

In addition, says Bergentoft, the emergence of artificial intelligence and machine learning-based technologies are now more available, along with skills around data modelling that can be utilised in liquidity forecasting. “While there is an effort to build forecasting models using these technologies, they can enable more accurate forecasts based on data patterns and external data points (market data and consumer behaviour),” he notes. “As the volume of data increases, the model will be further refined, thus adjusting future forecasts.”

Given the opportunities, it is unsurprising that improving the cash flow forecast is a goal for many treasurers, with 78% of respondents to the Deloitte survey reporting they expect cash flow forecasting to become more automated in the next two to three years. But as Paquette points out, systems using advanced software can only provide the desired efficiency if the data they operate with is complete.

“To achieve this level of completeness, practitioners must often work cross-functionally with other departments like AP, AR, and procurement to identify all the proper data channels and end points,” he explains. “This collaboration usually involves a technology component, as connecting and integrating the associated systems together is crucial for automating data collection and classification.

“And while projects of this magnitude can be challenging, once invoicing, sales, and purchase order data can be properly aggregated and used to supplement treasury’s existing visibility to core cash and payments activity, the strength of their forecasting process is enhanced significantly.”

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