Last week in the US, the Federal Open Market Committee (FOMC) held its November meeting.
That meeting didn’t really contain any surprises, and most market participants still expect the next interest rate hike in December.
Earlier in the year, the FOMC signalled that four rate rises were likely in 2018. We have had three so far. In Europe, the ECB is forecast to begin raising rates (albeit slowly) in 2019.
In an environment of rising interest rates, the accuracy of corporate cash forecasts comes into sharp focus because of the dual effects of the direct cost of debt, and the opportunity cost of holding uninvested cash.
It is the cost of debt, however, that will be foremost in the majority of corporate treasurers’ minds. Corporate debt is currently at record highs and, while not necessarily a cause for concern in its own right, this does increase the exposure and sensitivity to interest rate moves.
The corporate treasurer’s best defence in a rising rate environment? The cash flow forecast.
The three steps of increasing cash forecasting accuracy
The accuracy of cash forecasts can be continually increased through a cyclical process of measuring, analysing, and adjusting. However, measuring cash forecast accuracy can be a challenge in itself.
Step one: measure accuracy
The first step is to measure the accuracy of as many parts of the forecast as possible.
In large organisations, cash forecasting requires the collection of vast amounts of data from a wide array of sources. Due to the volume of data points, measuring the accuracy of a cash forecasts is not a straightforward task.
As actual cash data is required to measure the accuracy of forecasts, the level of data available to a treasury or finance team will determine which elements of the forecast can be analysed. For example, if only closing cash balances can be accessed, it is only the closing cash positions in the forecast that can be measured. Whereas if transactional cash flow data is available, other components of the forecast, such as operating and investing cash flows, will be able to be measured for accuracy.
Choosing what to measure, and how frequently to measure it, is the first step in understanding forecast accuracy.
Step two: perform data analysis
The next step, after collecting the accuracy measurements, is to perform data analysis.
Analysing closing cash positions and net cash movements will provide a good gauge of overall forecast accuracy. Cash generation and working-capital based analysis will give a better understanding of cash flow performance, although, as stated above, this will require more forecast and actual data.
Picking the key metric for analysis is a good starting point, and will direct focus on the element that matters most to the business.
Step three: adjust the process
The insights uncovered in the analysis enable adjustments to be made to the process that improve the accuracy of the forecast.
For example, if the analysis showed that the forecast was continuously over or underestimating the closing cash position, the cause of this could be examined and then corrected.
To take another example, if one particular element of the forecast is showing wild fluctuations in forecast accuracy, that element can be examined and adjusted. For instance, if accounts receivable forecast figures had more volatility that any other element of the forecast, it might be that there is some corruption in the AR data being fed into the process.
A process of iteration
Put simply, accuracy measurement provides the data, analysis of the data provides the insights, and the insights enable the adjustments that improve accuracy.
However, improving forecast accuracy is a process of iteration, and it is therefore important that the measurement and analysis is conducted on an ongoing basis.
Use software to achieve best practice
While many companies still manage their cash forecasting processes using manual, administratively heavy tools, an increasing number are looking to software to achieve best practice.
To illustrate the practical ways specialist cash flow forecasting software can improve the process, let’s review the three steps of increasing cash forecasting accuracy we explored earlier.
- Accuracy measurement. As mentioned, measuring cash forecasting accuracy requires the monitoring of a large number of data points. Specialist software tools automate the data collation process, so all data loaded into the system (either by automated file upload or human intervention) is tracked and can be reported on with the touch of a button.
- Data analysis. Specialist software enables all of this data to be reformatted in a number of ways to enable greater analysis. As we discussed in this article on the benefits of data visualisation, trends, anomalies, and other insights are far easier to identify when presented in an appropriate graphical format.
- Adjustment. When the cash forecasting process is managed through software, adjustments can be made with a minimum of effort. For example, where assumptions have been made in the model that do not reflect the actual data, rules can be built into the forecasting model to correct for this.
The catalyst for change
As more and more companies make the switch to cash forecasting software, the benefits of early adoption begin to diminish. However, the impact of rising interest rates is likely to be the spur that drives the late majority to action.
While the risk of being a laggard in this environment could be poorly informed decision making, there is still room to stay ahead of the curve.
CashAnalytics has helped many companies across a broad range of industries to build and maintain best-in-class cash forecasting processes that produce the highest quality reporting and analytics outputs. If you would like to see a demonstration of how software and automation can overcome these challenges, please contact us directly.