Treasury analysts participate in the four core activities of cash forecasting. This is how a day in the life of a treasury analyst with a manual treasury process:
Consequently, manual process or spreadsheet-based forecast reports are inefficient, error-prone, and inherently dead on arrival when they reach the treasurer or CFO.
Treasury analysts frequently attempt to alter their existing spreadsheets to meet new business requirements. Since the reports are often dead-on-arrival and based on assumptions, firms overborrow due to financial distress since they aren’t well prepared for macro-level fluctuations.
A recent survey with 1000 Fortune companies revealed that nearly 90% of treasurers rate their cash flow forecasting as unsatisfactory, with almost all citing spreadsheets as the major drawback, followed by ‘lack of richer data’ being the other. Discover the four reasons for inaccurate cash forecasts below to increase your accuracy.
According to a report by Gartner, 40% of businesses fail to achieve their business objectives due to poor data quality. The world today generates more than 2.5 quintillion bytes of data every day. Collecting, cleaning, managing, storing, and analyzing this ever-increasing volume of data is no small feat. Businesses usually store data in different formats in disparate sources (ERPs, bank portals, FP&A systems, TMS), and aggregating this data becomes tedious in spreadsheets. On-premise ERP systems are typically installed locally on the company’s hardware and managed and maintained by IT staff, which makes it difficult for treasury analysts to operate in an accessible and known environment while forecasting.
While forecasting manually, front-line staff need to collect the data from different teams like A/R, A/P, HR, and FP&A and then consolidate all the data into a single sheet which is time-consuming. Due to this, the turnaround time in creating forecasts and reports increases, which delays decision-making from the C-suites. In addition, poor data management practices such as not storing data securely, using raw data for analysis without cleaning it, and collecting inaccurate data can all negatively impact your business and its operations. For instance, incomplete or inaccurate data makes the cash forecasting process inaccurate, which makes it challenging for treasury analysts to create and share accurate numbers with treasury managers and CFOs.
Each customer has a unique payment pattern and behavior. Also, different invoices may have different currencies.
When predicting accounts receivable using spreadsheets, treasury analysts add variables like invoice date and average payment date but fail to add more variables to predict payment dates accurately due to the limitations. Adding 50+ variables provides a more realistic and holistic view. But, it is difficult to manually tweak as many as four variables, let alone track 50+ variables using spreadsheets.
According to the Cash Forecasting & Visibility Survey by Strategic Treasurer, 91% of treasury analysts still use spreadsheets for forecasting and face challenges in getting an accurate forecast.
However, spreadsheets can’t factor in all the necessary forecast data, such as changing FX rates, seasonality/business cycles, and macroeconomic fluctuations. This reduces the accuracy of a cash forecast. The frequency of forecasting can’t be increased enough due to lean treasury teams and most of their time getting consumed on manual data aggregation.
Lack of bandwidth hinders treasury analysts from performing variance analysis, especially when using manual tools like spreadsheets.
For most organizations, treasury analysts gather variances at the end of every year in spreadsheets by comparing past year actuals to current year actuals. Without regular analysis to understand the variance drivers and identify high variance categories, the forecast accuracy can’t be improved.
AI-Based cash forecasting software helps treasury analysts perform the following tasks easily:
This interface enables smooth data transfer, allowing treasury analysts to easily collect real-time data from multiple entities with different technology environments and integrate them. This eliminates issues such as data incompleteness or lack of data quality (inaccuracy). Additionally, with cloud workspaces, where treasury analysts, managers/execs, and other teams can share inputs and feedback and build forecasts together. This helps analysts analyze data and quickly create forecast reports for the CFOs.
With AI-based software, treasury analysts can perform historical analysis and regression analysis to understand the payment patterns and predict customer-specific payment dates by supporting multiple customer and invoice-level variables.
Treasury analysts can also adjust time horizons and increase the forecast frequency. This allows them to perform cash flow forecasts for multiple durations and win more credibility at the CFO’s office.
AI-Based software uses a closed-loop feedback system that helps identify past mistakes and improve results. It supports reusing the AI model’s predicted outputs to train new versions of the model and gives analysts the information they need to alter their forecasts accordingly.
A billion-dollar construction firm tackled liquidity shortages for 1800+ projects using automation and forecasts with 94% accuracy. They initially lacked project-level visibility, and a poor reporting system generated workflow difficulties, inadequate liquidity planning, and cash shortages.
But with HighRadius Cash Forecasting Cloud, they achieved:
Talk with our solution expert today to learn more about the features of HighRadius’ Cash Forecasting Software that can benefit your company and team.
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