Cash forecasting has been a top priority for treasurers for a long time, and their key focus is to improve forecast accuracy to monitor cash flows better.
The key to generating accurate cash forecasts is to ensure that the data for payments, receivables, etc., are accurate enough.
The data-driven approach in cash forecasting provides real-time data and insights for CFOs to make better decisions. This enhances their credibility with the external stakeholders.
The various challenges/roadblocks to data-driven forecasting are:
Most companies tend to have multiple entities, ERPs, and currencies. Gathering and consolidating data from scattered sources such as different departments, banks, ERPs, TMS, etc., is time-consuming and error-prone. As a result, the visibility of cash flows is lowered.
Historical data should be adequate and accurate since they are useful for modeling and enhancing forecasts. Some companies don’t have enough data or feed the forecasts with unreliable/stagnant data, which significantly reduces forecast accuracy.
Different industries have different peak seasons, and they need to incorporate those into their forecasts. If seasonality is not considered, the forecasts are not realistic enough for the company, which impacts liquidity.
1. Input Data:
AI is irrelevant without data. It’s necessary to feed sufficient, relevant, and good quality data for training the models and testing the results.
The processing depends on the type of data fed into the system. The choice and trade-offs between complex algorithms, complex techniques, and tools are dependent on the data, the type of output required and driven by an analysis of the data and the previous results.
3. Output data:
The output data should be relevant, actionable, and consistent. There should be a way to estimate and evaluate past performances to improve performance in the future. Moreover, the predictions and recommendations need to be consistent and readily usable, and the insights need to be available and actionable for executing confident decisions.
Typically, the warranty claims should lag behind sales by a few months. The write-offs need to be removed to enhance predictions. This can be done by using data-driven cash forecasting.
Different customers have different behavior/ payment trends that need to be taken into account while forecasting cash flows. Some accounts are easier to forecast using the weighted ADP approach, while other accounts may be complex to forecast. Incorporating each customer’s payment behavior increases cash forecasting accuracy.
Trends are captured better once external data are incorporated into the forecasts. As a result, the forecasts become more accurate and reliable.
As the manual and repetitive work gets automated, treasury can focus more on cash forecasting and re-forecasting to improve in managing liquidity. Here are some of the impacts on treasury from data-driven forecasting:
Being able to create forecasts accurately and time helps to capture real-time data and stay on top of market fluctuations.
Being able to track the cash conversion cycle faster improves governing and managing cash flows efficiently.
Reduced idle cash leads to better capital allocation and risk management.
Better and faster decisions can be made on investments, business expansions, and M&A.
The HighRadius™ Treasury Management Applications consist of AI-powered Cash Forecasting Cloud and Cash Management Cloud designed to support treasury teams from companies of all sizes and industries. Delivered as SaaS, our solutions seamlessly integrate with multiple systems including ERPs, TMS, accounting systems, and banks using sFTP or API. They help treasuries around the world achieve end-to-end automation in their forecasting and cash management processes to deliver accurate and insightful results with lesser manual effort.