Data-Driven Approach to Improve Cash Forecasting Accuracy

Why is data important?

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.

Importance of data-driven cash forecasting

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.

Challenges to data-driven cash forecasting?

The various challenges/roadblocks to data-driven forecasting are:

• Scattered data across disparate sources:

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.

• Lack of adequate data: 

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.

• Neglecting seasonality in forecasts:

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.

Three pillars of data framework

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.

2. Processing:

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.

Examples of data-driven forecasting

• Non-trade deductions(warranty claims) vs. sales: 

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.

• Varying customer payment behavior:

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.

• External data such as key raw material prices:

Trends are captured better once external data are incorporated into the forecasts. As a result, the forecasts become more accurate and reliable.

Impacts of data-driven forecasting

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:

• Reduced forecasting turnaround time:

Being able to create forecasts accurately and time helps to capture real-time data and stay on top of market fluctuations.

• Effective cash management:

Being able to track the cash conversion cycle faster improves governing and managing cash flows efficiently.

• Reduced idle cash:

Reduced idle cash leads to better capital allocation and risk management.

• Increased strategic investments:

Better and faster decisions can be made on investments, business expansions, and M&A.