Cash Flow Forecasting: Failures and Cause Analysis
Improve the accuracy of your cash projection reports by leveraging AI to predict invoice level delinquency of your outstanding receivables
Cash Flow Forecasting: Failures and Cause Analysis
Ensuring accurate cash flow projection is no mean feat! As per a survey by Kyriba, Six in 10 treasurers think that their cash flow forecast has either “significant” or “major” inaccuracies. The consequences of this lack of accuracy can’t be overly emphasized and can cause a company to require a lock-up huge reserves of cash. When many millions or billions of dollars are at stake, this lack of cash visibility can have a dramatic impact on how much “idle” cash an organization is forced to have on hand, to cover for unknown cash needs. The knock-on effect from this is that a company’s cash can’t be used optimally, for instance, for paying down debt or funding growth or M&A initiatives.
The following enlists the 4 primary causes of the same:
Use of Antiquated Systems
Despite the potential for error, the majority of treasurers continue to rely on Excel spreadsheets for their forecasting requirements. Some companies opt to develop their own cashflow forecasting tools based on existing systems. However, that needs extensive IT resource investment. Even with a straightforward system, another key challenge is to ensure adequate training for the staff.
Incoming Receivables Based on Static Assumptions
The Credit, AR, and Collections teams use static estimation metrics such as average days delinquent (ADD) and consider other static factors such as customer type- large or small, payer type- regular, fast, or slow to predict the payment date for each customer. However, ADD does not give a dynamic picture of customer payment behavior.
Cash inflows are added to the projection based on these payment date predictions. As a result, inaccurate data on incoming receivables based on static assumptions leads to inaccuracies further trickling down into the cash flow forecast.
Misleading Averages
Basing your cash flow forecast on an average estimate of can come back to bite you if that average suddenly changes. For instance, in AR, when a customer takes 29 days on an average to pay, after being invoiced and then suddenly begin stretching payments to 45 days, it can hit the business’s cash account pretty quickly.
Disparate Data Sources
One of the keys to creating accurate cash flow forecasts is having historical data about revenues and expenses. Furthermore, having enough historical financial data to capture the effects of seasonality (such as inventory purchases ahead of a busy season) and occasional uses of cash (such as ad campaigns) provides a clear pictures of expenses. However, businesses typically do not have a centralized repository to store all the information. Data integration is a critical pain-point in cash flow projection.
