Cash Flow Forecasting has been a top focus area for Treasury for over a decade. It is also one of the key areas that occupy a lot of the Treasury’s time. Thus, for increasing cash forecasting accuracy, firms need to adopt automation and Artificial Intelligence.
A company is either cash surplus or cash deficit based on the cash position. The purpose of cash forecasting differs for both companies:
Cash surplus companies have plenty of cash reserves and focus on business expansion and M&As. So, these companies can do with reasonable cash flow forecasting accuracy and frequency. On the contrary, cash deficit companies focus on tightly managing cash, delaying payables, and borrowing at LIBOR rates instead of overnight sweeps. Therefore, they need to create accurate cash flow forecasts and increase forecasting cadence to prevent overdrawing from their revolver. However, using spreadsheets for cash forecasts limits forecast accuracy and frequency.
Since the data is gathered from multiple sources and teams and consolidated into spreadsheets, it consumes time, involves great effort, and increases the scope of errors. The bandwidth of the treasury employees is restricted to menial tasks rather than effective decision-making.
Spreadsheet-driven forecasts are inaccurate and not timely enough to make confident decisions.
The inherent barriers to cash forecasting accuracy and frequency are:
The impact from the spreadsheet-based forecasting are as follows:
Spreadsheet-driven forecasts limit cash flow forecasting accuracy and visibility into cash flows. So, the treasurer needs to rely on inaccurate and unreliable data to make liquidity decisions. The consequences of spreadsheet-driven forecasts can be avoided by leveraging robust technologies.
Technologies such as RPA are useful to automate repetitive and administrative tasks with accuracy and speed. Machine Learning reduces the variance between forecast vs actuals to a high degree with continuous learning. Artificial Intelligence detects trends and patterns in forecasts by capturing historical data.
AI improves cash forecasting accuracy by supporting adding multiple and customer-specific variables and picking best-fit algorithms.
1. Approach: There are two types of approach:
2. Data gathering: Data is gathered either manually or automatically from sources such as ERPs, bank portals, TMS, FP&A systems, etc. Automated data gathering straight from the source reduces the scope of errors, and the data fetched is near real-time to create up-to-date forecasts.
3. Modeling: The forecasting models differ for simple and complex cash flow categories. For, e.g. A/R and A/P forecasts, due to their unpredictable nature, need AI models, whereas heuristic models suffice for forecasting Payroll and Taxes.
With AI, multiple customer payment behaviors are factored to predict payment due dates accurately.
Moreover, AI incorporates external factors such as currency fluctuations, seasonality, inflation, etc. to get a better sales forecast.
4. Variance analysis: Closed feedback loop allows the system to learn from past mistakes and understand variance drivers to improve performance. A feedback loop supports reusing the AI model’s predicted outputs to train new versions of the model. It provides them with data that allows them to adjust their parameters to perform better in the future.
As automation and AI boost cash forecasting accuracy, Treasury can reap benefits such as:
Accurate cash flow forecasting empowers the treasurer to make confident and strategic liquidity decisions in a timely fashion. As a result, the market value of a company increases due to the increased trust from investors.
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