Revamping Cash Forecasting with Artificial Intelligence

The hurdles encountered by the treasury team while forecasting A/R are:

• Forecast inaccuracy due to limitations of spreadsheet
• High turnaround time in reforecasting
• Difficulty to gather data spread across ERPs, banks, FP&A tools
• Lack of granular visibility of forecasts.

Adding relevant variables in cash flow forecasting improves the accuracy by a large margin. But, adding more variables increases the complexity. This makes it challenging to forecast A/R using spreadsheets due to human errors and consolidating data manually. However, the challenges due to spreadsheet-based forecasting are overcome by automated forecasting. With Artificial Intelligence, more variables can be added for greater forecast accuracy and help analysts shift from menial tasks to strategic tasks such as decision-making and reporting.

Overcoming the Challenges with Artificial Intelligence

Artificial intelligence provides the following benefits in cash forecasting:

• Accurate prediction of the payment date for an unpaid invoice.
• Automated capturing of all the necessary financial data from TMS, banks, or spreadsheets.
• Automated mapping of historical data with current data to analyze trends and patterns
• Ease in making frequent adjustments to the forecast

But, it is crucial to understand which models should be used for the various operating and non-operating expenses. For instance, Heuristic models are used for payroll, expenses, CAPEX, investment & debt, etc, whereas AI models are used for forecasting unpredictable factors like A/R and A/P.

With the help of AI, forecasts can be rolled up from regional to a global level for various entities, company codes, categories, subcategories, etc.