Revamping Cash Forecasting with Artificial Intelligence

What you’ll learn

Financial leaders stated cash forecasting inaccuracy as the greatest hurdle. Learn how automation helps in generating highly accurate forecasts and what additional challenges it can overcome.


According to a survey, 89% of Treasurers stated Cash Forecasting as their top concern and 83% of Treasurers stated Cash Forecasting inaccuracy as their greatest concern. Some of the factors that contribute to inaccuracy in cash forecasting are:

  • Sub-par technology that doesn’t meet cash forecasting goals
  • Inadequate time to carry out forecasts frequently
  • Insufficiency in gathering information from the right sources
  • Lack of variance analysis to understand the deviation between the forecasts and actuals
  • Lack of granular visibility into unit-level forecasts

The greatest cause of cash forecasting inaccuracy is the unpredictability of Accounts Receivables. 57% of Treasurers stated A/R as the toughest category to forecast, followed by A/P. However, because of inconsistent payer behavior during the pandemic, A/R Cash Forecasting has become a top priority to focus on.

Challenges Encountered in A/R Forecasting

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.

Benefits Provided from Automation

Automation is key to generating forecasts frequently to understand daily cash positions and taking proactive measures for future financial positions. Here are the following areas where automation helps you reap benefits:

  • Data gathering: Extracting data from various sources is done automatically.
  • Accuracy: Performing end-to-end forecast with utmost accuracy.
  • Decision making: Making confident decisions on debt, investments, etc, to utilize free cash better.
  • Reporting: Being able to present the information accurately, and drive investor confidence.
Gather more insights on how automation helps in building more accurate forecasts by applying the best-fit curve to your cash forecasting process.
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HighRadius Cash Forecasting Cloud – an advanced forecasting system – leverages the proven RivanaTM Artificial Intelligence (AI) platform to provide the most accurate cash flow forecasts – right from a ledger account level and rolling up to the organizational level. Delivered as a Software as a Service (SaaS), the solution seamlessly integrates with your company’s ERPs, accounting systems, banks and order management systems. Multiple AI and Machine Learning algorithms process datasets including bank statement inflows/outflows, sales orders/customers invoices, purchase orders/vendor invoices and expense reimbursements for comprehensive as well as accurate cash flow forecasts. The closed-loop, machine learning feedback system ensures that the forecast models become more accurate with time.