5 AI Use Cases of Cash Forecasting and Cash Management

Accounts Receivable (AR) forecasting is dependent on the customers who may or may not always stick to agreed-upon payment terms. This adds an element of uncertainty to the process which makes AR the most challenging category to forecast for treasury professionals.

AR management becomes increasingly difficult as a company grows larger. For an enterprise, it becomes complicated due to scattered data across many business units. Companies may face various issues, including poor receivables administration and time-consuming reporting processes.

Several factors like seasonal trends, business cycles, credit scores, customer payment behavior, and disputes and discounts increase unpredictability in A/R forecasting, leading to low accuracy.

AI extracts all relevant data from ERPs and analyzes it to create granular forecasts for AR cash flows.

Advanced AI models predict invoice payment dates using customer invoice data from ERP like the average days it took to pay invoices and specific business seasons when invoices are paid faster or slower. AI models also identify and track key factors that influence customers’ payment rates like seasonal business changes and invoice amounts to help create forecasts.

Similarly, AI and ML models help in predicting the time period beyond most open invoice dates using sales order data from the ERP. Treasury management solutions also integrate with ERPs to automatically override forecasted invoice payment dates based on the customers’ promise-to-pay dates.

Accounts payable forecasting is accurate in the short-term, up to the next 2 to 4 weeks. However, due to the uncertainty surrounding payments, accuracy suffers in the long run. With the limitation of spreadsheets, treasurers also have to deal with the unpredictability of accounts payables while creating a cash forecast.

An AI-based cash flow forecasting system would compare historical and recent data and run scenarios using different AI algorithms, selecting the most optimistic and realistic cash prediction to produce an accurate AP estimate. This allows treasurers to anticipate expenses that may happen throughout the forecast period and cost fluctuations.

Integration with ERPs and Treasury Management Systems (TMS) allows AI models to gather historical and recent data like open vendor invoices and purchase orders to make predictions for the time period beyond the open purchase bill dates and payments to vendors.

Scenario modeling involves creating different financial scenarios based on assumptions about various factors that can impact cash flow, such as changes in revenue, expenses, and market conditions. This allows companies to model different outcomes and develop contingency plans for managing cash flow in different scenarios.

On the other hand, scenario analysis in cash forecasting involves analyzing the impact of specific events or factors on cash flow. This typically involves identifying risks and opportunities and then evaluating the potential impact of those factors on cash flow.

Scenario Builder – Users can create scenarios on top of a base forecast with an easy-to-use builder interface. They can change amounts, percentages, or timing of cash inflows or outflows or FX rates.

Forecast Snapshot Comparison – Users can save a version of the forecast as a “snapshot” and access it at any time in the future. Users can compare any two snapshots side-by-side with differences highlighted in a visual “heatmap,” including a base forecast against a scenario forecast or two scenario forecasts. Users can also compare multiple snapshots generated from different forecast sheets or scenarios in one chart view.

AI-based scenario building and analysis help treasurers prevent losses through proactive analysis, improve investments and optimize returns, recognize and prepare for cash shortages, allocate financial surpluses effectively, and control foreign exchange risks.

Variance analysis is a quantitative method of assessing the difference between estimated budgets and actuals. In cash forecasting, variance refers to the difference between a cash forecast and the actual cash position for a particular accounting period. The root cause analysis of the deviation between the forecasts and actuals helps to identify the areas that need correction. It also helps in budgeting accurately, regulating risk, and forward-thinking to implement proactive decisions.

Enterprises typically have a lot of cash flow data, which makes it challenging for treasurers to create low-variance forecasts, especially with manual methods like spreadsheets. The disadvantage of manual variance reduction approaches is that they frequently produce a variance between 20-25% and require a significant amount of time, effort, and money.

To spot, report, and fix the reasons for forecast variances, cash flow forecasting software drills down into forecast variances across complicated cash flow categories like A/R and A/P, regional, and company level. Additionally, AI further revises the forecast by evaluating historical and current forecasts and the high variance categories across various horizons such as monthly, quarterly, and yearly to raise the accuracy of the cash forecast by 90-95%.

With historical cash inflows and outflows from bank statement data, treasury solutions use Auto-Machine Learning to automatically create daily cash forecasts.

Best Fit Models – The system reviews all prior bank transactions in each cash flow category and selects the modeling method with the highest prediction accuracy for the category at that point in time.

Daily Model Selection – For each time period (day, week, or month) in the forecast, the system reviews and updates the best-fitting model automatically.