How AI Can Revolutionize Cash Forecasting in 2024

Spreadsheet errors can lead to significant losses, with one in every five major corporation treasurers experiencing such setbacks. While larger corporations may withstand these financial hardships, small to mid-sized businesses can be severely impacted.

In today’s data-driven world, organizations face the challenge of effectively gathering, managing and extracting value from the vast amount of data generated daily, which amounts to 2.5 quintillion bytes. Comparing data from recent years to the current available data reveals exponential growth. However, handling and storing this immense volume of data becomes a challenge.

To overcome these obstacles, businesses need robust solutions and tools to ensure accurate financial management and harness the potential of their data. By adopting advanced technologies and implementing efficient data management strategies, companies can mitigate the risks associated with spreadsheet errors and effectively handle the ever-increasing volume of data.

Lack of global visibility hinders confident decision-making to manage idle cash, which could otherwise be used to buy fixed assets, buyback stocks, etc. If cash forecasts fails to predict cash surpluses, businesses lose out on investment opportunities in the share market.

The accuracy of Accounts Receivable (AR) forecasting is influenced by customer behavior, as they may not always adhere to agreed-upon payment terms. This introduces uncertainty and makes AR the most challenging category to forecast for treasury professionals.

As a company expands, managing AR becomes more complex. Data is scattered across multiple business units, posing difficulties for enterprise-level AR management. Companies may encounter issues such as inefficient receivables administration and time-consuming reporting processes.

Factors such as seasonal patterns, business cycles, credit scores, customer payment behavior, disputes, and discounts contribute to the unpredictable nature of Accounts Receivable (AR) forecasting, resulting in low accuracy.

To address this, AI technology is leveraged to extract relevant data from Enterprise Resource Planning (ERP) systems. Through analysis, AI generates detailed forecasts for AR cash flows.

Advanced AI models utilize customer invoice data from ERPs, considering average payment duration and specific business seasons when invoices are settled faster or slower. These models identify and monitor key factors influencing customer payment rates, such as seasonal business changes and invoice amounts, to enhance forecast accuracy.

Furthermore, AI and machine learning (ML) models assist in predicting payment dates beyond the open invoice period using sales order data from the ERP. Treasury management solutions integrate with ERPs to automatically update forecasted invoice payment dates based on customers’ promised payment dates.

AI and ML models also aid in forecasting the duration beyond the typical open invoice dates using sales order information from the ERP. In addition, treasury management solutions integrate with ERPs to automatically adjust projected invoice payment dates based on customers’ promised payment dates.

An AI-driven cash flow forecasting system compares historical and recent data, running different scenarios using various AI algorithms. Selecting the most optimistic and realistic cash prediction generates an accurate estimate for Accounts Payable (AP). This empowers treasurers to anticipate expenses throughout the forecast period and fluctuations in costs.

Integration with ERPs and Treasury Management Systems (TMS) enables AI models to gather and analyze historical and recent data, including open vendor invoices and purchase orders. This allows for predictions beyond the open purchase bill dates and vendor payments.

Scenario modeling enables companies to create and analyze different financial scenarios by considering factors such as changes in revenue, expenses, and market conditions. This helps in developing contingency plans for managing cash flow in various situations.

On the other hand, scenario analysis in cash forecasting involves examining the effects of specific events or factors on cash flow. This includes identifying risks and opportunities and assessing their potential impact on cash flow.

Scenario Builder

With an intuitive interface, the Scenario Builder allows users to easily create scenarios on top of a base forecast. They can modify cash inflows or outflows, percentages, amounts, or foreign exchange rates to simulate different scenarios and evaluate their impact on cash flow.

Forecast Snapshot Comparison

Users can save forecast versions as “snapshots” for future reference. These snapshots can be accessed at any time, enabling users to compare two snapshots side-by-side. Differences between the snapshots are visually highlighted using a heatmap. This comparison can be done between a base forecast and a scenario forecast, or between two scenario forecasts. Furthermore, users can conveniently compare multiple snapshots from different forecast sheets or scenarios in a single chart view.

Leveraging AI technology, the scenario-building and analysis capabilities empower treasurers to proactively analyze and mitigate potential losses. It aids in improving investment decisions, optimizing returns, identifying and preparing for cash shortages, effectively allocating financial surpluses, and managing foreign exchange risks.

Variance analysis is a quantitative approach used to compare estimated budgets with actual figures. In the context of cash forecasting, variance refers to the variance between a cash forecast and the actual cash position during a specific accounting period. Conducting a root cause analysis of these deviations helps identify areas that require corrective action. It also aids in accurate budgeting, risk management, and proactive decision-making.

Managing cash flow data can be challenging for enterprises, making it difficult for treasurers to generate low-variance forecasts, particularly when relying on manual methods like spreadsheets. Manual approaches to reducing variances often result in variances ranging from 20-25% and require substantial time, effort, and resources.

To address forecast variances, cash flow forecasting software delves into complex cash flow categories such as Accounts Receivable (A/R) and Accounts Payable (A/P), at regional and company levels. Additionally, AI technology enhances forecast accuracy by assessing historical and current forecasts while identifying high-variance categories across different time horizons, such as monthly, quarterly, and yearly. This can improve the cash forecast accuracy by 90-95%.

By leveraging advanced cash flow forecasting tools, businesses can effectively identify, report, and address the causes of forecast variances, leading to more accurate financial planning and decision-making.

Treasury solutions utilize Auto-Machine Learning algorithms to generate daily cash forecasts by analyzing historical bank statement data. These sophisticated systems employ best-fit models, which examine past transactions within each cash flow category and choose the modeling method that provides the highest prediction accuracy for that specific category at a given point in time.

Daily Model Selection

To ensure up-to-date and precise forecasts, the system automatically reviews and updates the chosen model for each time period in the forecast, whether it’s on a daily, weekly, or monthly basis. This continuous model selection process optimizes the accuracy of the cash forecasts generated by the treasury solution.

Best Fit Models

By leveraging Auto-Machine Learning and employing the best-fit model approach, businesses can streamline their cash forecasting processes and improve the reliability and accuracy of their daily cash forecasts. This enables treasurers to make informed financial decisions and effectively manage their cash flow.