How AI Can Revolutionize Cash Forecasting in 2024

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What's Inside?

  • How AI-powered solutions are revolutionizing financial forecasting with precision and seamless data integration.
  • The hidden risks of spreadsheet errors that can cripple businesses, especially smaller ones.
  • How AI is transforming Accounts Receivable forecasting by predicting customer payment behavior.
  • The realm of Accounts Payable forecasting and see how AI predicts payment durations, integrating seamlessly with ERPs.
  • Future of financial planning with scenario modeling, enabling better decision-making in uncertain times.
  • Art of variance analysis, comparing budget estimates with actual figures to uncover insights.
  • How Auto-Machine Learning is reshaping daily cash forecasts, selecting the best-fit model for unparalleled accuracy.

Chapter 1


Chapter 2

Challenges With Spreadsheet Based Forecasting

Chapter 3

Advanced AI-Based Forecasts for Accounts Receivable

Chapter 4

Advanced AI-Based Forecasts for Accounts Payable

Chapter 5

AI-Based Scenario Modeling and Analysis

Chapter 6

Variance Analysis

Chapter 7

Auto-Machine Learning for all Cash Flow Categories
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Chapter 01


According to the Global Treasurer survey, treasury teams dedicate around 5,000 hours annually to spreadsheets, with approximately 792 hours specifically allocated to generating cash flow forecasts.

A well-executed cash flow projection tool is vital in ensuring stability, fostering growth, and building resilience against unexpected obstacles. In today’s dynamic business landscape, organizations are increasingly adopting Artificial Intelligence (AI) for automated forecasting, leveraging its precision to navigate the intricacies of financial management effectively.


An AI-powered treasury management solution overcomes these challenges by enabling precise predictions and seamless data integration. Discover in this eBook how AI automates manual tasks, reduces reliance on spreadsheets, and empowers you with a competitive edge.


Chapter 02

Challenges With Spreadsheet Based Forecasting

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.

Chapter 03

Advanced AI-Based Forecasts for Accounts Receivable

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.


To forecast AR cash flows in the US, instead of just using bank data, AI will pull invoice data from ERP and predict account-specific payment patterns. This creates a better bottom-up estimate of cash from AR in the US for the next 45 days.

Chapter 04

Advanced AI-Based Forecasts for Accounts Payable

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.


To forecast AP cash flows in the US, instead of just using bank data, AI will pull ERP data and estimate vendor-specific payment patterns for each location that issues vendor checks. This creates a more granular estimate of how your company will pay vendors for the next 45 days.

Chapter 05

AI-Based Scenario Modeling and Analysis

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.


To build a $100 million factory, you will borrow $50 million and use $50 million

of your own cash. You might want to know:

Scenario 1: The effect on overall cash if you start the project next month as planned

Scenario 2: The effect on overall cash if the project is delayed 45 days due to slow bank approvals on the loan

Scenario 3: The effect on overall cash if you build the factory in two phases, spending $50 million in 45 days and another $50 million 9 months later

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.

Chapter 06

Variance Analysis

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.


The cash forecast predicts that you’ll have a $10 million closing cash balance in 30 days. On average, your forecast ranges from 87% to 96% accuracy in 30 days. Your actual cash will probably range from $8.7 million to $11.3 million.Based on this, you can determine how much cash to hold, borrow or invest in
this period.

Chapter 07

Auto-Machine Learning for all Cash Flow Categories

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.


To forecast cash flows from AR in the US for each of the next 14 days, the module selects “Week Of Year Avg” as the method with the highest prediction accuracy. From Day 15 to Day 90, the module selects “Seasonal Avg” as the best method. These methods are automatically reviewed and refreshed daily.


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The HighRadius™ Treasury Management Applications consist of AI-powered Cash Forecasting Cloud and Cash Management Cloud designed to support treasury teams from companies of all sizes and industries. Delivered as SaaS, our solutions seamlessly integrate with multiple systems including ERPs, TMS, accounting systems, and banks using sFTP or API. They help treasuries around the world achieve end-to-end automation in their forecasting and cash management processes to deliver accurate and insightful results with lesser manual effort.

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