5 AI Cash Flow Forecasting Use Cases in 2024

Introduction

Cash forecasting is integral to effective treasury management, providing companies with a holistic understanding of their cash flow dynamics over time. It facilitates optimal cash management, risk mitigation, and capitalizing on cash surpluses, thereby bolstering overall financial health and enabling strategic planning. However, traditional spreadsheet-based forecasting methods often consume excessive time and resources, with treasury teams spending up to 5,000 hours annually on manual tasks. This inefficiency underscores the need for streamlined processes and technological advancements to enhance forecast accuracy and empower treasurers to deliver strategic value.

In response, companies globally are exploring various options and technological solutions to optimize cash forecasting processes. By leveraging AI in forecasting, they aim to improve integration, obtain real-time insights, and mitigate the limitations of traditional methods. AI holds promise in transforming cash forecasting by automating tasks, refining predictive models, and delivering actionable insights, thereby enabling treasurers to allocate their time more strategically and drive better decision-making.

Traditional Cash Forecasting Methods

There are various types of cash flow forecasting methods that businesses can use, depending on their needs and the availability of data. Some of the most common methods include:

1. 
   

   Direct Method Cash Flow Forecasting:

   
   

   This method involves forecasting cash inflows and outflows by directly estimating the amounts that will be received or paid out over the forecast period.

   
   
2. 
   

   Indirect Method Cash Flow Forecasting:

   
   

   This method involves forecasting cash inflows and outflows by estimating the changes in the company’s balance sheet accounts, such as accounts receivable, accounts payable, and inventory based on the net profit /income figures

   
   
3. 
   

   Rolling Cash Flow Forecasting:

   
   

   This method involves updating the cash forecast regularly, often on a weekly or monthly basis, to reflect actual results and new information.

   
   
4. 
   

   Bottom-Up Cash Flow Forecasting:

   
   

   This method involves forecasting cash inflows and outflows for each department or business unit and then aggregating the results to create an overall cash forecast.

   
   
5. 
   

   Top-Down Cash Flow Forecasting:

   
   

   This method involves forecasting cash inflows and outflows for the entire business and then allocating the forecasted amounts to each department or business unit.

   
   
6. 
   

   Stochastic Models:

   
   

   This method involves utilizing probability distributions and statistical techniques to evaluate the likelihood of various cash flow scenarios, helping to quantify uncertainty and risk.

   
   
7. 
   

   Integrated Financial Planning (IFP):

   
   

   This method offers a comprehensive picture of financial performance by integrating cash flow forecasting with other financial planning procedures like forecasting, budgeting, and strategic planning.

   
   
8. 
   

   Dynamic Financial Analysis:

   
   

   This method combines simulation and scenario analysis to measure the influence of numerous factors on cash flow, allowing for more informed risk assessment and decision-making.

   
   
9. 
   

   Zero-Based Forecasting:

   
   

   This method requires creating cash flow estimates from scratch without depending on historical data, often used in rapidly evolving or startup environments.

   
   

There is one more method that is cash forecasting using AI/ML that we are going to discuss in detail in the subsequent section and highlight its superior accuracy compared to other traditional methods. 

Importance of Selecting the Appropriate Type of Cash Flow Forecasting Model for the Business

It is essential that organizations choose the cash flow forecasting model type that best suits their demands and situation. When choosing a cash flow forecasting model, factors to take into account are the degree of information needed, the forecast period, the complexity of the business processes, and the availability and reliability of data. A large organization with several business units and complex operations would require a more sophisticated methodology, like rolling cash flow forecasting, whereas a small business with straightforward operations might find that direct method cash flow forecasting suffices. To sum up, cash flow forecasting is a critical instrument that helps organizations plan their financial activities, manage their cash flow, and make wise financial decisions.

By selecting the appropriate type of cash flow forecasting model and techniques, businesses can secure precise and dependable cash forecasts, integral to achieving their financial aims.

Challenges with the Traditional Cash Forecasting Methods

Most of the forecasting models are done on spreadsheets. Spreadsheets based forecasting models are error prone and lead to significant losses, with one in every five major corporate treasurers experiencing such setbacks. While larger corporations may be able to withstand such financial hardships, small to mid-sized businesses can be severely impacted.

In this data-driven era, organizations encounter the daunting task of efficiently collecting, organizing, and deriving insights from the immense volume of data generated daily, totaling 2.5 quintillion bytes. Recent trends indicate exponential growth, presenting challenges in managing and storing such vast datasets.

Let’s take a look at each of the possible challenges based on spreadsheet-based cash forecasting models:

1. 
   

   Siloed information: In many organizations, knowledge and data related to factors that influence cash flow reside in silos across business units and teams. Lack of information sharing results in each team lacking insight into the broader picture, limiting the accuracy of cash flow forecasts.

   
   
2. 
   

   Reliance on historical data: Traditional cash flow forecasting relies heavily on historical cash flow data. While historical data provides a useful reference point, it does not account for current conditions which may differ significantly from the past. This can lead to inaccurate forecasts.

   
   
3. 
   

   Inaccurate assumptions and estimates: Cash flow forecasting requires assumptions and estimates to predict future cash flows. If these assumptions and estimates are inaccurate due to bias, lack of information, or improper methodology, the resulting forecasts will also be inaccurate.

   
   
4. 
   

   Lack of real-time data: Traditional cash flow forecasting typically uses data that is days or even weeks old. This lag in data access means that recent events that could impact cash flow are not reflected in the forecasts. Real-time data access is needed for the most accurate forecasts.

   
   
5. 
   

   Failure to account for unexpected events: Unexpected events by definition cannot be predicted but can have a huge impact on cash flow. Traditional cash flow forecasting techniques typically assume business as usual and do not account for unforeseen events.

   
   
6. 
   

   Limited input from stakeholders: Gathering input from only a limited number of stakeholders, like finance teams, can provide a narrow perspective. Input from a wider range of stakeholders, including business units, can help identify issues that may otherwise be missed and lead to more accurate forecasts. 

   
   

These challenges can hinder the accuracy of cash flow forecasting, resulting in missed opportunities, cash flow issues, and other financial concerns. Overcoming these limitations in cash flow forecasting can yield significant benefits for organizations. 

The lack of comprehensive visibility globally impedes confident decision-making in managing surplus cash, which could otherwise be utilized for purchasing fixed assets or conducting stock buybacks. 

Businesses require robust solutions and tools to ensure precise financial management and unlock the potential of their data. By enhancing capabilities such as real-time data accessibility, input from diverse stakeholders, and the incorporation of unforeseen events, we can elevate cash flow forecasting to a strategic advantage.

How AI Can Enhance Cash Forecasting

As we’ve seen, relying solely on spreadsheet-based forecasting models can lead to costly manual errors, potentially resulting in significant financial losses for organizations. In this section, we are going to explore 5 real-life scenarios where harnessing the power of AI can revolutionize cash forecasting, mitigate errors, and drive financial success.

1. Advanced AI-Based Forecasts for Accounts Receivable(A/R)

1. Customer payment behavior plays an important role in cash forecasting.
2. The bigger the company, the more complex the business process and subsidiaries the bigger the amount of data, more the complexity.
3. AI and machine learning (ML) models assist in predicting payment dates beyond the open invoice period.

The accuracy of A/R forecasting is deeply influenced by customer payment behavior, as they may not always adhere to the agreed-upon payment terms in the contract. Since it is heavily dependent on external factors, A/R forecasting becomes extremely challenging for the Treasury professional to forecast. 

As a company expands and acquires more companies, managing A/R and collecting payments becomes more complex. As companies expand, data gets scattered across various business units, systems, and ERPs, which poses a unique difficulty for the treasurers to perform accurate forecasts. 

Apart from the above mentioned challenges, factors such as seasonal trends, business cycles, credit scores, customer behavior, disputes, and discounts also add complexity to AR forecasting, leading to inaccuracies.

With the use of client invoice data from ERPs, advanced AI models take into account average payment terms as well as certain business seasons when invoice settlement rates are higher or lower. To improve prediction accuracy, these models detect and track important variables that affect client payment rates, such as seasonal variations in business and invoice amounts. 

Additionally, utilizing sales order data from the ERP, AI and machine learning (ML) models help estimate payment dates beyond the open invoice period. By integrating with ERPs, treasury management solutions can automatically adjust projected invoice payment dates depending on clients’ committed payment dates.

For instance, To forecast AR cash flows in the company, 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.

2. Advanced AI-Based Forecasts for Accounts Payable

1. Treasury solutions integrate with ERPs to override payment forecasts 
2. Compare historical and recent data to run AI-based scenarios to produce an accurate AP estimate

AI and ML models leverage ERP sales order data to forecast payment timelines beyond standard invoice dates. When integrated with ERPs, these treasury solutions automatically adjust projected payment dates based on clients’ promised payment schedules, ensuring seamless cash flow management.

An artificial intelligence (AI) system that forecasts cash flow combines current and historical data while executing many scenarios using different AI algorithms. Choosing the cash projection that is both reasonable and optimistic yields an accurate estimate for accounts payable (AP). This gives treasurers the ability to predict costs and spending over the course of the forecast period.

For instance, To predict AP cash flows in the US, AI goes beyond bank data by analyzing ERP information to discern vendor payment patterns across different locations issuing checks. This yields a detailed projection of how your company will settle vendor payments over the next 45 days.

3. Variance Analysis

1. Use variance analysis to implement proactive decisions.
2. Need of enterprise treasurers to create low-variance forecasts.

Variance analysis is like a financial detective, revealing disparities between projected and actual cash flows. It’s a strategic tool, guiding us to investigate anomalies and refine our financial strategies. By delving into root causes, we gain clarity for proactive decision-making, effective risk mitigation, and precise budget adjustments, ensuring our financial journey stays on course towards success.

Navigating cash flow data can be daunting for enterprises, complicating treasurers’ efforts to generate accurate forecasts—especially with manual methods like spreadsheets. Manual variance reduction often yields variances of 20-25%, consuming substantial time and resources. 

Cash forecasting software analyzes intricate categories like A/P and A/R across regions and corporations, leveraging AI to enhance forecast accuracy by 90-95%. This facilitates informed financial decisions and optimal resource allocation for sustained growth and stability.

For instance, Suppose the cash forecast anticipates a closing balance of $10 million in 30 days, with an accuracy range of 87% to 96%. This suggests your actual cash may vary from $8.7 million to $11.3 million. Use this insight to make decisions on cash reserves, borrowing, or investments for the period ahead, ensuring financial stability and growth.

4. AI-Based Scenario Modeling and Analysis

1. Create different financial scenarios based on assumptions of various factors.
2. Identify risks and opportunities to evaluate the potential impact on cash flow.
3. Prevent losses through proactive analysis, improve investments, and optimize returns

Scenario modeling empowers businesses to explore various financial situations, factoring in shifts in revenue, expenses, and market dynamics. Conversely, cash forecasting scenario analysis delves into how specific events or factors affect cash flow, pinpointing risks and opportunities and evaluating their potential cash flow impact. 

By using these insights companies anticipate and navigate financial challenges, and make informed decisions to drive success.

Scenario Builder

With HighRadius AI Cash Flow Forecasting we can easily create or build different scenarios based on different economic assumptions on top of a base forecast. Treasurers can modify cash inflows, cash outflows, percentages, amounts, and foreign exchange rates to stimulate different scenarios and evaluate their impact on cash flow statements.

Not only this, treasurers can also save forecast versions as ‘snapshots’ for future comparisons. In one single chart, at any given point in time treasurers can compare two or more scenarios by keeping them side-by-side.

Visual cues, like heatmaps, highlight differences, making it simple to compare a base forecast with a scenario or between two scenarios. You can easily analyze various snapshots from different forecasts in one chart, aiding decision-making and strategic planning.

For instance, consider the following scenario and evaluate its 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

With AI, treasurers can proactively analyze scenarios to mitigate losses, improve investments, optimize returns, prepare for cash shortages, allocate surpluses, and manage foreign exchange risks.

5. Auto-Machine Learning for all Cash Flow Categories

1. Selecting the best-fit AI model for accurate forecasting.
2. Need for continuously selecting the best fit AI model.

These treasury solutions employ Auto-Machine Learning by utilizing past bank data to provide daily cash estimates. They optimize accuracy by selecting the best-fit models for each cash flow category, based on past transactions, ensuring precise AI-based forecasting and informed decision-making.

As we can see above, there are two aspects of model, daily selection and best-fit models. To ensure up-to-date and precise forecasts, the system continuously updates chosen models for each forecast period—daily, weekly, or monthly. This dynamic model selection process optimizes the precision of treasury solution-generated forecasts.

By leveraging this Auto-Machine Learning and adopting the best-fit model method, businesses optimize cash forecasting, enhancing daily cash forecast reliability and precision. Treasurers make informed financial decisions, efficiently managing cash flow dynamics.

For instance, consider the following example: 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.

Benefits of Using AI Cash Flow Forecasting Solutions

Artificial Intelligence (AI) revolutionizes conventional and automated cash flow forecasting methods by adeptly identifying the seasonal ebbs and flows within business operations. By meticulously scrutinizing financial data, AI discerns temporal patterns, dynamically adjusting forecasts to mirror seasonal trends and recent market events.

This provides businesses with actionable information to enhance financial plans and capitalize on emerging opportunities by enabling them to develop projections that are both accurate and sensitive to the constantly changing landscape.

For instance, suppose your business experiences significant cash outflows to a vendor over three consecutive months, followed by a cessation of payments. HighRadius’ AI-driven cash flow forecasting tool, will swiftly recognize this alteration and automatically omit the vendor’s payments from subsequent forecasts until instructed otherwise by you.

1. 
   

   Data Collection Automation – These AI systems automatically gather, store, and sort the financial data, helping us save countless hours spent on manually entering the data and storing it in one convenient place for future retrieval and usage.

   
   
2. 
   

   Enhancing Accuracy and Increasing Efficiency – AI algorithms possess the capability to efficiently manage and analyze vast quantities of data, providing valuable insights to senior stakeholders in a fraction of the time it would take human analysts. These systems are adept at adapting to dynamic changes in the environment and can effectively process data from a variety of sources. Their ability to discern intricate relationships and correlations, often imperceptible to human observers, allows for more nuanced and accurate forecasts. Moreover, their adaptability enables them to continually learn and improve their predictions, ensuring relevance and accuracy as conditions evolve over time.

   
   
3. 
   

   Providing Predictive Insights – With the help of advanced AI algorithms and ML, these systems predict and give accurate future cash flows trends. Not only, they also take into account all the dynamic economic changes that might affect the cash flow projections, so businesses can make informed financial decisions.

   
   
4. 
   

   Offering Scalability – Businesses have multiple ERPs, divisions,and subsidiaries from where they have to gather data to make accurate cash flow predictions. But,unlike traditional cash forecasting methods which pose challenges for analysts to work on such intricate data. AI-powered systems have the ability to adapt to such changes and keep pace with such complex business models, hence eliminating the need for additional forecasting resources.

   
   
5. 
   

   Seamless, Reliable Integration – AI-based cash forecasting systems like HighRadius, seamlessly integrates with major global banks and ERPs to provide real-time insights. These systems also provide a predictive short-term cash flow forecast based on the bank, A/R, and A/P data to help businesses streamline their activities and make better decisions. 

   
   
6. 
   

   Customizing Forecasts for Intelligent Analysis – By utilizing AI capabilities,, analysts are now able to craft forecasts finely attuned to the intricate nuances of their business, incorporating variables that might otherwise escape notice. These AI-driven systems delve deeply into historical payment data, deciphering customer behavior patterns to inform their predictions accurately. 

   
   

Platforms such as HighRadius harness this capability to generate tailored forecast models at the local, regional, or global scale, dynamically adapting to 

evolving requirements. Through this iterative process, they deliver recurring forecasts complete with necessary adjustments, empowering organizations to make informed decisions with precision and foresight.

Case Studies or Examples

Many companies remain unaware of the hidden challenges and untapped opportunities until they face significant losses or encounter disruptions in productivity. As previously discussed, analysts invest up to 5,000 hours navigating spreadsheets to oversee treasury operations and construct precise cash forecasting models. This staggering time investment underscores the critical need for streamlined processes and advanced tools to unlock efficiency and foresight in financial operations. By recognizing these challenges and embracing innovative solutions, organizations can proactively mitigate risks, optimize productivity, and seize opportunities for sustained growth and success.

We are going to discuss one such challenge that HNTB was facing and how HighRadius, an AI-powered cash forecasting solution provider helped them to resolve their challenges. 

HNTB was facing a daunting task managing cash forecasts due to their heavy reliance on Excel sheets. Spending up to 20 hours weekly, they struggle to gather data from various sources, resulting in unreliable predictions. Moreover, with a significant line of credit at stake, inaccuracies in forecasting pose substantial risks to their financial stability. Compounding the issue, Harris also maintained a substantial line of credit, amplifying the risks associated with flawed forecasting. 

This confluence of challenges underscores the urgent need for transformative solutions to streamline operations, enhance visibility, and fortify financial resilience.

This is when HighRadius helped them transform their cash forecasting process and we can see how significantly HighRadius has impacted HNTB with their solution.

Challenges and Considerations

Implementing AI in forecasting solutions comes with its own set of challenges. These challenges vary ranging from data to integrations to compliance.

Let’s understand these challenges and the various considerations taken into account while deciding on the implementation.

1. 
   

   Data Quality and Availability – The success of AI-based solutions heavily depends on the quality and availability of data. These systems self-learn, train themselves, and evolve continuously based on the given dataset. Ensuring that this data is accurate, complete, and timely is extremely crucial for effective and accurate forecasting. Companies often face challenges in gathering this data due to complex business divisions, processes, and disparate systems. 

   
   
2. 
   

   Integration with Existing Systems and Banks – This is one of the biggest challenges that organizations face while implementing such systems. According to IDC, global spending on integration and orchestration middleware is expected to reach $15.8 billion by 2025.

   
   

   Companies often find it difficult to integrate their ERPs, CRMs, and internal systems with these AI-systems, due to which either they are not able to make a concrete decision or end up spending on middleware. The challenges are not only limited to this, issues like compatibility, data synchronization, and workflow integration also need to be addressed to ensure seamless operation.

   
   
3. 
   

   Change Management and Adoption – Implementing AI in forecasting often requires changes in processes, roles, and organizational changes. Overcoming resistance to change and bridging the gap in understanding among stakeholders are critical steps to ensure the success of such initiatives.

   
   
4. 
   

   Regulatory Compliance and Data Privacy – In today’s data-driven landscape, organizations leveraging AI-based forecasting solutions must prioritize compliance with key regulations like GDPR, PCI DSS, SOC, or HIPAA, along with upholding stringent data privacy standards. Safeguarding sensitive financial data against unauthorized access or misuse is not just a priority; it’s a non-negotiable responsibility. By proactively managing these aspects, businesses can not only build trust among stakeholders but also ensure the integrity and security of their operations. 

   
   
5. 
   

   Continuous Monitoring and Improvement – Ensuring the continued accuracy and relevance of AI models demands constant vigilance. Organizations need to implement robust monitoring systems to track model performance, swiftly address any issues, and integrate feedback for enhanced forecasting results.

   
   
6. 
   

   Business Process Integration, Resource Allocation, and Skill Requirements – Implementing AI cash flow forecasting solutions demands substantial resources, from financial investment to skilled personnel and computational infrastructure. To ensure successful deployment, organizations must assess their resource capabilities and address skill gaps. Alignment with existing business processes and decision-making frameworks is crucial. Seamless integration into operational workflows and decision support systems is vital to fully harness the benefits of AI technology.

   
   

Future Trends and Outlook

Based on the most recent data from Statista Market insights, the AI market surged to $108 billion in 2020 and is projected to skyrocket to $738 billion by 2030. This staggering growth represents an incredible 683% increase in just a decade. Such exponential growth underscores the immense potential and lucrative opportunities within the AI sector, urging businesses to capitalize on this transformative technology for sustainable success.

If we delve a little deeper to understand the nuances of AI in FinTech, we will find that the market for AI in FinTech is alone growing at a CAGR of 28.5%. The numbers look very promising and the growth of the overall industry is definitely opportunistic for new players and the existing players.

As Gen AI continues to revolutionize industries, its versatile applications transcend boundaries. Companies are actively exploring its potential across diverse fields, leveraging its capabilities to drive innovation.

For instance, HighRadius has pioneered FreedaGPT, a Gen AI tool enhancing its AI-based cash forecasting solution. By transforming chat prompts into actionable insights and dynamic visuals, FreedaGPT empowers users to analyze scenarios, make informed decisions, and optimize liquidity strategies, such as investments and borrowings. This innovative integration underscores the transformative power of Gen AI in driving efficiency and efficacy across business operations.

Conclusion

In conclusion, the landscape of AI cash flow forecasting tools is evolving rapidly, with numerous use cases reshaping financial management in 2024 and beyond. From predictive analytics to real-time insights, AI is revolutionizing how organizations optimize liquidity, mitigate risks, and drive strategic decision-making. As we look to the future, expect continued advancements in AI technology, further enhancing accuracy, efficiency, and adaptability in cash forecasting. With innovative chat-driven AI tools like ChatGPT on the horizon, businesses can anticipate even greater agility and foresight in navigating complex financial landscapes. Embracing these transformative possibilities, organizations are poised to unlock new levels of financial resilience and growth in the years ahead.

FAQs

1) What is the role of AI in financial forecasting?

AI plays a pivotal role in financial forecasting by leveraging advanced algorithms to analyze vast amounts of data quickly and accurately. HighRadius harnesses AI technology to enhance cash forecasting accuracy, providing businesses with actionable insights to optimize liquidity management and drive financial performance with confidence.

2) Which AI is best for forecasting?

HighRadius employs cutting-edge AI technology tailored specifically for financial forecasting. With its advanced algorithms and robust analytics, HighRadius ensures 

accurate and efficient cash forecasting, helping businesses make informed decisions and achieve financial success with ease and confidence.

3) What is the use of AI in prediction?

AI leverages historical data and market trends to predict future cash flows accurately in cash forecasting. This technology enables businesses to make informed decisions, optimize liquidity management, and achieve financial goals effectively and efficiently by anticipating cash flow fluctuations and potential risks with precision and insight.

4) How is AI used in sales forecasting?

AI analyzes historical sales data, customer behavior, and market trends to predict future sales accurately. It helps businesses anticipate demand, optimize inventory levels, and make informed decisions to drive sales growth. AI-driven sales forecasting enables businesses to adapt to changing market conditions and achieve their revenue targets with confidence.