Smart ways to transition from manual to strategic tasks: Discover the hidden fact about cash forecasting solutions.

According to a Global Treasurer survey, treasury teams spend almost 5,000 hours per year on spreadsheets, with 792 hours spent on preparing cash flow estimates. Treasury can use automated cash forecasting to free up 792 hours to focus on strategic decision-making.

Apart from time-saving, here are some reasons why automated cash forecasting is better than manual cash forecasting:

• Seamless integration: Treasury needs to collect data from various sources such as ERPs, TMSs, and bank portals, as well as from various teams such as FP&A, Payroll, HR, A/R, and A/P. This procedure is time-consuming and prone to errors. An automated cash forecasting software supports seamless integration with numerous data sources as well as automatic data consolidation, hence reducing the scope of errors.
• Increased accuracy in cash forecasts: As spreadsheets limit the number of variables that can be added, individual nuances cannot be tracked while estimating cash flows. AI cash forecasting, on the other hand, incorporates several customer and invoice-level data to properly estimate customer-specific payment dates. External factors such as raw material price variations and seasonality can also be taken into account in order to capture trends and generate an accurate real-time cash flow prediction.
• Real-time access to information: The treasury department needs to manually update the spreadsheets regularly. As a result, the reporting turnaround time increases, and the reports are obsolete by the time they are given to the CFOs. This makes it harder to make timely decisions. On the contrary, automated cash forecasting assists in automatically capturing real-time data and storing it in a central repository. The ability to retrieve data across all entities automatically reduces the time it takes for CFOs to make data-driven decisions.
• Identify variance across multiple entities and durations: The manual procedure makes tracking variances between forecasts and actuals, as well as the sources of variance, challenging. AI forecasting software helps track and reduce variance in forecasts for various cash flow categories over several time periods.

Companies suffer financial losses when a crisis arises. Accurate cash forecasting is required to avoid catastrophic implications on liquidity. Since the treasury is in charge of all company’s cash flows, teams must react quickly and proactively to avoid the repercussions of an economic crisis.

The following are ways treasury can respond to a crisis with an AI forecasting software:

• Use baseline forecast as a guide: Treasurers can regularly track current and future cash positions by using global treasury solutions based on the forecast. A baseline forecast is essential for analyzing and fine-tuning forecast models. This results in more proactive cash management.
• Incorporate internal and external data to forecast: Determining the appropriate data sources assists in obtaining accurate data for accurate cash forecasts. Forecast accuracy is improved by taking into account prior performance and adding seasonality tendencies as well as external factors such as raw material price fluctuations and customer behaviors.
• Detect early signs of cash crunch: Accurate data and real-time reporting can help treasurers avoid making short-sighted judgments by identifying impending cash shortfall or surplus. CFOs are in a better position to develop appropriate decisions to limit the effects of prospective financial threats if they have precise cash flow forecasts.

By developing global treasury solutions, controlling cash flows, managing bank relationships, and managing risks, the treasury department’s ultimate purpose is to ensure that there is enough liquidity for managing the company’s day-to-day operations. During difficult times, their focus shifts to managing working capital, but most organizations continue to conduct treasury operations in the same way.

Automation and AI-based technology add value to treasury in the following ways:

• Digital adoption: Even small and mid-sized businesses have begun to embrace technologies to automate operations that do not necessarily require human involvement or are particularly labor-intensive, allowing their employees to focus more on high-priority tasks such as decision-making. So treasurers must take preventive efforts to manage cyber frauds and data breaches and neutralize them with severe countermeasures.
• Transactional efficiency: Employees used to spend a significant amount of time in Payables, Receivables, and Reporting, manually doing predictable and/or routine activities, managing exceptions and disputes, and identifying risk. Artificial intelligence (AI) has the potential to improve the efficiency of these tasks.
• Data-based decisions: Massive data banks exist in business, yet most organizations still don’t understand how to use that data to produce commercial goals. To assist treasurers in making better judgments and forecasts, predictive analytics incorporates AI subfields such as pattern recognition, data mining, and sophisticated statistical modeling.

• Increased granular visibility: Companies with operations in different parts of the world have several banks and ERP systems, resulting in limited granular visibility into cash flows. Variance analysis is hampered by a lack of visibility, which impacts decision-making. Automated cash forecasting creates a consolidated view by automatically collecting data from sources such as TMS, ERPs, banks, and FP&A systems. As a result, it improves granular visibility into business cash flows.
• Improved analysis and reporting: The software creates high-quality reports in real-time, with deep insights and drill-down capabilities. Because the turnaround time is shortened, CFOs obtain reports on cash flows, bank balances, and funding requirements quickly. The results are also reliable enough to be delivered to external stakeholders, hence improving the credibility of the board and investors.
• Improved risk identification: Automated cash forecasting software uses machine learning to increase prediction accuracy by comparing previous and recent outcomes, finding flaws, and making continual improvements. Treasurers may proactively detect and reduce risks before they become losses for the firm by having real-time data visibility and accuracy in projecting cash flows.
• Improved workforce productivity: The treasury department’s administrative burden is reduced because automation handles manual and time-consuming activities. As a result, treasury can concentrate on high-value tasks like cash management, risk management, and strategic decision-making.

Talk to an expert today to learn how to make the best use of AI and automation to transition from manual to strategic tasks.