The ultimate goal of the treasury department is to ensure that there is enough liquidity for managing its day-to-day operations by creating cash forecasts, managing cash flows, managing bank relationships and risks. During turbulent times, their priorities shift towards managing working capital, but most businesses still perform treasury functions the same way. Moreover, the role of treasury is becoming increasingly complex, but the treasury resources are not increasing. Thus they need to transform their existing processes and systems to ensure better liquidity management.
The drivers of treasury transformation are:
Cash forecasting helps transform treasury by performing rapid analysis, monitoring regulations, ensuring real-time reporting, proactively managing cyber risks, and handling multiple banks seamlessly.
Cash forecasting helps treasury in:
These are the bottlenecks in the four cash forecasting levers that prevent treasurers from meeting their business objectives:
First Principles Thinking is a basic assumption that cannot be broken down further. It involves deconstruction (breaking down complicated problems into basic elements) and reconstruction (reassembling them to make better decisions, processes, and systems).
The problem with the approach is that the forecast does not accurately predict real-time cash movements. The problem can further be broken down into its basic elements such as:
The basic elements of the approach solution are then gathered to identify the current approach to make improvements. The solution is to focus on cash moving through bank accounts at the lowest level.
When we deconstruct data gathering, the problem is that the manual data gathering from various teams and/or systems is cumbersome and error-prone. The problem can further be broken down into its elements:
The basic elements of the problems can be reassembled to generate the solution, which is to obtain the data directly from the source, rather than manually gathering data from multiple teams.
Oftentimes, the treasury department lacks the technical expertise to build the right model for different cash flow categories. The problem can be further divided into basic elements such as:
The solution for improving modeling is to use AI models to create accurate forecasts for complex cash flow categories such as A/R and A/P and use heuristic models for static categories such as taxes and payroll.
Variance analysis is difficult to perform for each entity level over multiple durations for multiple cash flow categories. The basic elements of the problem are:
The problem in variance analysis can be addressed using automated tools that capture multiple forecast iterations at a granular level.
37% of treasury professionals stated that they lack time to perform their day-to-day responsibilities, 35% of them feel that treasury is understaffed, and 63% feel that their top operational challenges are manual processes and lack of investment or technology in treasury operations. Treasury professionals need to shift their roles from performing administrative tasks to performing value-added tasks. Treasurers need a creative mindset nowadays instead of a transaction-oriented traditional mindset to stay on top of the curve.
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