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Without automation a cashflow forecasting process for head office teams can involve significant manual work, therefore automating key workflows can alleviate much of this administrative burden. In a similar way, actual cash flow data can be pulled automatically from these systems and fed directly into the cash forecasting process. Most commonly stored in MT940 or BAI2 files, automating bank account inputs means identifying and extracting the relevant information automatically. Depending on the system, and how it is being used, the TMS may also hold some of the required forecast cashflow data (such as financing or intercompany cashflows). Longer-term forecasted cashflows may be contained in budgeting/planning systems. For more information, read our post on increasing accounts receivable forecasting accuracy. If this data is available, it is therefore beneficial to include it in the forecast process. Quite often, short-term payable and receivables are already recorded in ERP/accounts systems. Forecasting AP and AR is an integral part of the cash forecasting process. This be achieved by either algorithmic or rules-based classification. Automating the input of this data to the process encompasses both the collection and classification of this data. Typically, there are two sets of data collected in a cash forecasting process, the forecast data and the actual data. The result of these benefits is that automation doesn’t just mean fewer mistakes, when applied well, it leads to a roundly improved, best practice forecasting process. This, when combined with the factors listed above, means that automation can substantially improve the quality of the forecasts produced. When the process is carefully designed and mapped from the outset, automation can leverage data already in the corporate technology infrastructure (TMS systems, ERP systems, bank systems, etc.). In turn, this changes the focus of activities from administration to analytics. Automation can therefore increase efficiency and alleviate an overburdened workload. Treasury teams, even those in the largest organisations, are often small. Automating part or all of the process reduces the risk of human error, thereby improving confidence in the forecast.

How can automation benefit treasury and finance teams?Īutomating the cash flow forecasting process brings many benefits, namely: For further reading on how ML/AI can be used in cash forecasting, we have written an article discussing how ML/AI methods of forecasting compare with traditional statistical ones, which explores the topic in further detail. This post focuses on how cash flow forecasting processes can be automated without ML/AI. Some parts of the cash forecasting process may be automated with Machine Learning (ML) or Artificial Intelligence (AI) technologies, or those technologies may be used at the end of the process and deployed more on the output. Cash forecasting automation may refer to automating the process in its entirety or may refer to the automation of the constituent parts of this process.Īs mentioned above, the extent to which the process is automated, and indeed how the process is automated, fall on a spectrum. There are many workflows, data inputs, and communications and reporting exercises that make up a cash forecasting process. In this post we look at what forecasting automation is, what the benefits of automation are, which components of the process can be automated, and how cash forecasting automation can be achieved. Some are fully automated, “no touch” processes, some deploy automation at key steps in the process, while others are fully manual, labour intensive processes.

Working with treasury and finance teams across the globe, we see a large number of cash forecasting processes with varying degrees of automation.

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