Financial forecasting is a tool used to predict future revenues, expenses, and cash flow, in order to try to improve profitability. It’s a sophisticated process and uses detailed algorithms to make forecasts more like predictions than guesses.
Financial forecasting processes are tied to financial, historical and market data, which reflect and affect the company’s performance. The assumption is that, if nothing changes, then the future is predictable with some degree of certainty.
But of course, in the real world as opposed to the modelled world, things rarely stay the same and change is a part of the challenge of running a business.
It is this uncertainty that is the enemy of financial forecasting and can impact your future plans in both the short- and long-term. Part of the financial forecasting process must account for foreseeable and changing circumstances, if it is to be used to inform decision making, so when something unexpected happens, financial forecasting must be updated and repeated to account for the change.
Adding inputs and greater data volumes to the forecasting equation can result in more accurate predictions, using data such as buying patterns, fraud detection, real-time stock market information, customer segmentation and more. However, all this extra data, often referred to as big data, can overwhelm the limits of traditional financial forecasting methods meaning it takes your finance team far too long to get the answers needed in time for them to have any significant business value.
This is where machine learning (ML) and artificial intelligence (AI) can be used. Computers can analyse large volumes of data much faster than people can, so an analysis can be completed in hours rather than several weeks using traditional methods, depending on the size of the data set and the complexity of the query. Machine learning and artificial intelligence can greatly accelerate and improve the accuracy of financial forecasting work.
What is machine learning?
Machine learning is a part of artificial intelligence, but unlike artificial intelligence, which is built to closely mimic human thinking, machine learning tools do not think or learn like humans do. Instead, machine learning software analyses large data sets and, through continuous iteration, builds and adapts its own models without human intervention.
Using machine learning results in much more accurate data which is more highly attuned to nuances in that data over time.
Advantages of machine learning in financial forecasting
Machine learning adds several significant advantages to financial forecasting, all with the aim of reducing or eliminating limitations.
With machine learning, businesses can use more data from more sources and conduct more complex and sophisticated querying of that data, producing accurate forecasts faster. This far exceeds the limits of traditional spreadsheets and financial software.
However, there is an ongoing shortage of AI engineers who are needed to program and train AI, and while there are companies conducting their own AI projects, many prefer to use commercial software with pre-trained AI embedded. Other software vendors embed ML algorithms that your finance team can train, or add training, as needed.
Further, machine learning can recognize more patterns within the data that can indicate, identify or establish nuances in business drivers and forecast errors. This leads to improving the ability to produce accurate forecasts more quickly allowing the business to react to opportunities quicker, improving top-line revenue growth and cash flow. Machine learning tools can also automate many functions and processes to provide additional or updated insights, using the same or varying queries.
Financial forecasting and predictive analytics
Both financial forecasting and predictive analytics render predictions. Traditional predictive analytics typically power recommendation engines. For example, predictive analytics will be used to offer a product to you based on previous purchases.
Machine learning applied to predictive analytics makes predictions based on historical data by using vastly larger amounts of data, from more sources, with machine learning techniques.
In summary
Financial forecasting is the one area where finance can help drive the most value within an organisation and have direct impacts on revenue, profitability and shareholder value. Big data and machine learning accelerate and vastly improve financial forecasting over traditional methods. Speed is important because that means the forecast can be made on real time or near real-time information making the output more useful and relevant to forward-looking decisions. But acceleration must come with no loss in accuracy.
Machine learning is the only way to achieve both speed and accuracy when using huge amounts of data in financial forecasting.