Financial forecasting refers to a process that businesses use to predict future revenues, expenses, cash flow, and improve profitability. Much like weather forecasting, the process may appear to resemble gazing into a crystal ball and guessing your company’s financial trajectory. But there is no crystal ball, and the predictions are not guesses but rather the outputs of a sophisticated and often elegant algorithm.
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, business realities are rarely static for any appreciable length of time. Circumstances change, sometimes drastically and with little warning. It is this element of uncertainty that can throw off your financial forecast and bash your future plans in both the short- and long-term. Thus, financial forecasting must account for foreseeable and changing circumstances too if it is to inform pragmatic decisions. When unexpected circumstances arise, financial forecasting must be done again to incorporate the necessary adjustments into the prediction model.
Adding inputs and greater data volumes to the forecasting equation can render more accurate predictions, using data such as buying patterns, fraud detection, real-time stock market information, customer segmentation and more. But this additional data, often referred to as big data, can exceed the limits of traditional financial forecasting methods. Mining and analyzing big data can also exceed human capabilities. It would take 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) come in. In short, machines can mine and analyze huge volumes of data much faster than people can. Machines have been known to deliver outputs, that is answers to queries put to data, in mere hours as opposed to traditional methods which can take weeks, months or even years depending on the size of the data set and the complexity of the query.
By equipping your finance team with ML or AI tools, you’re providing them with machine assistants that can greatly accelerate and improve the accuracy of their financial forecasting work.
What Is Machine Learning?
Machine learning is a subset of artificial intelligence (AI). Unlike artificial general intelligence (AGI), which is built to closely mimic human thinking, machine learning tools do not “think,” nor do they learn like humans do. ML is generally referred to as AI in marketing efforts to better communicate its uniqueness to an audience unfamiliar with the specifics in software programming. However, ML is not the same as AGI.
Yet, they’re unlike other types of computer software since they are not programmed by humans to do a task. Instead, the machine “learns” via rapid-fire trial and error computations and comparisons after being “trained” on large data sets.
In general, machine learning software analyzes large data sets and, through continuous iteration, builds and adapts its own models without human intervention.
That means ML gets better—far faster, more accurate and more highly attuned to nuance in the data—over time. But at no point does it decide on its own to take on another, totally unrelated task. If another task is required, humans must train ML on different data sets suitable for the new task. Therefore, ML exists in many different applications, performing tasks directly related to each application’s business purpose. This is why you may have many different ML-infused computer programs operating throughout the company.
Financial Forecasting Challenges
The three primary challenges in financial forecasting today are limitations in: humans, models and tools.
People are limited by time and in their ability to perform pattern recognition and consume large volumes of data. On the mathematical side, older models/calculations, the number of data inputs, the volume of data, the number of data sources and underlying assumptions greatly affect the accuracy and usefulness of outputs. From the executive perspective, traditional and familiar tools limit innovation and problem-solving.
Advantages of Machine Learning in Financial Forecasting
Machine learning adds several significant advantages to financial forecasting, all of which stem from a central theme: reducing or eliminating limitations.
With machine learning, business 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 which will allow finance teams to partner with the business to exploit opportunities in order to improve top-line revenue growth and improve cash flow. Machine learning tools can also automate many functions and processes to provide additional or updated insights, using the same or varying queries.
Machine learning makes it possible for companies to expand their analyses beyond traditional datasets, which can potentially reveal unexpected relationships between metrics. For example, companies can potentially derive better forecasts of revenue and sales from traditional sources of data, such as regional market information, with the help of machine learning that analyzes the availability of stock or weather data.
Financial Forecasting & Predictive Analytics
Both financial forecasting and predictive analytics render predictions. Traditional predictive analytics typically power recommendation engines. One example is a grocery store offering coupons by predicting when you’ll run out of a product you bought on the last visit and repeatedly in the past. Another example is Netflix offering new TV shows based on what you watched previously.
Machine learning applied to predictive analytics supercharges what is known and what can be predicted. Specifically, modern predictive analytics makes predictions based on historical data by using vastly larger amounts of data, from more sources, with machine learning techniques.
Here’s an example: Traditional analytics can inform a company that a customer bought a part for their home air conditioner unit five years ago and therefore is likely to need another replacement part this year or next. ML-enabled predictive analytics can tell you more, such as that part has executed 15,000 use cycles and is highly likely to cease functioning on March 15—or that the combined number of use cycles on the original and first replacement part predicts that the resulting wear on other parts will cause the entire unit to fail within three months.
This information prompts the company to upsell the customer to a unit replacement rather than just a part replacement. It also prompts changes to the financial forecast by predicting the likelihood that this customer will buy a new unit in the next few weeks.
Financial forecasting is the one area where finance can help drive the most value within an organization 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.