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Being able to predict a business’ financial future is the key to planning future investments. Financial forecasting enables you to do that. However, it’s not simple; there are multiple methods that you can use, and you’ll need large amounts of data to make accurate predictions. This is when machine learning comes into play, with its potential to process information and extract insights from it. Do you want to learn more? Then keep reading!
What Is Financial Forecasting?
Let’s start by defining what financial forecasting is. This is the term coined to describe estimating a business’ financial future based on historical data, such as:
- revenue,
- cash flow,
- expenses,
- sales.
There are multiple methods of financial forecasting, leading to different results. You may, for instance, estimate:
- income,
- revenue,
- gross margin and SG&A expenses.
This makes effective financial forecasting a bit more difficult, as you need to be able to use each formula efficiently, which requires additional knowledge and tools.
Machine Learning in Financial Forecasting
When we defined what financial forecasting is, we mentioned historical data—a term that you’ll most commonly see in the context of machine learning services. This is because financial forecasting is a set of efforts perfect for this technology.
The main advantage of ML is the possibility to extract valuable insights from vast data volumes, which sounds… exactly like our definition forecasting. Naturally, your ML model needs to incorporate different financial forecasting formulas, but if you feed it with them, you can create accurate forecasts in a matter of minutes. Why is this important?
- Machine learning is accurate—you won’t receive an erroneous forecast just because somebody forgets to input a certain piece of information into the calculator/formula.
- ML-based models are fast and effortless—you can generate financial forecasts automatically, meaning that your team members have more time on other value-bringing tasks.
- AI-powered systems give you a better overview of your data—if you use an AI Prompter taught on your data, you can easily prompt the information to appear in the format you want to present it—whether it’s numbers, a roadmap or a graph.
- Machine learning may consider more than just financial data—the best systems may analyse other information sources (e.g., the news, commodity exchanges, indexes, stock market, etc.) to make even better predictions based on the current geopolitical and economic situation in the world.
Our Tools and Software will Help You with Financial Forecasting!
At Ailleron, we specialise in technology for financial institutions, ranging from data platforms to designing and building complex natural language processing models based on ML and LLMs. We can design financial forecasting software specifically for your organisation, ensuring that it has all the functions you need to extract valuable predictive insights.
The Takeaway
Financial forecasting requires some thought, as you must decide what you are forecasting beforehand. It’s a complex process that machine learning can simplify significantly. Therefore, we encourage you to invest in relevant technology, make this task easier for your company, and benefit from more accurate forecasts.
You may also read: The Might of Machine Learning: Opportunities and Challenges in Banking.
