The future of financial forecasting: artificial intelligence and machine learning

Despite the Chancellor stating we avoided recession, economic uncertainty continues to run wild, with many CFOs predicting that disruption will continue late into this year.

As businesses face increasing pressure to stay competitive and adapt to change, accurately forecasting financial performance has become more critical than ever.

However, while essential for predicting future financial outcomes, generating accurate and timely forecasts remains an enormous challenge for many businesses.    

Measure once, cut twice

Too often, finance teams and businesses have to choose between turning around an accurate forecast at a slower pace or a less accurate forecast at a quicker pace – neither option is ideal.

This reality creates tremendous pressure; guesswork and assumptions can lead to inaccuracies, which can have serious consequences for businesses.

Getting forecasting wrong isn’t just an oopsie moment.

Unstable inventory, deteriorating supplier relationships, increased costs and poor cash management, to name a few, are potential risks of inaccurate forecasting.

Top tip: One way of combating inaccurate forecasting is to use techniques such as statistical analysis, scenario analysis and sensitivity analysis. By incorporating these techniques into the forecasting process, you can reduce the risk of errors and improve the accuracy of your forecasts.

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Get the robots in

Seeing into the future doesn’t require magic but science.

Going forward, I urge finance teams and businesses to embrace emerging tools which use artificial intelligence (AI) and machine learning (ML) to enhance and secure their forecasting capabilities.

With AI and ML, you can analyse large quantities of data in real-time, helping identify patterns/trends and ensuring accurate predictions based on truth.

Additionally, these tools eliminate human bias in the forecasting process; human biases, such as overconfidence or anchoring, can significantly affect the accuracy of forecasting.

Be more strategic with artificial intelligence and machine learning

Do note that artificial intelligence and machine learningshould not replace human expertise.

Instead, AI and ML should work with people to optimise the budgeting and forecasting process.

The forecasting process can be time-consuming, with many spending hours gathering and analysing data.

But by automating some of these tasks, team members can focus on developing business strategies or examining market trends.

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Keep rolling

Now that your time is freed up by AI and ML (hypothetically), evolve your approach, changing from static yearly budgets to rolling forecasts.

Revisiting and reforecasting on a quarterly or monthly basis helps maximise visibility and facilitates agility.

For example, if shortly after generating a budget, you lose a major client or the economy takes a drastic turn for the worse, your yearly budget will already be outdated; however, rolling forecasts can adapt to change, enabling figures to be altered as and when they’re needed.

With the right tools, strategy and mindset, rolling forecasts support better business decisions as they’re more accurate and useful than static budgets, which cannot adapt to change – perfect for dealing with uncertain times.

Sooner rather than later

According to a report by Gartner, artificial intelligence and machine learning are top investment priorities for CFOs.

While the use of AI and ML for forecasting is still in its early stages, I strongly suggest you look to implement it into your workflow as it's where the industry is heading.

Adopting these technologies early sets you up for long-term success, helping you unlock your power and create a competitive advantage.

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