FP&A On Steroids With AI Powered Financial Forecasting

Imagine a football coach never being present at the match of his team. He can only instruct the players prior to the match and evaluate the results afterwards. During the match he will not be able to predict what will happen next and make tactical or strategic changes if needed. During the match the players would have to figure out on their own how to make the best decisions without guidance. Would that lead to best possible results? I think not!

Many companies build an annual budget and then wait for the actual results to come in to take corrective measures. This is basically similar to a football coach being absent. There will be only a plan and when the actual monthly results come in they can evaluate and make decisions.

Fortunately there are companies that have forecasting processes that will help them to look ahead and change tactics during the course of the month. The problem with these forecasting processes is that they are manually created and typically bottom up. These forecasts lack agility, accuracy, dimensionality and are full of human biases.  Traditional forecasting is also a labor-intensive and time consuming process

Modern technology offers the possibility to put FP&A software on steroids with AI powered financial forecasting.

What Promises does AI Hold for FP&A’s Forecasting Process?

AI/ML is currently widely used in many areas of a business: to segment customers, detect fraud or predict risk. Although FP&A teams are showing great interest in applying AI/ML, the use cases are slim. Companies should spend less time developing detailed plans and more time taking action to counter threats and avail more opportunities. So, the question remains what value could automation and AI add to the forecasting process?

Improved Quality and Accuracy: AI could be used to remove human biases and improve the manual forecast quality and accuracy. This leads to better decision making using more accurate predictions.

More Time for Value-add Activities: Imagine how you could use the time that is saved by automating the forecasting process. AI reduces manual effort needed to generate the forecast, freeing up valuable time. This time can be allocated to value add activities like robust scenario analysis and partnering with the business to develop improved strategies. 

Quickly React on Changing Circumstances: When circumstances in the business are quickly changing, a manual forecasting process is not agile enough to quickly adapt to these changes. Think about external factors like weather or commodity prices, or internal factors like a significantly changed sales funnel or winning/losing an important customer. AI powered forecasts will be able to quickly re-forecast based on these changing circumstances and this will empower the business to take immediate action. Better forecasts will lead to better decision making and create a competitive advantage.

How Far Are Companies With Implementation?

In my 20 years of experience in working with FP&A teams and CFO’s I have seen the last few years an increased interest in leveraging AI in forecasting. Some companies have experimented with it or use it in isolation within one department or business unit, but few have a real AI powered forecasting process embedded. “AI can accurately analyze high volumes of data and identify patterns and apply decision rules much faster than people using traditional technology or spreadsheet-based solutions, but only 2% of FP&A functions have adopted AI,” says Richard Ries, VP, Advisory, Gartner. 

What could be the reason for this limited adoption? I can think of several reasons why companies are not as far as they would like to be:

• Limited availability of out of the box software solutions 
• Poor data quality (garbage in garbage out)
• Lack of the right skills (data scientists)
• For some FP&A teams implementing algorithms replacing their day jobs will feel like profesional suicide

If Adoption is Limited, are There any Companies who Nailed It?

One of the frontrunners in forecasting using machine learning is Microsoft.  They use their own technology (Azure Machine Learning service) to improve forecast accuracy and they eliminated manual forecasting almost completely. The company also enhances its financial planning with insights from external data.

Victor Casalino, CFO for the Americas business at Microsoft, recently shared his thoughts in a webinar with Prevedere. Here are some highlights of what Victor had to say.

What Trends do we See at Born Capital?

At many corporations there is a strong push from IT to take over the data analytics activities from finance. Although there might be synergies with technology, IT lacks the skills to really understand financial data and interpret the complex logic and understand the governance around FP&A. 

FP&A people are not software engineers, so they need out of the box no-code software tools. We see a clear trend of FP&A software vendors like OneStream, Oracle, Anaplan and Board International embedding algorithmic forecasting as part of their planning solutions, but we also see a new generation of FP&A software tools being created just for this purpose, a great example is Aurora Predictions.

What The Future Holds & Why Born Capital Will Invest in This technology?

A football coach is obviously present during the game. He sees what is going on and tries to predict what happens next. He will quickly decide to change the strategy if his predictions will not lead to the desired success. The same should be done by companies to predict business moves accurately in real-time based on changing circumstances so that you can level up your strategies & tactics while you are in the middle of the game. 

Any CFO seeking to transform the forecasting process should consider leveraging machine learning as a key part of producing financial forecasts. At Born Capital we expect that AI powered FP&A solutions will become widely adopted in the next decade and make this an important part of our investment focus. 

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