How will AI, ML, and deep learning influence financial planning and analysis (FP&A)?

New data processing tools like artificial intelligence (AI), machine learning (ML), and deep learning have enormous implications for financial planning and analysis. Especially as teams are inundated with more data and requests than ever before. In this article we will explore some of the most common challenges FP&A teams face today and examine how the application of artificial intelligence in finance can help bridge the gaps.

FP&A has been evolving rapidly in the past few years, racing to keep up with the digital transformation of business. And the technology FP&A practitioners use has evolved rapidly too – with the COVID pandemic only serving to accelerate processes that were already in motion.

Significant FP&A developments include:

• Evolutions of important forecasting methods and processes.
• Changes to the way that companies create plans.
• Changes to the role of the wider finance department in operations.

But perhaps more than anything else, FP&A developments have been driven by data.

Why does a surge in data necessitate an adoption of new tools?

The sheer volume of data available creates a double-edged sword for a financial planning and analysis function. On the one hand, it creates an opportunity to develop forecasts that are more accurate than ever before, and accurately simulate the effects of various scenarios on your organizations’ financial health. On the other, it creates a new surge of demand that might threaten to overwhelm your teams’ capabilities.

And this demand can only be met by a new generation of intelligent tools. One that allows teams to focus on the value-added work of strategic decision making, modeling, and scenario planning, while leaving data sorting and processing to the robots.

Enter AI and machine learning

Artificial intelligence and machine learning are no longer far-flung sci-fi concepts. AI and ML are actually extensions of already existing principles in enterprise software.

They allow tools – like ERP platforms – to “train” against incoming data in order to automate data segmenting, tagging, storage, and recall for a host of different tasks. This further eliminates the need for manual tagging and the time-intensive “cleaning” of datasets before use.

By doing some of the work for your teams (especially the low-value tasks that can’t be automated by conventional means), removing the burden of quality assurance, and guaranteeing a high-quality input for your systems, AI in finance can help you do more high-value work, and do it faster. All while avoiding the central problem with using technology to create predictive planning models – poor-quality input produces a poor-quality output, or what many refer to as ‘garbage in, garbage out’.

But beyond doing better work to a higher standard of accuracy, artificial intelligence for finance can bring something wholly new to the world of digital FP&A.

Towards automated forecasting

One of the most exciting possibilities of AI for FP&A comes in the form of AI financial planning. Imagine that – instead of performing your own forecasts more quickly – you could pick the drivers you wanted to explore, apply your data to an AI or ML model, check what happens when you include a new driver or revise an existing one, and then include or exclude it only if it was shown to have a significant effect. This ability would allow you to instantly forecast against any number of variables and create a totally automated model that will improve the quality of your forecasting through the continuous training of the machine learning model as your datasets are updated.AI and ML in finance mitigate time spent on non-value adding activities, ensure data quality, and can deliver analysis that increases your FP&A team’s strategic agility and its ability to add value to the organization. All while making the job more streamlined without actually changing core functions and skillsets.