Digital FP&A: Looking into the future
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Digital FP&A: Looking into the future

Understand the power of arfiticial intelligence in finance – and especially FP&A – today and where it’s going. Sascha Alber, Unit4’s Head of FP&A and Product Director, recently spoke about the power AI is bringing to FP&A and what that means for the future with FP&A Trends London Circle.

Why are AI and machine learning in finance so powerful for business?

In the real world, a lot of things take time, particularly when done manually by humans. That's not a bad thing in the right circumstances. But in many instances, for both users and the people performing those tasks, that time could be better spent elsewhere on more important tasks.

AI can perform repetitive, manual tasks in a fraction of the time, and coupled with machine learning (ML) it can even learn to do them better.

But how is this relevant for FP&A? We believe FP&A represents one of the most promising machine learning applications in finance. Let’s take a look at why.

The time challenge

To understand this, first ask the question, what’s your organization’s biggest challenge or pain point?

For most CFOs and FP&A teams, one of the biggest challenges is time. Not enough of it to do everything that’s needed.

A certain amount of finance work can be repetitive. Although essential, these tasks might generate only little value, and might include things like bank reconciliation, and checking variations between budgets and actuals. Think about how often you do these tasks, and the minutes quickly add up.

It’s also time you’re not spending on being a business partner for the organization and helping the business to achieve its goals by supporting the departments with management, control, risk, and funding.

How does AI Smart Forecasting solve the time deficit?

For clarity, let’s consider one example. Imagine a company running numerous gas stations throughout the country, that wants to predict the personnel costs and hours needed for a station.

To do this they have to identify how many people they need each week and each day in order to man all these stations, how many hours this will be in total, and how much it will ultimately cost. So what do they do?

The first challenge is the number of different influence factors involved, for example, the location of each station, varying gas prices, even considerations such as weather and public holidays.

Problems like this can take your teams a lot of time to consider. AI, however, learns from information that you feed it. And once it has this information, it can begin to look for patterns. Making it possible for financial forecasting using machine learning to predict future needs based on assumptions it can derive from the data.

So in this example, if you’re trying to predict your personnel cost and hours for a day or week next month, the AI can look at historical data from previous months and years, and use all the factors you’ve given it to make pretty accurate predictions.

What’s most important here is that it can do this almost instantly. But are these predictions as reliable as your own?

The role of data and the forecast value

AI accuracy will always depend on the data. And most importantly, the data’s quality.

It’s critical here to understand there’s a difference between the amount of data and data quality. And that the volume of data has no impact on the quality. Lots of data is just lots of data. Quality data actually reflects the situation you are analyzing.

In this example, if your AI is using data from 2 years previous, but since then something dramatic like a road closure or extension has happened, then the predictions it makes won’t reflect the actual situation.

So you have to understand the value of the forecast before you use it. Initially, people believed the value of a forecast only came from its accuracy. But is that so?

At Unit4 we’ve learned it’s a little more nuanced. Accuracy matters, but it has to be balanced with speed and adaptability. This has proven to be especially true in 2020, where organizations have needed to react more quickly.

The future of FP&A and AI

What’s interesting is AI enables companies to become this adaptable. Many Unit4 customers are now seeing forecasts that used to take months to create, now only taking minutes.

But how can AI help organizations benefit from the time they are now saving?

As AI integrates further into your software, it is becoming more accessible for users. No longer is it only the realm of data scientists and the like. All staff can access, use, and benefit from AI in the workplace – and the applications of AI in finance are increasing all the time.

Embedding it into your operational environment will allow you to identify value drivers faster than ever and have the time and adaptability to take action. Instantly showing you the impacts of changing variables.

What’s more, as AI learns more and more about your organization over time, it will become capable of more and more sophisticated functions. Moving beyond simple forecasting and becoming able to support or even automate planning itself in some instances.

So AI would not only identify key business drivers and how they will change over time but would also be able to use extremely detailed learnings from multiple sources; including what’s worked in the past, to advise and support you in planning how to respond to drivers. And would be able to do this at any level from organization-wide down to project by project.

And all this is only the beginning.

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Discover how Unit4 can help

To understand how Unit4 can help you to bring the power of AI, machine learning, and smart forecasting to your FP&A teams, check out everything our solution can do at our FP&A product page or click here to book a demo.