Founded in 2013 by Larysa Melnychuk, the International FP&A Board is a high-profile and truly global professional think-tank for senior finance professionals at CFO- and finance director-level. Its mission is to identify and support new global trends, skillsets and thought leadership in corporate financial planning and analytics (FP&A).Operating on a nonprofit basis, it now has professional chapters in 25 cities round the world, with more to come: “We are opening new chapters in Houston and Washington DC before the end of the year, which shows how in demand our financial planning and analytics is in enhancing decision-making in this incredible business environment,” Melnychuk says.
As with many savvy businesses these days, the FP&A has recognised the benefits of flexible working. “We have a fantastic partnership with IWG – we need premium locations for meetings, which it provides,” explains Melnychuk. But just as remote working has become a more interactive and efficient way of operating, allowing companies to be more reactive to upscaling and downscaling, for example, leveraging new technologies, is proving equally vital for future success. “The ‘span of predictability’ for every business is shrinking, so we are less able to forecast what is going to happen over the next 12 months,” says Melnychuk. Flexible systems of working and collaborating are therefore essential across the board.
PREDICTING THE FUTURE
Just a few years ago, ML was not very much used by finance professionals – but the world has changed. Travelling the world with the FP&A Board, Melnychuk noticed a lot of developments in ML, a branch of artificial intelligence (AI) that, as IBM describes on its website, “enables a system to learn from data rather than through explicit programming”. When it comes to using machine learning for financial planning, you first need to prepare your historical data – gather it together, clean it, remove outliers and divide into sets. Assuming machine learning is the right too for the job (it isn’t always), the data is crunched with various algorithms that identify patterns and make predictions.
One of the biggest challenges of FP&A is Predictive Analytics (PA). PA and ML go hand-in-hand, as predictive models typically include a ML algorithm. These predictive models can be trained over time to respond to new data or values, delivering the analytical results the business needs.
IBM states: “With big data, it is now possible to virtualise data so it can be stored in the most efficient and cost-effective manner, whether on premises or in the cloud. In addition, improvements in network speed and reliability have removed other physical limitations associated with managing massive amounts of data at the acceptable speed. Add to this the impact of changes in the price and sophistication of computer memory and it’s now possible to imagine how companies can leverage data in ways that would be been inconceivable only five years ago.”
Leveraging data is the key to supporting financial decision-makers and it is machine learning that is giving companies the edge when it comes to financial planning, especially when there is the threat of an economic crisis, for instance. It can give insights into future sales, stocks and trades more easily than more traditional methods. “Global organisations have to make decisions very quickly, almost in real time,” explains Melnychuk. “They have to think about how each particular investment or acquisition will help with the strategic direction of the company. Modern technology can do this very quickly with the use of data. If you can do real-time accurate forecasts you can react today. This is why more and more organisations are beginning to use artificial intelligence, specifically machine learning, to forecast.”
STAYING AHEAD OF THE COMPETITION
Microsoft is a good example. “Not only did they implement predictive analytics, driver-based planning and machine-learning but also implemented it through the one platform – to forecast almost in real time at the touch of a button,” says Melnychuk. “So one of the biggest companies in the world is able to forecast quickly and dynamically. This is a huge message to everyone in the finance profession. Invest in your technology – automate, work with this and then you can compete.”
Takeshi Murakami, Group Controller at Microsoft, told fpa-trends.com that, by 2020, the “digital universe will be 44 zettabytes – that’s a ten-fold increase from 2013”. He continued: “The size of the digital universe will double every two years at least… Given the environment, we believe that being a winner or a loser in the market will heavily depend on how well we manage data and leverage technologies. The new big data can provide you with insights you were never able to create before… With a combination of machine learning and bots we get to the position that we can say what to do, automating the decision-making process and moving really fast from data to decision.”
Some people may be wary about AI but Melnychuk wants to reassure professionals that the human element is essential, too. “When human intelligence is combined with artificial intelligence, it really can create very powerful solutions in FP&A,” she says. “We can now see examples of how machine-learning can now help companies to forecast not only their revenue line but the whole profit and loss account. The benefits of using machine learning are that it is very quick and gives a lot of insights. The problem with machine-learning forecasting is that it is based on historical data and doesn’t mean that it can accurately predict the future. If it is not reviewed by human intelligence it is not going to be successful. Another problem with this method is accountability: many managers claim that they cannot be responsible for the forecasts and budgets that were created by machines.
“Machines are good at forecasting ‘business as usual’, something which is based on historical data and traditional assumptions. They will take into consideration so many factors and they will do this very accurately. The power comes from the combination of artificial and human intelligence. With machine learning we can automate tasks and release time for very valuable analysis that only human beings can do. Machines enhance our abilities.”
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