AI in finance – what you need to know

The implications for finance departments of new data processing capabilities like AI and machine learning (ML) are enormous. Especially as finance teams are inundated with more data and requests than ever before. Here, we want to explore some of the most common challenges finance teams are facing today and explore why AI and ML might provide a solution. 

Finance has seen a rapid evolution of its responsibilities and capabilities in the past few years, racing to keep up with the digital transformation of business. And the use of the technology department has also evolved rapidly too – with the COVID pandemic only serving to accelerate processes that were already in motion. 

These changes include evolutions of the methodology used in forecasting, changes to the way that companies do planning and budgeting, and changes to the role of the wider finance department in operations. But what is really notable is that they’ve been driven by data. 

Why a surge in data demands an adoption of new tools

The sheer volume of data available creates a double-edged sword for an analysis and planning function. On the one hand, it creates an opportunity to develop more accurate forecasts than ever before, and accurately simulate the effects of various different 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 bots. 

Enter AI and machine learning

“Artificial intelligence” and “machine learning” are – far from being far-flung sci-fi concepts – in many cases they are extensions of already existing principles in enterprise software. AI in banking and finance is an area of growing interest in everything from automated trading to risk management and mitigation.

These technologies allow tools – like ERP cloud 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. 

Click to read Refine your plan-to-profit process with ERP 2023 Gated

The application of artificial intelligence in finance

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 and machine learning 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 – garbage in, garbage out. 

But beyond doing better work to a higher standard of accuracy, AI and machine learning can bring something wholly new to the world of finance. 

Towards automated forecasting

Imagine that – instead of performing your own forecasts more quickly – you were able to pick the drivers you wanted to explore, apply your data to an AI/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 not just instantly forecast against any number of variables but create a totally automated model that will create continuous improvements to the quality of your forecasting through the continuous training of the machine learning model as your datasets are updated. 

AI and ML mitigate time spent on non-value add activities, ensure data quality, and can deliver analysis that increases your team’s strategic agility – and its ability to add value to the organization. All while making the job more streamlined without changing core functions and skillsets. This translates to a risk-management approach that can truly consider all variables and still be both practical and timely. 

Answering the complex questions simply – and quickly

Backed by the computing power afforded by modern cloud-based systems, AI can analyze large and complex datasets – originating both within and outside of your organization – more efficiently and accurately than humans.  

And beyond crunching numbers, this means for the first time that it’s now possible for AI and ML powered systems to analyze unstructured data. By scanning and processing key words and phrases in filings, research, your own records, and even news coverage and online chatter, it’s possible to create accurate pictures of trends within your industry and understand how they might impact your organization. This also provides a picture of how you can safeguard against risks and capitalize on opportunities. 

All of which will help your department to better fulfil its purpose of empowering other teams within the organization to be innovative and do better work. 

How AI finance can take the strain off your teams

Beyond these more advanced uses, AI’s most compelling use in finance departments is significantly more mundane. An AI system can be trained with relative ease to make basic “yes” and “no” decisions based on finite inputs.  

This means a lot of the low-level, low-value work that would previously have occupied much of your teams’ time – particularly in realms like regulatory compliance – can be passed off to the machines. Giving everyone time to focus on the high value work that really matters – something we could all do with considering as much as 33% of the average working professional’s time is currently spent on administration. 

But make sure you have the right people in place to manage it

The application of AI in accounting and finance means CFOs will need to reconsider the way their teams work. But it also means they’ll need to carefully consider who will be on those teams. Entirely new roles will be required in some cases – and this means that your AI strategy must be carefully considered in line with your organization’s wider people strategy. 

The finance department isn’t going to be completely automated any time soon – and in fact AI will not so much see the replacement of people with machines as it will be an evolution of the roles which your people play. More often than not for the better. 

How can Unit4 help you leverage AI finance management?

Unit4’s people and project centric enterprise software solutions are already leveraging powerful AI and ML based processes and features that can simplify every aspect of your finance workflows. From virtual assistants capable of retrieving, analyzing, and cross referencing any of your teams’ financial data to planning and forecasting tools capable of spotting emerging trends rapidly - allowing you to both mitigate and manage risk and also capitalize on opportunities with greater flexibility and agility.

To learn more about what how we can help you integrate AI and ML into your workflows, check out our product pages for ERP and FP&A today or click here to book a demo.

FAQs

Is AI the future of finance?

It’s probably better to ask what the future of AI will be in the world of finance. AI will play a significant role in finance’s future - but mostly in the capacity of a “load lightener”, taking over the management of large datasets, helping to automate compliance, and allowing finance experts and leaders to focus on more value-added work that can’t be handled by the application of rules-based and emergent models to problems with simple “yes/no” answers. As time goes on, AI will most likely increasingly be used to spot emerging trends in large datasets - helping create more robust strategic financial plans faster and providing more granular and accurate pictures of the health of the organization.

How can my finance team adopt new AI tools?

The question of how your finance team can use AI in finance is one with multiple different answers. At the most general level, you’ll need to approach the problem from the questions of your organization’s goals, challenges, and needs. The tools have to fit the parameters of the job, and your organization’s strategic trajectory.

What is the best way to prepare for future finance innovations?

You should approach finance innovations in the same way that you approach all other digital transformation projects - with an eye to your organization’s long-term goals and needs. But more than anything, you should focus your resources into continuous learning and upskilling for your teams, who will be the real “make or break” in terms of the success of any new tools and processes that do become available in the future.