AI in finance – what you need to know
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AI in finance – what you need to know

The implications for finance departments of new data processing capabilities like AI and machine learning 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 technology department uses has 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 perhaps more than anything else, 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 – actually 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. 

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 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. And also provide 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 an evolution of the roles which your people play. More often than not for the better. 

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Integrate AI into everyday financial operations

To discover how Unit4 can help your teams to integrate AI into everyday financial operations and create a better people experience for your whole enterprise, click below to book a short demo of our solutions’ capabilities and experience them for yourself. 

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