The need for finance to deploy more efficient, dynamic ways of working predates the global pandemic, yet the events of 2020 are proving to be a significant catalyst for technology transformation. For finance, that means embracing digital technologies, such as machine learning, that can be applied to core processes.
CFOs have long been looking to reduce the time spent on processes such as close, consolidations, reporting, and payroll, and the COVID-19 pandemic and changes to where and how businesses operate have made this shift an imperative.
Thomas Willman, principal, finance advisory global practice leader at The Hackett Group, shares, “Finance has had to transform in so many ways in 2020. What hasn’t changed is that all of this work still has to be done; what has changed is that it has to be done away from the office. Finance professionals are exploring ways to increase automation and apply machine learning to identify patterns and make recommendations that previously would have required manual intervention.”
What Intelligent Automation Means for Finance Day-to-Day
In the right hands, digital technologies and greater automation can be a fantastic combination for CFOs to transform the finance function. However, success will depend on identifying and prioritizing tasks that will deliver the greatest value. When it comes to automation, the first goal for a finance team should be to automate the repetitive and transactional tasks that require human effort or manual intervention. Doing this will free up a significant amount of finance’s time to be more of a strategic advisor to the business.
The second goal is to identify where digital technologies, such as machine learning, can be applied to detect, predict, or recommend, ultimately infusing a greater level of “machine” intelligence into a transaction or process. Once the machine sees a pattern, it’s capable of applying the same result over and over, and as the machine continues to learn, it gets smarter and smarter.
The result—automation paired with machine intelligence—creates intelligently automated processes, thus eliminating much of the time that has been previously spent on traditional transactions and processes. A Workday Adaptive Planning survey found that over 40% of finance leaders say that the biggest driver of automation within their organizations is the demand for faster, higher-quality insights from executives and operational stakeholders.
The research in Accenture’s "Charting a Path to Intelligent Automation" report states, “About three-fourths of CFOs surveyed say they are helping to drive business-wide transformation, so getting things right in the finance function is critically important. Thinking through the end-to-end strategy, methodology and deployment of intelligent automation tools in the context of shaping the organization rather than fixing a specific pain point is essential.”
To finance, of course, numbers matter, and when you put automation under the spotlight from a cost and efficiency perspective, the evidence speaks for itself. Research from an Argyle webinar featured in CFO Dive states, “A company with a 20-person finance team typically loses the equivalent of 1,920 hours annually, or an estimated $124,800 in costs, to manual tasks. A big company with a 100-person finance team might lose 9,600 hours, at an estimated $624,000 a year.”
Where Machine Learning Can Drive Finance Transformation
Despite the obvious financial and operational benefits of machine learning, many finance functions have been slow to adapt. Accounting, supplier management, procurement, and auditing are all key areas that are ripe for automation, yet the risk—particularly for large companies operating across multiple geographies—can act as a barrier to innovation. Teams in each of these areas are also immersed in “keeping the lights on”—often at the cost of transformation.
Transaction processing is another barrier that prevents finance from achieving transformation and ultimately delivering a better business partnership. It's not surprising that it’s the first port of call for CFOs looking toward automation.
“Automation provides finance leaders with a great way of optimizing the way they manage their accounting processes. This has been a painful area for finance for such a long time and can have a direct impact on an organization’s cash flow,” says Workday’s Barbara Larson, general manager, Workday Financial Management. “Finance spends a huge amount of time sifting through journal entries, invoices, and other documentation to manually correct errors while machine learning could automate this, helping to intelligently match payments with invoices.”
Machine learning can also mitigate financial risk by flagging suspect payments to vendors in real time, enabling a much more effective and efficient process. Internal and external fraud costs businesses billions of dollars each year. The current mechanism for mitigating such instances of fraud is to rely on manual audits on a sample of invoices. This means looking at just a fraction of total payments, and is the proverbial “needle in the haystack” approach to identifying fraud and mistakes. Machine learning can vastly increase the volume of invoices that can be checked and analyzed to ensure that organizations are not making duplicate or fraudulent payments.
“Ensuring compliance to federal and international regulations is a critical issue for financial institutions, especially given the increasingly strict laws targeting money laundering and the funding of terrorist activities,” explains David Axson, CFO strategies global lead, Accenture Strategy. “At one large global bank, up to 10,000 staffers were responsible for identifying suspicious transactions and accounts that might indicate such illegal activities. To help in those efforts, the bank implemented an AI system that deploys machine-learning algorithms that segment the transactions and accounts, and sets the optimal thresholds for alerting people to potential cases that might require further investigation.”
Improving Financial Planning and Analysis
If you subscribe to the view that the role of financial planning and analysis (FP&A) in the future will be to deliver data-driven decision support for the business in real time, then it’s clear that finance must transform its processes to meet this vision—and automation is a central component in this transformation.
Research from McKinsey states that on average, approximately 60% of finance activities can be fully (40%) or mostly (17%) automated with technologies available today. Where FP&A sits on this spectrum is open to debate, but the same study claims that many tasks in this category have the ability to fully (11%) or mostly (45%) be automated.
Few could argue that there’s a transition going on from a spreadsheet-based FP&A culture to a much more collaborative, automation-based FP&A culture. It’s hard to say where we are in that transition, but the desire to move toward analytics and technology skills in finance versus spreadsheet skills is a pretty dramatic shift. In a CFO Insights survey, 78% thought Microsoft Excel® skills were most important two years ago; that figure is now 5%. The automation in applications that has become available to finance professionals is driving that shift.