Here’s how more advanced methods of automation, including machine learning, can help CFOs transform the finance function to be more of a strategic advisor to the business.
In a recent McKinsey survey, only 13 percent of CFOs and other senior business executives polled said their finance organizations use automation technologies, such as robotic process automation (RPA) and machine learning. What’s more, when asked how much return on investment the finance organization has generated from digitization and automation in the past 12 months, only 5 percent said it was a substantial return; the more common response was “modest” or “minimal” returns.
While that number may seem low right now, automation is coming to the finance function, and it will play a crucial role in furthering the CFO’s position in the C-suite. Research suggests corporate finance teams spend about 80 percent of their time manually gathering, verifying, and consolidating data, leaving only about 20 percent for higher-level tasks, such as analysis and decision-making.
In its truest form, RPA will unleash a new wave of digital transformation in corporate finance. Instead of programming software to perform certain tasks automatically, RPA uses software robots to process transactions, monitor compliance, and audit processes automatically. This could slash the number of required manual tasks, helping to drive out errors and increase the efficiency of finance processes—handing back time to the CFO function to be more strategic.
What Automation Means for Finance Day-to-Day
According to the report “Companies Using AI Will Add More Jobs Than They Cut,” companies that had automated at least 70 percent of their business processes compared to those that had automated less than 30 percent discovered that more automation translated into more revenue. In fact, the highly automated group was six times more likely to have revenue growth of 15 percent per year or more.
In the right hands, automation and machine learning can be a fantastic combination for CFOs to transform the finance function, yet success will depend on automating the right tasks. The first goal for a finance team should be to automate the repetitive and transactional tasks that consume the majority of its time. Doing this will free finance up to be more of a strategic advisor to the business. An Adaptive Insights survey found that over 40 percent of finance leaders say that the biggest driver behind automation within their organizations is the demand for faster, higher-quality insights from executives and operational stakeholders.
Accenture’s global talent and organization lead for financial services, Andrew Woolf, says the challenge for businesses is to “pivot their workforce to enter an entirely new world where human ingenuity meets intelligent technology to unlock new forms of growth.”
Where Automation and Machine Learning Can Drive Finance Transformation
Transaction processing is one of the major barriers preventing finance from achieving transformation and the ultimate goal of delivering a better business partnership. It’s not surprising that it’s the first port of call for CFOs looking toward automation.
“RPA combined with machine learning provides finance leaders with a great way of optimizing the way they manage their accounting processes. This has been a painful area of finance for such a long time and can have a direct impact on an organization’s cash flow,” says Tim Wakeford, vice president, financials product strategy, EMEA at Workday. “Finance spends a huge amount of time sifting through invoices and other documentation to manually correct errors in the general ledger, 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. 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 which 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.”