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.
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.”
“RPA combined with machine learning provides finance leaders with a great way of optimising 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.”
“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.”