- Finance teams currently spend 65% of their time on manual processes; automation can reduce that by 30%
- AI can automate corporate financial workflows, eliminate repetitive tasks, and guide better decision-making
- Adoption of predictive analytics in finance is expected to skyrocket in the coming years
In corporate finance operations, one small mistake can exact a huge toll. When a major consumer company recently disclosed a multimillion-dollar accounting goof that had gone undetected for three years, it was forced to revise financial statements going back to 2016. The revelation sent its stock plummeting and prompted an ongoing SEC probe.
By digitizing corporate finance workflows and reducing manual tasks, emerging technologies like machine learning and AI can reduce human error, improve efficiency, prevent fraud, and reduce staff turnover. In fact, 40% of all finance processes can be fully automated and another 17% can be mostly automated, according to McKinsey research.
Emerging technologies are doing more than streamlining and automating financial workflows. Ultimately, AI’s greatest potential benefit for CFOs, managers and accountants is to help them make smarter, faster data-driven decisions.
“The market is increasingly dynamic,” says Meenakshi Tripathy, senior director and head of finance business products at ServiceNow. “The finance operating model has to change to enable business agility. How can my people collaborate better? How can I transform the way they work? How fast can we innovate?”
Recent studies point to an array of benefits for companies that digitize all aspects of their finance operations. Those that deploy machine learning and AI as part of broader digital initiatives have even more to gain.
One study, by Oracle, finds a direct correlation between AI deployment in finance and higher revenue. And a majority of finance leaders, according to a 2019 survey by Robert Half, already expect AI to help eliminate human error, slash costs, and boost employee productivity within three years. Yet to date just 24% of surveyed companies have invested in AI-powered financial management tools.
Finance personnel spend 65% of their time on manual processes. Automation tools can slash that time by 30%.
What’s the holdup? “The AI is still evolving,” says Nigel Duffy, global AI leader at EY. “But after you solve the technology problem, you have to solve the adoption problem, which requires both cultural comfort and having the right IT infrastructure in place. Then you have to solve the compliance risk and trust challenges.”
Still, finance AI is maturing fast. A majority of software developers have already begun adding it to new enterprise resource planning (ERP) products. And 80% believe existing finance software will eventually be totally replaced by AI.
Solving the complexities of financial close
Financial close is a monthly ordeal for most companies. Every month, finance teams must audit and reconcile all transactions to ensure regulatory and tax compliance. It can take days—or weeks—for teams to gather and manually input data. A misplaced decimal point can result in costly errors.
“What I’ve heard from a lot of customers is that they have too many siloed and outdated systems and their processes are too complex,” Tripathy says. “Customers are looking for better productivity, better information, better visibility, and lower risk.”
ServiceNow’s corporate finance team uses ServiceNow Finance Close Automation to manage its monthly close. As a result, they have seen a roughly 30% reduction in accounting errors, a 15% increase in staff retention, and shaved a full day off what had typically been a six-day close cycle. “There are companies whose close runs 15 days,” Tripathy says. “Their savings will be much higher.”
It’s a good example of how digitizing financial workflows can boost companies’ bottom lines—even before considering investments in AI. Finance personnel spend 65% of their time on manual processes. Process automation techniques can slash that time by 30%, according to Hackett Group research, reducing inefficiencies in payroll, budgeting, auditing, and compliance.
Cracking down on fraud
For most companies, internal fraud is difficult to detect. It’s often done piecemeal and doesn’t leave a clear data trail—or leaves one that has been intentionally muddied by perpetrators.
This problem caused $7 billion in corporate losses in 2018, according to the Association of Certified Fraud Examiners. Aside from outright cash diversion, employees at more than 1 in 4 enterprises (27%) clock in for hours that they haven’t worked. It takes auditors 16 months on average to uncover instances of fraud.
Enter machine learning and AI. “Forensics for fraud detection is an area where AI has had significant impact in the last 10-plus years,” says Duffy. “By detecting anomalies, they provide leads to pursue as opposed to needing to get every little piece of information perfect.”
By tracking and analyzing employee behavior in real time, bots can uncover misdeeds more quickly and flag workers for suspicious activity. For example, AI can compare unusual expense invoicing with known instances of expense abuse to detect fraud early or proactively prevent it.
For corporate finance leaders, time is the greatest gift of workflow digitization. Today, finance pros spend roughly 80% of their work day manually gathering, consolidating, and verifying data, with only 20% left over for analysis, according to Adaptive Insights. Almost two-thirds of finance operations workers say they have no time for higher-level analytic work.
“With AI, people can focus on things that help your organization move forward,” says Aman Mann, co-founder and CEO of spending software company Procurify.
For example, AI-powered analytics tools can improve budget planning. That’s a screaming need: Just 1% of firms can forecast costs at 90% accuracy 30 days in advance, according to a recent study by BPM Partners.
“If we have very good predictive models for demand management, we’re able to use them in a ‘what if’ context,” says Duffy. “What if we have a cold summer versus a warm summer? How can we use that system to enable ‘what if’ scenarios to do better strategic thinking?”
Adoption of predictive analytics tools, which increasingly rely on machine learning and AI, is expected to skyrocket in corporate finance in the coming years. In 2017, just 24% of companies used predictive analytics for finance, according to Grant Thornton. Today, 88% of CFOs say they plan to use those tools within the next two years.
Ultimately, investment in AI may be less about the business costs that bots can subtract and more about the business value that people can add. “Finance people want to contribute more to the business,” Duffy says. “But those ambitions are constrained when mundane work leaves no oxygen to make strategic contributions. AI will make that possible.”