How Automation and Machine Learning Are Reshaping the Finance Function, Part Two

Take a deeper look at the specific use cases of automation and machine learning across finance, and what they mean for traditional, day-to-day finance activities.

Steve Dunne December 09, 2019
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In the first part of this blog, we looked at the impact of automation and machine learning on the CFO function. In this article, we take a deeper look at specific use cases across finance, and what they mean for traditional, day-to-day finance activities.

Improving Financial Planning and Analysis (FP&A)

If you subscribe to the view that the role of Financial Planning and Analysis (FP&A) in the future of finance 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. Automation is a central component in this transformation.

Research from McKinsey states that on average, approximately 60 percent of finance activities can be fully (40 percent) or mostly (17 percent) 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 are fully (11 percent) or mostly (45 percent) automatable.

Few could argue that there's a transition going on from a spreadsheet-based FP&A culture to a much more automation-based FP&A culture. It's hard to say where we are in that transition, but the desire to move towards technology skills in finance versus spreadsheet skills is a pretty dramatic shift. In a CFO Insights survey, 78 percent thought Excel skills were the most important skill two years ago, and that number is now 5 percent. The automation in applications that have become available to finance professionals are driving that shift.

Automating Reconciliations

Finance functions today spend far too much time reconciling data across various systems. Think of the transactions between internal and external systems, as well as across various ledgers. The manual nature of the task means mistakes are inevitable, with duplicate entries or data entry errors.

Robynne Sisco, co-president and CFO at Workday, saw this firsthand in previous organizations where she worked. “Each month finance would have to close the period, access the data, reconcile it, format it, and analyze it. By the time we delivered the numbers to the business, it was two weeks after the period ended and too late to take action,” she says.

Using rules and patterns, machine learning can provide finance professionals with the ability to identify a large number of these reconciliations, understand what the problem is, and in some cases, correct the problem or flag it for human intervention.

Closing the Books Faster and More Efficiently

At most companies, even the slightest mention of closing the books is enough to send blood pressures soaring across finance. That’s in large part due to the number of systems involved in the financial close process, with input from various functions across the business.

Intelligent automation will help finance unlock its true potential as a strategic guide.

For finance teams dealing with multiple, disparate systems, new tools and resources available to help close the books more efficiently and accurately fall into two categories: cloud technologies and machine learning. With cloud technologies, a key advantage for the user is easy deployment versus on-premise software. Updated versions are also much easier to deploy, and the cloud offers the ability to quickly and efficiently scale up and link to different tools.

“Currently, much of today’s finance work is condensed into an intense period around each month end, with many manual entries being processed at this time,” says Wakeford. “Intelligently automating core transactions and processes will address this inefficient working pattern and help ensure entries are posted correctly first time, removing the need for a high degree of manual intervention. A good example of this is machine learning enabled anomaly detection which will identify potentially anomalous transactions and automatically correct coding or surface them for review before the entries are posted.”

Security is also a strength of cloud technology, enabling enterprises to leverage expertise rather than develop it. Many vendors are strategically moving their solutions to cloud models with the long-range plan to offer only cloud-based tools—a major trend for companies looking to invest in finance tools. For people driving transformations within finance, the increasing availability of cloud tools is a significant opportunity.

Machine learning is a hot topic among finance professionals because it is expected to perform a lot of the work that companies currently have to do manually. For example, with multiple charts of accounts, machine learning is starting to perform some of the mapping and analysis that had previously been manual, helping companies with their closing and consolidation process. 

Delivering Faster, Better Analytics and Insights

While intelligently automating the processes mentioned above will give finance a huge boost, it is the ability to meet the increased demand for reporting and analysis, and the rising volume and complexity of data required in near real-time from key stakeholders, that will be transformed by intelligent automation.

 Savvy CFOs will streamline their processes through automation and machine learning.

In fact, 26 percent of organizations in a global CFO study said that their primary reason for implementing automation into the finance organization was to provide enhanced decision support that will make them and their teams more strategic.

RPA will likely sound the death knell for manual data gathering, consolidation, verification, and formatting across finance. Today these non-value-added tasks eat up an enormous amount of time, leaving the finance team little time for analysis. And, as manual, routine processes become more automated, finance teams will be able to focus on value-add activities, such as scenario planning, risk assessments, and performance and predictive modeling.

“With new sources of data come new analytics techniques and a search for insights. Organizations will apply their usage of automation and data mining techniques over planning, delivery, and outcome data to enhance visibility and tracking across those processes,” says Jason Byrd, Partner, technology business management, KPMG. “New insights will allow teams to capture timely data to analyze velocity, deployment, and customer response, creating a feedback loop of decision-making and course correction.”

On the journey to becoming a strategic business partner, savvy CFOs will streamline their processes through automation and machine learning building the confidence needed to support a rapidly changing business environment. Intelligent automation will help finance unlock its true potential as a strategic guide from its roots as a number cruncher buried under a mountain of transactional processes. Today’s finance function must support business agility, and that means CFO collaboration with a forward-thinking finance function where automation will play a key role in driving finance transformation.

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