By Tim Wakeford, vice president, financials product strategy, EMEA at Workday
Automation and Machine Learning (ML) are powerful tools which, if used in the right way, can help CFOs to transform the finance function to focus on agility and growth. To this point, the Martec report “Companies Using AI Will Add More Jobs Than They Cut,” found companies that have automated at least 70 percent of their business processes were six times more likely to have revenue growth of 15 percent per year or more.
ML and Robotic Process Automation’s (RPAs) ability to increase revenue and transform the finance function and boost revenues stems from the technologies’ ability to streamline tasks. For example, these technologies can provide faster access to critical data for decision-making and remove unnecessary back and forth with various departments. The success of reshaping the finance function is, however, dependent on automating the right tasks.
Minimise errors and deliver time-efficient business insights
Today’s finance teams are finding their work being condensed into a high-pressure period around the end of each month. This is largely due to the number of systems involved in the financial close process, and how reliant they are on input from various functions across the business. Intelligently automating finance processes will tackle these inefficiencies.
Through tools such as ML enabled anomaly detection, for example, tasks such as data entries are more likely to be posted correctly the first time, or brought up for review if they are incorrect. Removing the need for a high degree of manual intervention will contribute to the amount of time that can be redistributed to other tasks not only in finance, but in this case, across the business.
Automate to deliver insight
“An Adaptive Insights survey found that over two fifths of finance leaders agree that the biggest driver behind automation within their business is the demand for faster, higher-quality insights from executives and operational stakeholders.”
Yet, according to a recent McKinsey survey, corporate finance teams spend around 80 percent of their time manually gathering, verifying, and consolidating data. This leaves only 20 percent for higher-level tasks, such as analysis and crucial decision-making which means a higher risk of missing key insights or making mistakes. Therefore, one way for finance teams to achieve the goal of delivering time-efficient business insights is by automating repetitive tasks. By cutting the amount of repetitive administration jobs, the finance team will have significantly more time to be a strategic advisor to the business.
Optimise accounting processes and mitigate risk
Accounting continues to be another pain point for finance. Each month the finance team has to manually sift through invoices and other documentation, correcting errors in the general ledger — or risk a direct impact on a business’ cash flow. But this can change with the implementation of automation and ML. These technologies can help to match payments with invoices, and reduce any high-risk issues such as fraud in the process.
Internal and external fraud is an issue that costs businesses billions of pounds each year. ML can help mitigate this by flagging suspect payments to vendors in real-time. Instead of relying on manual audits on a sample of invoices, using ML will vastly increase the volume of invoices which can be analysed to ensure that the business is not making mistakes or fraudulent payments. Overall, RPA combined with ML can provide finance leaders with a great way of improving the way their accounting processes are managed — while also removing the risk of manual errors.
Spend your extra time on building your business strategy
There has been a huge rise in the volume and complexity of real-time data required from key stakeholders; and analysis of said data as a result. The ability to use automation to meet this demand for reporting and analysis is what will separate the successful finance teams from the others.
In fact, 26 percent of businesses in a global CFO study said that their primary reason for automating finance was to provide enhanced decision support, and ultimately improve business strategy. The time that is freed up from manual data gathering, consolidation, and formatting can now be spent on scenario planning, risk assessments, performance and predictive modeling – ultimately creating a much more strategic role for the CFO that will spur successful business growth.
To see real success, today’s finance function must consider how to implement automation and ML to support business agility. That means having CFO collaboration with a forward-thinking finance function where automation plays a pivotal role to truly drive finance transformation.
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