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Applying Machine Learning to Optimise Finance Processes

Contributed article by Henner Schliebs, Head of Finance Audience Marketing, SAP

Machine learning is becoming a buzzword in nearly every industry today, and finance is no exception. As its applications and capabilities grow, machine learning is powering finance departments towards the next generation of digitalisation. Here are three ways finance professionals can embed machine learning to move up maturity in their financial management systems or go beyond the spreadsheet to uplevel their accounting and reporting capabilities to meet today’s business challenges.

Embrace digitalisation in your finance strategy

Before finance departments can unlock the value of machine learning, they must first ensure that digital tools are at the core of their operations. Machine learning depends on the availability of live business (and big) data, integrated across the organisation – which is only possible to aggregate in real-time through digital in-memory applications based on a modern ERP platform. With a recent survey showing that nearly one in five small and medium enterprises today are not using accounting software at all, many businesses are still a step behind in being able to apply more advanced technologies.

As new technology makes it easier for finance departments to adopt digital infrastructure that is connected, intelligent, responsive and predictive, they can also take advantage of machine learning to further improve business performance. Along with digital solutions for managing large amounts of data, machine learning becomes a natural next step to increase efficiency and improve business performance.

Use automation where it counts

In today’s workplace, finance professionals are spending just 17 percent of their time on strategic activities, with a lack of automation serving as the culprit for much of this inefficiency. As finance teams move away from managing work through spreadsheets and towards digital and cloud ERP solutions, machine learning can provide an additional edge in driving innovation through the finance function.

Digital finance solutions that incorporate machine learning capabilities can greatly expedite transactional tasks and bring teams closer to eliminating manual administrative work entirely. Financial professionals can increase the time spent on strategic priorities by automating back-office processes like procure-to-pay, order-to-cash and record-to-report. Machine learning programs utilize predictive algorithms to churn through massive amounts of data, working at a much faster speed than traditional processes dependent on human input. Turning non-strategic tasks over to computers will allow corporate finance professionals to spend time on more rewarding and higher-value work like business/deal support or advanced analytics, which will in turn yield increased business opportunities for the entire organisation.

 Supplement human processes with machine support

While not all financial processes can be completely automated, machine learning can help to support transactions and reduce error in tasks that require human input. For example, it is likely that financial statement auditing will never be completely trusted to machines, as it depends on human judgement and evaluating circumstantial reasoning behind data. However, machines can become useful in using patterns in data sets to highlight potential areas of discrepancy and double-checking human work. Machine learning technologies can sort through high volumes of data from financial reports at an exponentially faster pace, and then turn that data over to human eyes, which can subsequently investigate the story behind the numbers and evaluate whether certain patterns or anomalies may be cause for concern.

Another process that can allow finance teams to benefit from collaboration between humans and intelligent machines is fraud reduction and cybersecurity. As finance departments are embracing digital solutions for storing and managing financial data –either on premise or in the cloud –cybersecurity is becoming a top concern for CFOs who must ensure access to that information is monitored and regulated. Again, machines have the ability to churn through massive sets of data regarding access to and transactions upon those data sets, identifying abnormal patterns or unique access behaviors. Cybersecurity professionals in the enterprise can then analyse that data and determine the next steps that need to be taken to ensure the continual security of sensitive financial information, easing the worries of CFOs.

By embracing a robust digital strategy, allowing automation to take over administrative work and applying machine learning to supplement human processes, finance professionals can work at the speed of business today and ensure that their organisations are able to continuously innovate. As the capabilities of machine learning continues to grow, CFOs will need to ensure that their organisations are ready to unlock the full value of automation and machine learning through its many applications in accounting and finance.

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