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 digitization. Here are three ways finance professionals can embed machine learning to boost the maturity of their financial management systems or extend their accounting and reporting capabilities to better meet today’s business challenges.
Embrace digitization 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 live business and Big Data being integrated across the organization – which is possible to aggregate in real time only through digital in-memory applications based on a modern ERP platform. With a recent survey showing that nearly one in five small and midsize 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 organization.
Supplement human processes with machine support
While not all financial processes can be completely automated, machine learning can help to support transactions and reduce errors 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 be useful in using patterns in data sets to highlight potential areas of discrepancy and double-check human work. Machine-learning technologies can sort through high volumes of data from financial reports at an exponentially faster pace than humans, 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 that 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 analyze 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 organizations are able to continuously innovate. As the capabilities of machine learning continue to grow, CFOs will need to ensure that their organizations are ready to unlock the full value of automation and machine learning through its many applications in accounting and finance.
This article originally appeared in PPN, and is republished by permission.Comments