Machine learning, a subset of artificial intelligence where machines can predict outcomes by using algorithms to interpret data, won’t eliminate the need for humans in accounting work, according to a leading expert on business, technology and big data.Machine learning, a subset of artificial intelligence where machines can predict outcomes by using algorithms to interpret data, won’t eliminate the need for humans in accounting work, according to a leading expert on business, technology and big data.
However, utilizing machine learning will be necessary to remain competitive, said Bernard Marr, a best-selling business author recognized by LinkedIn as one of the world’s top 5 business influencers, in a recent article for Forbes.com. “Accounting departments and firms will be forced to adopt machine learning to remain competitive since machines can deliver real-time insights, enhance decision making and catapult efficiency,” he wrote.
Instead of being afraid of the changes that artificial intelligence and machine learning will have on accounting, accounting professionals should be excited, argued Marr, author of Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things and Key Performance Indicators for Dummies. When machines take over the more mundane and repetitive tasks, it frees up accountants and bookkeepers to spend more time tapping into their professional knowledge to analyze and interpret the data so they can provide clients with recommendations and more value, he wrote.
Machines may be able to learn to perform “redundant, repeatable and oftentimes extremely time-consuming tasks,” but there currently is no replacement for the emotional intelligence that accountants are required to utilize in their work, according to Marr.
Marr’s views echo those of other accounting and advisory thought leaders who recommend using technology solutions to handle routine tasks in order to free up time for helping business owners solve more complex, big-picture challenges. “One of the biggest ways that technology can help accountants is to create deeper, better relationships with clients,” said marketing and practice management consultant Charles Hylan in the eBook, Tech Roadmap: Selecting the Ideal Solutions to Thrive in Business Advising.
Here are five possible tasks that machines could take over for accountants, providing more capacity for offering advisory services, according to Marr:
Auditing expense claims: Once a machine “learns” the expense policy, it could read receipts and audit expense claims, approving clear-cut expenses and forwarding to a human for approval only those that are questionable.
Analytics calculations: Given the right data, machines can learn to answer common questions about growth over certain periods or product-specific revenue during a particular period. Sageworks’ solutions, for example, can show how a company’s profitability compares with peers in order to highlight potential improvements.
Risk assessment: Marr notes that machines could pull data from every completed project and compare it to proposed projects in order to provide a comprehensive assessment of risk tied to the proposed project. Another example of machine learning for risk assessment would be a solution that documents expected value calculations in an audit. Auditors are able to streamline preliminary analytics because outliers from expected values can be flagged for additional review.
Bank reconciliation: Machines can automate much of the process for matching transactions on bank statements to those in the general ledger.
Matching payments to invoices: Technology solutions can analyze invoices and possible combinations of invoices to match them with paid amounts or to generate invoices reflecting shortfalls in payments.
Instead of feeling threatened by machines and artificial intelligence, accountants who embrace the new technology for the heavy lifting of repetitive, routine tasks can embrace their ability to deliver more value and handle more clients, Marr argued.
“It is high time for every accountant to reflect on their job, identify the opportunities machine learning could offer to them, and focus less on the tasks that can be automated and more on those inherently human aspects of their jobs,” he wrote.
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