Robots are NOT going to replace all human accountants or bookkeepers (at least not anytime soon).
White-collar workers who are part of the knowledge economy are beginning to experience what manual laborers have in the past when new technology made their jobs obsolete. Given the improvements we have recently seen in computing, many professionals fear for their future as machines threaten to overtake them.
Rather than fear changes that machine learning will have on accounting tasks, it's an opportunity for accounting professionals to be excited. The profession is going to become more interesting as repetitive tasks shift to machines. There will be changes, but those changes won't completely eliminate the need for human accountants, they will just alter their contributions.
Let's take a look at how machine learning will change accounting.
What is machine learning?
Machine learning is the leading edge of artificial intelligence (AI). It's a subset of AI where machines can learn by using algorithms to interpret data from the world around us to predict outcomes and learn from successes and failures. As machines infiltrate accounting tasks to take over the more mundane and repetitive tasks, it will free up accountants and bookkeepers to spend more time using their professional knowledge to analyze and interpret the data to provide recommendations for their clients.
Machine learning will propel innovation in accounting
When accounting software companies eliminated desktop support in favor of cloud-based services, accounting firms were forced to adapt to life in the cloud. Similarly, 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.
Accounting tasks that machines can learn to do
Rather than eliminate the human workforce in accounting firms, the humans will have new colleagues-machines-who will pair with them to provide more efficient and effective services to clients. Currently, there is no machine replacement for the emotional intelligence requirements of accounting work, but machines can learn to perform redundant, repeatable and oftentimes extremely time-consuming tasks. Here are some of the possibilities:
Auditing of expense submissions: Machines could learn a company's expense policy, read receipts and audit expense claims to ensure compliance and only identify and forward questionable claims to humans for approval. Otherwise, machines could handle the bulk of this task.
Clear invoice payments: Today, when customers submit payment that might combine multiple invoices or that don't match any invoices in the accounting system, it's time-consuming for accounts receivable staff to apply payment correctly without making a call to the client or trying to determine the right combination of invoices. However, smart machines could analyze the possible invoices and can match the paid amount to the right combination of invoices, clear out short payments or automatically generate an invoice to reflect the short payment without any human intervention.
Risk assessment: Machine learning could facilitate risk assessment mapping by pulling data from every project a company had ever completed to compare it to a proposed project. This very comprehensive assessment would be impossible for humans to do on this scale and under a similar timeline.
Analytics calculation: The accounting department is continuously barraged with questions similar to, "What was our revenue for this product in third quarter last year?" or "How has this division grown over the last 10 years?" Given the data, machines can learn to answer these questions very quickly.
Siri-type interface for business finance: Pegg, an app that works with the messaging app, Slack, is already showing what's possible in terms of creating invoices, responding to questions about revenue projections and status of expense accounts. This app as well as other conversational interfaces have huge potential to disrupt accounting and make some tasks as simple as chatting.
Automated invoice categorization: Accounting software firm Xero is deploying a machine learning automation system that will be able to learn over time how to categorize invoices, something that currently requires accountants to do manually.
Bank reconciliation: Machines can learn how to completely automate bank reconciliations.
As accounting firms and departments begin to rely more heavily on machines to do the heavy lifting of calculating, reconciliations and responding to inquiries from other team members and clients about balances and verifying info, accountants and bookkeepers will be able to deliver more value to their clients and handle more clients than ever before.
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 tarks that can be automated and more on those inherently human aspects of their jobs.