We’ve been talking a lot about AI in the workplace recently. From part 1 discussing the core definitions of AI in the workplace to part two discussing why an AI implementation project won’t take most accounting jobs, today, we would like to discuss what jobs or tasks it will take over.

According to the Business Software Education Center, AI will have specific responsibilities—taking over tasks that are repetitive, error-prone, and task-based. However, as mentioned in our last blog, those who understand how to deliver context, vision, and understanding will be able to keep their job even when AI becomes an accounting mainstay.

Today, we’d like to discuss what tasks you may see automated in the next few years. Knowing this, if any of your staff is explicitly working on any or all of these, you may want to recommend they stay tuned for part four—the skills needed to succeed.

Different Uses of AI in Accounting

According to the Accounting Principals blog, several large accounting firms and organizations made early investments in artificial intelligence and now use AI to reduce the amount of time their accountants spend on complex audits and other accounting tasks. Here are five places they believe AI will be used in the next couple years:

Transaction Categorization

The transactions that need to be entered into an organization’s accounting software may be spread across PDFs, spreadsheets, invoices, receipts, and bank statements. AI can extract information from these disparate sources of information and categorize it in the accounting software, something that accountants have traditionally had to do manually.

For example, during a sale of a business, companies will need to analyze thousands of documents. PICPA notes that many Big Four firms are embracing Natural Language Processing for AI and many small firms are as well:

  • Deloitte is using natural language processing to review hundreds of thousands of legal documents to identify change control provisions as part of a client’s sale of a business unit.
  • EY is using natural language processing to review leases to ensure that they comply with the new lease accounting standards.

Expense Report Review and Approval

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.

An RPA solution can be used to automatically read invoices, import specific data from invoices into the enterprise resource planning (ERP) system, perform a three-way match, route the invoice to the appropriate personnel for approval, and submit the payment to the vendor. In addition, an RPA solution can identify exceptions and notify the appropriate personnel for manual review.

Matching 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.

When customers submit payments that don’t match invoices in the accounting system, artificial intelligence can analyze invoices and match the paid amount to the appropriate invoices.

It can also clear out short payments or automatically generate a new invoice to reflect the short payment. This frees up the accounts receivable staff from the time-consuming tasks of calling the client for clarification or trying to determine the right combination of invoices to apply the payment.

Fraud Reduction

Artificial intelligence can analyze and digest large volumes of data fast to detect outliers and anomalies. The technology can learn acceptable patters and recognize potentially fraudulent activity in purchasing and accounts payable and forward issues to the appropriate level of management for follow up or review. The technology can detect suspicious behavior long before a human would and significantly reduce the potential hit to the company’s bottom line.

According to the Pennsylvania Institute of CPAs, EY is using machine learning to detect anomalies and fraudulent invoices. It indicates that the technology is 97 percent accurate at identifying faulty invoices, and has enabled the organization to minimize its risk exposure when it comes to violating sanctions, anti-bribery regulations, and other aspects of the Foreign Corrupt Practices Act.

AI Analytics Calculations

Accounting personnel are constantly tasked with answering questions like, “What was our revenue for this service line in the second quarter of last year?” or “How has this product line grown over the last ten years?” With the right data, AI can answer these questions very quickly.

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.

Keeping Up with AI in the Accounting Department

AI and machine learning will enable Controllers and their teams to spend less time on data preparation and analysis and more time on interpreting results and developing insights. With AI expected to become a major part of all organizations in the next five years, it pays to embrace it and work to reduce your exposure should it come into the business. Stay tuned for part four of our article on the steps you should take to protect your job when the robots do come.