When you mention robots, most people think of Star Wars or those car-manufacturing assembly lines where giant, agile machines move heavy parts, make spot welds or secure bolt-on chassis. Robots have developed to the point where robotic process automation (RPA) is now coming to the world of corporate finance.
In finance, we’ve automated by implementing general ledger and enterprise resource planning (ERP) systems, as well as spreadsheet programs like Microsoft Excel. Yet many corporate financial processes are still stuck in the 20th century. Corporate finance teams spend about 80%of their time manually gathering, verifying and consolidating data, leaving only about 20% for higher-level tasks like analysis and decision making.
RPA will unleash a new wave of digital transformation in corporate finance. Instead of programming software to perform certain tasks automatically, RPA uses artificial intelligence (AI) to train and teach software robots to process transactions, monitor compliance and audit processes automatically. Machine learning, a form of AI, takes finance automation even further — by building systems that have the ability to automatically learn and improve based upon new data and experience — without being specifically programmed to do so.
According to a recent survey by Grant Thornton (paywall), corporate finance executives are embracing these new forms of finance automation, ranking AI and advanced analytics among their highest-priority planned investments, while many have already begun their digital transformations.
In the area of transaction processing, CFOs can use forms of RPA to optimize their accounts-payable and accounts-receivable processes. How a corporation collects from customers influences both cash flow and customer satisfaction. A CFO’s company may already do that well, but AI augments such expertise so that the company can collect as expeditiously as possible while maintaining good customer relations.
At the same time, a customer may make a duplicate payment, combine multiple invoices into one payment or pay the wrong amount. Manually correcting the errors in the general ledger system can take an enormous amount of time to sift through invoices, find the source of the errors or track down the customer to get the problem resolved. AI and machine learning could support finance teams by automatically tracking down the relevant information, finding the source of a GL problem and suggesting which payments to match to which invoices.
AI can also mitigate financial risks by stopping erroneous payments to vendors in near real time. For example, enterprises with $500 million in annual payments could be leaking anywhere between $500,000 and $2.5 million in cash from errors, fraud and misuse. Today, large organizations rely on manual sample audits of invoices and payments to mitigate risks. However, these sample audits encompass only a small fraction of transactions, thus finding little in the way of errors or waste in the process. However, AI can expand the scope of compliance monitoring and analysis, saving millions and preventing duplicate and fraudulent payments while stemming the tide of cash leakage.
AI spots microtrends and uncovers problems that humans often overlook. It augments what humans do and enhances their effectiveness, speed and efficiency. It’s similar to the revolution we’ve seen in manufacturing robots because the combination of automation and artificial intelligence in accounting is faster and more reliable than humans alone.
However, RPA doesn’t just automate operations — it allows you to redesign and improve processes and even help educate humans about better ways to work more efficiently and make spending decisions.
For example, say you have an account executive who travels to New York City. It’s December — the most expensive time of the year to visit New York — and your executive spends $500 a night for her hotel room. That’s what you’d expect to pay for a hotel around Christmas. The AI-based system wouldn’t flag that trip as an anomaly.
But say your AE travels to New York a few months later, when hotel rates are typically lower, and again pays $500 a night. Armed with data from millions of expense reports, an AI system would know that this rate is extravagant for a trip to New York in March, flagging the transaction and informing her that the going rate for a quality hotel in Manhattan should be lower. The result is that in the future, the executive makes a better decision about where to stay and how much to spend. In this way, artificial intelligence steps in with the guidance an account executive needs to do her job faster and more efficiently, reducing risks and saving her company money.
In this way, AI can help teach employees to see patterns they’re not aware of and to understand the implications of their behaviors and transactions, so they make better financial decisions in the future.
Helping CFOs Get The Job Done
Company executives, too, can use AI to identify opportunities to improve company policies and procedures and to make better financial decisions. Forms of AI such as voice-enabled digital assistants like Siri and Alexa are now widely used by consumers. These devices are likely to find their way into corporate boardrooms and executive suites — or even cubicles — in the future. For example, CFOs could use digital assistants to access data analytics systems and instantly answer questions on the fly to drive strategy and decision making in a corporate board meeting and beyond. Just as software robots get smarter over time, AI technologies could also end up helping humans make more informed decisions and even get smarter, too.
AI isn’t a moon shot for your company. Thanks to increased computing power and improved machine learning algorithms, AI is becoming a reality for corporate finance teams, moving beyond “Do I need to take this risk?” to the point where the greatest risk is in not adopting AI — because your competitors already have or soon will.
This article was originally published on Forbes