5 Reasons Why Human Auditors Can’t Compete with Robo-Auditors

Artificial intelligence is slowly taking over roles once reserved for humans. AI can now diagnose pneumonia more accurately than radiologists. In 20 years, driverless cars will transport people faster and safer than humans ever could.

It’s not just that AI does jobs that humans don’t want to do, like take your order at McDonald’s. It’s that these systems do a better job than humans ever could.

In the business world, we’re starting to see this trend as companies begin adopting finance automation systems. As AI moves to corporate finance in the back office, the role of corporate finance teams, including auditors, will shift from data crunching to decision making, which is an area where human judgment still reigns supreme.

In this article, we’ll look at five reasons why even the best, most experienced human auditors must yield analytics-driven auditing of companies’ financial data to robo-auditors and robotic process automation systems like Oversight Insights On Demand^®.

Reason #1: Robo-auditors have a better memory than humans.

Quick question: what did you have for breakfast on Tuesday three weeks ago?

If you’re able to remember, it probably took you some time to search your memory for the right answer. The processing power of AI enables immediate recall of a mountain of digital data bigger than anything human beings could keep in their brains.

Let’s look at an example from the corporate world. Most companies have a threshold for when employees must produce travel receipts, usually around $25.

Can a human auditor remember that an unusually large percentage of meals and taxi rides that one employee had over the past six months was barely under $25?

No. But artificial intelligence can.

A robot can spot that this employee submits $24 expenses at three times the rate of other account executives and flag the pattern for review.

Reason #2: Robo-auditors don’t fall victim to positive confirmation bias.

While human beings often suffer from positive confirmation bias, robots don’t.

Confirmation bias skews how auditors see results. After examining multiple transactions and seeing that the vast majority are correct, the auditor may begin to assume that every transaction will be correct. It’s not their fault – when the human brain sees many things that are correct, it’s difficult to find things that are not okay.

This time-saving mechanism is just part of how our brains are wired. On the other hand, a computer with sophisticated AI algorithms doesn’t have the same burden.

For most companies, only a few transactions are problematic and finding them is like searching for needles in a haystack. What’s worse, it usually costs more to manually locate the needles than they make from correcting the problems they find.

But now, technology can look through all that hay and come back with a stack of needles for the human auditors to examine. The humans won’t suffer from confirmation bias because AI has done the heavy lifting and brought them less to look at. A system like this provides a much better return on investment for the company.

Reason #3: Robo-auditors can search for anomalies around the clock.

Fraudulent charges and erroneous transactions can cost companies millions, yet the process by which human auditors must find them is tedious and ineffective.

When human auditors try to identify things that are not working, they typically use a manual sample-based approach and only do so every six months or once a year. Not only is this method infrequent, but you have confirmation bias to worry about.

Robo-auditors never get tired and have no confirmation bias, which means they’re capable of running this type of analysis 24/7 without missing a thing.

When you see a problem happening under certain conditions or circumstances, you can put something in place to avoid it in the future. It’s always better (and more cost effective) to prevent a mistake than to correct it after it’s occurred.

Reason #4: Robo-auditors are always getting smarter and improving.

Robo-auditing, with its combination of automation and artificial intelligence, gets smarter about spotting anomalies. Robo-auditors continually expand their knowledge base, and this makes them more efficient at spotlighting these abnormalities.

As you continue to use a robo-auditor, your financial processes get better, but just like quality in manufacturing, there’s always room to improve. Your pile of needles may get smaller and smaller, but AI systems get smarter and more efficient in finding them.

No company ever has perfect processes because business always changes: the transactions running through your system change, your employees change, and the business environment changes. No company exists in a static environment.

When facing new challenges, you want a system that is nimble, quick, and has been improving with every anomaly it detects and every new rule that gets written.

Reason #5: Robo-auditors can help foster positive behavioral changes.

The robo-auditor can find the problematic transactions, show you which actors need to have their behavior influenced, and help you drive meaningful process improvement.

Here’s one example: a Fortune 200 utility company had a problem with accounts payable and used recovery auditors to claw back erroneous payments.

That decision was a costly one. The utility lost the timely use of the money it mistakenly paid and then handed over 20% of what was recovered to the auditors.

When the utility employed Oversight Insights On Demand for Procure-to-Pay, it discovered how many of those erroneous payments were made in the first place and improved the process.

That process improvement saved the utility $1.5 million in cash, plus the $300,000 that would have gone to recovery auditors. In fact, the robot-auditor buttoned up the process so well that the recovery auditors eventually quit the account.

Why, you ask? The utility company had no erroneous payments to reclaim anymore.

This is the first blog in an ongoing series based on the recently released book, Robo Auditing: Using Artificial Intelligence to Optimize Corporate Finance Processes by Patrick J.D. Taylor, Manish Singh and Nathanael L’Heureux.