Anura PR Team
Machine learning and artificial intelligence definitely have their perks. In fact, there is even an A.I. that reads privacy policies for you. But becoming too reliant on technology can backfire. There are some nuances that machines can’t pick up on, at least not yet, anyway. And that means you could be missing important clues that fraud is afoot.
When you want it done right, sometimes you need to roll up your sleeves and do it yourself. Here are three ways a human still outperforms the machine when detecting fraud.
1. Understanding Daily Trends
The ability to interpret daily trends is crucial for spotting unusual activity. It requires the power of recognition, finding patterns that occur day in and day out.
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Sure, a machine can be programmed to recognize basic patterns and trends, but does it possess emotional intelligence like a human does? No, unless you’re fictional characters named WALL-E and EVE.
Let’s say a fraudster is currently flying under the radar. Their ego gets the best of them and they make a bold move, but suddenly get cold feet and back off. That short blip in the pattern will likely be overlooked by the machine, which tends to focus on the big discrepancies. But with a human their “spidey senses” go off. They know there’s something to that “blip” and make it a priority to investigate.
2. Prioritizing Small Imperfections
To catch all fraud, the focus needs to not only be on the large anomalies but on the small imperfections, too. Fraudsters like to stay under the radar, not unlike the protagonists in the movie Office Space. Here, the underdogs didn’t steal a huge chunk of money, instead they took pennies at a time, which quickly added up.
In the ad fraud world, fraudsters can make bank with just five to 10 clicks a day and not even raise a red flag in the eyes of a machine. Why? Because machines prioritize the big discrepancies over the small ones. When it comes down to man versus machine, humans are the most adept at catching small anomalies.
3. Diving Into Data (From a Human Perspective)
The ability to deep dive into data is considered tacit knowledge. Tacit knowledge is learned by doing, not by studying or following rules. At this time, tacit knowledge isn’t something that A.I. or machine learning can do.
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Sure, algorithms can demonstrate “human-like tacit knowledge” but it isn’t the same level of expertise. Judgment still needs to be handled by humans. Even when using a machine, rules and reputation lists still have to be updated by a human.
Remember Machine Learning Is a Tool, Not a Replacement
Don’t let the WALL-E effect happen. If you’re not careful, machine learning and A.I. will make you lazy. Technology can be disruptive if you let it. Instead use it to complement, not replace. We are tool users, and the systems are tools. Machine learning as a tool, can amplify the power of our brains. Conversely, machine learning needs our experience and knowledge to learn.
Remember, machine learning and A.I. won’t replace data analysts anytime soon, as long as we don’t let them.
