By Olov Renberg
It’s very clear to me that fraud prevention needs a serious update. For someone like myself, who travels extensively to speak at conferences, meet customers, or getting some well needed vacation, getting intrusive calls from my credit card provider asking questions about my weird travel patterns is among my least favorite things. It is very clear to me that machine learning could assist with fraud detection in a bigger way. I’m still to this day surprised to experience fraud alerts, sometimes even shutting off my credit cards, which then results in long phones calls with customer service despite them having extensive knowledge about my consistent, albeit weird, travel patterns. When studying my payment patterns leading up to the failed transaction, I am often lead to the conclusion that its often a rule based error or trigger that disrupted by transaction.
When introducing unsupervised machine learning in parallel to supervised machine learning it’s important to constantly evaluate both scores and design an overall fraud system which allows both types of fraud detection to weigh into final decisions. In the early beginnings, even supervised machine learning may be perceived as successful within the organization without accounting for potential revenue losses due to false positives and false negatives. Since they depend on human intelligence and past observations, they cannot foresee future fraud attacks, since real total cost of fraud is often a grey number. To truly reduce this grey area of fraud you need unsupervised machine learning which can sift through this data and increase your revenue from good customers while also reducing fraud. It also provides future proofing for new unknown fraud behaviors yet to be discovered.