With almost 3 millions consumers complaints in 2017 in the US, it is now a pretty common scenario in which an ill-intentioned person gets a hold of a credit card information and proceeds to empty the account it is attached to. For fraud analysts, it is essential to reduce the detection time of these situations, which can lead to serious financial losses for the organizations.
As often, to fight back against criminals, it is important to understand how they operate.
Personal data breaches occur more and more frequently, meaning that the opportunities for hackers to get their hands on credit cards data are numerous. The fraudster might have bought the stolen card numbers from a dubious website, or simply got the card details through the use of an ATM or gas pump skimmer.
Early October 2018, the Sheriff’s Office of a Californian town found five credit card skimmers at two gas stations. They identified more than a dozen cases of credit card theft, accounting for 20,000$. Thieves had created fake credit cards encoded with customer stolen information. They had used them to buy multiple, low value, gift cards at different supermarket knowing that banks typically do not get suspicious over low-value transactions at grocery stores.
What led the police to unveil the fraud was that for all the stolen credit cards cases they investigated, there was something in common. At a point in time, the victims had used their credit card to fill their car tank at the gas stations. From there, the police was able to identify the criminals and arrest them.
Perhaps when reading this story, you begin to understand what stolen credit cards and graphs have in common. In the rest of this article, we put ourselves in the shoes of a credit card company seeking to detect fraud.