Credit Card Fraud | Shared Personal Info | Lost Money |

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For more viral videos please check: https://www.youtube.com/channel/UCF2gx5285QWbQxLaYEXcJig/videos You're sitting at home minding your own business when you get a call from your credit card's fraud detection unit asking if you've just made a purchase at a department store in your city. It wasn't you who bought expensive electronics using your credit card — in fact, it's been in your pocket all afternoon. So how did the bank know to flag this single purchase as most likely fraudulent? Here's a typical scenario. When you go to a cashier to check out at the grocery store, you swipe your card. Transaction details such as time stamp, amount, merchant identifier and membership tenure go to the card issuer. These data are fed to the algorithm that's learned your purchasing patterns. Does this particular transaction fit your behavioral profile, consisting of many historic purchasing scenarios and data points? The algorithm knows right away if your card is being used at the restaurant you go to every Saturday morning — or at a gas station two time zones away at an odd time such as 3 a.m. It also checks if your transaction sequence is out of the ordinary. If the card is suddenly used for cash-advance services twice on the same day when the historic data show no such use, this behavior is going to up the fraud probability score. If the transaction's fraud score is above a certain threshold, often after a quick human review, the algorithm will communicate with the point-of-sale system and ask it to reject the transaction. Online purchases go through the same process. In this type of system, heavy human interventions are becoming a thing of the past. In fact, they could actually be in the way since the reaction time will be much longer if a human being is too heavily involved in the fraud-detection cycle. However, people can still play a role — either when validating a fraud or following up with a rejected transaction. When a card is being denied for multiple transactions, a person can call the cardholder before canceling the card permanently. Computer detectives, in the cloud The sheer number of financial transactions to process is overwhelming, truly, in the realm of big data. But machine learning thrives on mountains of data — more information actually increases the accuracy of the algorithm, helping to eliminate false positives. These can be triggered by suspicious transactions that are really legitimate (for instance, a card used at an unexpected location). Too many alerts are as bad as none at all. It takes a lot of computing power to churn through this volume of data. For instance, PayPal processes more than 1.1 petabytes of data for 169 million customer accounts at any given moment. This abundance of data — one petabyte, for instance, is more than 200,000 DVDs' worth — has a positive influence on the algorithms' machine learning, but can also be a burden on an organization's computing infrastructure. Fraud detection is an arms race between good guys and bad guys. At the moment, the good guys seem to be gaining ground, with emerging innovations in IT technologies such as chip and pin technologies, combined with encryption capabilities, machine learning, big data and, of course, cloud computing. Fraudsters will surely continue trying to outwit the good guys and challenge the limits of the fraud detection system. Drastic changes in the payment paradigms themselves are another hurdle. Your phone is now capable of storing credit card information and can be used to make payments wirelessly — introducing new vulnerabilities. Luckily, the current generation of fraud detection technology is largely neutral to the payment system technologies.