The presentation discusses a proactive approach to credit-card fraud prevention using data intelligence and dark web intelligence. The approach involves tracking breached payment card data and using traditional intelligence methods to mitigate the damage caused by the breach. The presentation also discusses a classic big data problem of correlating transaction histories to find the point of compromise and how to deal with uncertainty in the process.
- Data intelligence can be used to prevent credit-card fraud proactively
- Tracking breached payment card data and using traditional intelligence methods can mitigate the damage caused by the breach
- Dark web intelligence can be used to shift from a reactive to a proactive approach
- Correlating transaction histories to find the point of compromise is a classic big data problem
- Uncertainty can be dealt with by using a control group and setting different thresholds to get rid of irrelevant data
The presentation provides a case study of using transaction data and dark web intelligence to find a large breach. The point of compromise was spread across ten different batches on card shops, and the business was likely Canadian. The probability distribution for the merchants showed a spike with a probability of 0.04%, which was 60 times larger than random chance and twice as large as the second-largest group. However, secondary evidence was needed to confirm the point of compromise, which was a restaurant chain called Bob's.
Right now, combatting credit card fraud is mostly a reactionary process. Issuers wait until transactions occur that either appear fraudulent according to rules-based analytic engines or are reported by customers, and only then, do they intervene to prevent further fraud. But by then, it's often too late - losses through merchandise theft, investigation cost, reissuance, etc., have already occurred, and those losses have piled up into over $10B of stolen funds each year being pumped into the online criminal ecosystem.There is a better way. By using intelligence gathered from online sources such as the dark web combined with transactional data, we demonstrate predictive analytics that can not only identify who the next fraud victims will be, but also where card data is being stolen from, all before any fraudulent transactions have occurred.Payment card fraud is the slush fund that underlies most global criminal threats, from organized crime to political meddling, in large part because of antiquated, reactive techniques and a dearth of innovative techniques to more proactively combat it. Our approach represents a paradigm shift in fighting payment card fraud; by using dark web market intelligence combined with transaction data to predict both fraudulent charges and points of compromise, we can intervene before any loss occurs, stopping payment card fraud dead in its tracks and eliminating a major source of funding for the global criminal ecosystem.