Academic journal article ABA Banking Journal

How to Pinpoint a Data Breach More Efficiently

Academic journal article ABA Banking Journal

How to Pinpoint a Data Breach More Efficiently

Article excerpt

Fraud costs banks $1.9 billion a year, but a three-year-old Chicago-based company named Rippleshot--recently endorsed by ABA for card breach detection--is helping banks take a major bite out of that by using big data, machine learning and predictive analytics to detect card fraud weeks or even months before traditional methods sound the alarm.

Using an algorithm that compares and combines merchant and card issuer data on hundreds of millions of card transactions and fraudulent activity reports, Rippleshot's Sonar product finds common points of compromise--the places cardholders shopped at before their cards were suddenly used fraudulently somewhere else. This reveals which stores--even which point-of-sale terminals--might be compromised, as well as which cards were used there during the suspected breach and which cards are most likely to report fraudulent use soon.

The approach is dramatically different from how many banks currently get a heads up about a breach, Rippleshot CEO Canh Tran said.

Today, "it's pretty much sort of like a little telephone chain," he explained. "Word gets passed around. People make calls to colleagues at other banks and pretty soon somebody sends an anonymous post to [cybersecurity reporter Brian] Krebs and he hits publish. Then they're calling the processors and saying 'Hey, are you seeing anything here? We're seeing a lot of customers having debit or credit card fraud. We can't pinpoint it.' The processor will say something like 'I'm working on that. We'll get back to you.' It's a very manual, reactive type of process. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.