Simply put, card fraud shouldn’t be something you have to shoulder alone. It’s complex, labor intensive and ever-changing – and it’s certainly harder when you’re on your own. We’re in this fight together, so we’ve made a collaborative tool to actually fight fraud together – with data.
In our over 40 years of combined experience identifying and mitigating card fraud, we’ve seen the problem grow from a transaction-level headache to a weekly news headline. In 2013, we came together to change the fight against fraud by creating Rippleshot. We take a big-data machine learning approach that is familiar to search, genetics and advertising and apply it in a novel way for the payment processing industry, helping banks, merchants and processors to proactively monitor suspicious fraudulent activity and implement smarter fraud risk management strategies when card compromises do occur.
Our team is committed to our mission of making people feel secure about the financial information they’re using, storing or transacting with. We’re actively involved in the fraud community, as members of the International Association for Financial Crime Investigators (IAFCI) as well as the Federal Reserve’s Secure Payments Task Force.
Conventional Card Fraud Mitigation
Transactional Fraud Detection Systems – Fraud systems that look for changes in consumer behavior work well at minimizing losses to individual accounts. Our team has over a decade of experience in these systems. At best, they only warn you of fraud at the time of the spend and do not predict a card will soon go fraudulent. They fare poorly against wide-scale card compromises seen in data breaches, and freezing cards detected this way leads consumers to spend with competitors’ cards, a concern vitally important to any card issuer.
EMV (Chip Cards) – When a microchip card is used in an EMV POS terminal, the chip generates a unique code that the issuing bank validates each time, making it more difficult for fraudsters to create counterfeit cards for brick-and-mortar fraud, since they won’t have the chip to generate the correct code . This approach has decreased counterfeit card fraud losses at brick-and-mortar stores throughout Europe, though in the case of the large-scale compromise that took place at Target in 2013, chip cards alone would not have prevented the data breach. In any case, as has been seen in Europe and Canada, among others, fraud will largely just shift channels to online/card not present.
How Rippleshot Stops Fraud
Rippleshot looks for the original cause of cards going fraudulent, often the result of a data breach. Instead of looking at consumer behavior, Rippleshot looks at the merchant. When consumers visit a compromised merchant and their card information gets stolen, fraudulent spending takes place over weeks to months, sometimes concurrent with the consumer spending. Because many counterfeited cards have a shared history of shopping at the same breached merchant, we stop the fraudulent use of the other compromised cards before it happens.
All merchants transact with cards that will eventually be used fraudulently. Merchants with greater volumes of these cards are likely to be the source of a data breach. Rippleshot uses a novel, big-data approach to sift through hundreds of millions of credit card transactions to spot common shopping history in fraudulent cards. This approach specifically identifies the source of fraud to the chain, stores, particular days, or even down to POS terminals. Rippleshot uses cutting edge statistics and machine learning to distinguish between real data breaches and nearby stores that share the same patrons, resulting in accurate assessments with dramatically fewer false positives.
Co-Founder & CEO
Co-Founder & CTO
Co-Founder & Chief Scientist
Director of Business Development
Director of Product Management
We’re always on the lookout for smart, driven and creative people to join our team. Rippleshot employees enjoy the benefit of working in one of Chicago’s most successful incubators, Catapult, on an innovative product that can help reduce the overall impact of card compromises.