Rippleshot, a provider of proactive fraud detection and prevention solutions to payment processors and financial institutions, announced today a partnership with Flashpoint, the globally trusted leader in actionable intelligence, to pair Rippleshot’s compromised and high-risk merchant data and insights with Flashpoint’s Payment and Credit Card Fraud Mitigation solution.
Equipped with tools such as AI/ML, automation, and data-driven strategies, Rippleshot’s cloud-based solution leverages a data consortium of more than 4,500 financial institutions and is updated daily. Using Rippleshot’s detailed data and solutions, financial institutions can enhance their existing fraud prevention strategies to effectively detect compromised cards, data breaches and high-risk merchants, while also saving time and resources.
Flashpoint’s Card Fraud Mitigation solution helps fraud teams detect compromised credit cards from illicit communities and data breaches and identify high-risk merchants before fraudulent transactions occur or multiply. Rippleshot extends Flashpoint’s capabilities to identify stolen cards and the individual merchants that have been compromised. This provides financial institutions and card issuers with the ability to detect card fraud more proactively, identify likely card fraud victims and risky merchants, and drive fraud loss avoidance.
“Flashpoint is a market leader in delivering intelligence that provides a detailed view into what cyber criminals in illicit communities are seeing,” said Canh Tran, Co-founder and CEO, Rippleshot. “By pairing that with Rippleshot’s compromised and high-risk merchant data, this partnership will equip the industry with unparalleled financial intelligence to react much more quickly to instances of verified card fraud and proactively stop further damage from fraudsters.”
“Common Point of Purchase (CPP) analysis is a notoriously complex process for fraud teams,” says Matt Howell, SVP of Product at Flashpoint. “The integration of Rippleshot’s high-risk merchant data into our platform increases the speed and scale of identifying potential fraud across CPP. This means teams have the analysis, across multiple financial institutions, completed for them so they can focus on preventing fraud from occurring.”
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