Visa has announced that its newly-established scam disruption practice successfully prevented over $350 million in attempted fraud in 2024.
The department, which operates under Visa Payment Ecosystem Risk and Control (Perc), comprises a diverse team of professionals including engineers, artificial intelligence developers, and former law enforcement and military personnel, all working alongside data visualization experts.
“Visa has invested more than $12 billion in technology over the past five years, focusing on reducing fraud and enhancing network security,” stated Paul Fabara, chief risk and client services officer at Visa. “At the same time, we recognize that our most effective tool against scammers is our people. By merging our cutting-edge technology with the unique insights and experiences of our talent, we are better equipped to identify and combat even the most sophisticated scams.”
The investigators utilize Generative AI tools that facilitate correlation and graphing analyses, enabling them to uncover complex relationships and sift through vast amounts of data to dismantle scam operations.
In one of the largest scam networks uncovered to date, Visa detected fraud patterns linked to ‘identity verification’ merchants. Scammers used phishing links sent through dating websites disguised as legitimate identity verification platforms, leading victims into recurring billing cycles. By correlating transactions with IP data and applying advanced analytical techniques, Visa mapped out a network of merchants sharing similar scam characteristics, ultimately shutting down nearly 12,000 sites and preventing losses exceeding $37 million.
“Fraud often remains faceless, but scams are personal,” remarked Michael Jabbara, SVP and global head of Perc at Visa. “These scams have direct and often devastating consequences for victims. Visa also collaborates with intelligence partners, law enforcement, and industry working groups to ensure that not only do we dismantle these scams, but that other players in the ecosystem are equipped to recognize warning signs on their own.”