TSYS (TSS), today announced an agreement with Featurespace, a global leader in adaptive behavioral analytics, that will reduce fraud for its clients with a revolutionary machine learning software platform — the ARICSM engine — that monitors every individual — one customer at a time — to deliver real-time decision capabilities.
“TSYS’ collaboration with Featurespace aligns with our overall strategy of integrating with advanced, innovative technology partners to help our clients grow their business, reduce costs, and deliver an exceptional customer experience,” said Andrew Mathieson, group executive, issuer product group, TSYS. “We will incorporate these capabilities across the credit risk lifecycle, enabling our issuers to catch more fraudulent transactions while dramatically reducing false-positive alerts for genuine transactions — a sharp contrast to the industry paradigm of blocking more valid transactions in order to detect actual fraudulent activity.”
The new agreement allows TSYS to strengthen its position in faster payments by leveraging machine learning to provide clients with actionable insights in real time, using adaptive behavioral analytics that result in operational efficiencies.
“TSYS has a long-standing leadership position in authorization processing and fraud management and we are excited to integrate our ARIC engine for TSYS’ clients,” said Martina King, chief executive officer, Featurespace. “We are proud to be working with TSYS to deliver world-leading machine learning fraud protection and exceptional customer management to their clients.”
The collaboration with Featurespace is yet another example of TSYS’ partner-centric approach with new, innovative technology partners, particularly in the fraud- and risk-management space. Earlier this year, TSYS announced the TSYS Transaction Recovery NetworkSM, powered by Ethoca, to enable card issuers and e-commerce merchants to stop online fraud, recover lost revenue, and eliminate chargebacks by providing merchants with immediate notification of confirmed fraud.