Sovereign Bank, a Fiserv client for more than two decades, demonstrated its commitment to protecting its customers from fraud by agreeing to be among the first to leverage the Predictive Modeling platform, part of Financial Crime Manager suite from Fiserv.
“Sovereign has had a long-standing relationship with Fiserv and we continue to work together to provide us with best-in-class risk and fraud management solutions,” says Jim Zardecki, senior vice president, director of Loss Prevention and Security, Sovereign Bank. “Fiserv understands our data structure and our operational environment, plus their suite of check fraud detection products have a proven track record.”
This is one example of Fiserv’s role in technologies that identify risk and minimize exposure to avoid losses, while providing a strategic view of risk across multiple business channels. Through its core competency of Risk and Compliance, Fiserv is delivering the tools and expertise banks and credit unions require to mitigate multiple types of risk to remain in compliance with the ever changing regulatory environment, while at the same time staying competitive.
Predictive Modeling, a platform from Fiserv, offers Sovereign Bank a progressive step toward enterprise fraud management. The platform has an infrastructure that is expansive to multiple payment types and data inputs, and uses advanced decisioning analytics, which can lead to a 30 to 40 percent reduction in false positive alerts, compared to current outputs. Sovereign’s fraud alerts will now be ranked in order of likelihood of fraud, so that users of the platform can easily manage incidents in priority order. The flexibility of Predictive Modeling will allow Sovereign to adjust to emerging fraud trends, policy changes or levels of staffing support.
“Predictive Modeling from Fiserv is an advanced platform that delivers real-time, industryleading fraud prevention,” says John Filby, president, risk management solutions, Fiserv. “Through the combination of rules and analytics we can identify more fraud and materially reduce false positives.”
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