AUSTIN, Texas--Q2 Holdings, a provider of secure, cloud-based digital banking solutions for community-focused financial institutions, announced the release of Q2 Patrol, an event-driven validation product designed to mitigate certain high-risk non-transactional fraudulent activity.
Q2 Patrol utilizes behavioral machine learning to identify potentially fraudulent digital banking sessions. It analyzes past login behavior and device details, including IP addresses, geolocation, device type, time stamps and more to create a digital footprint for each end user.
“Attempting to identify fraud using a single-layered security approach is becoming more difficult,” said Bob Michaud, chief security officer at Q2. “Q2 Patrol’s multi-layered, proactive security monitoring enables FIs to better understand their customers’ risk levels while empowering end users to take a bigger role in their account security without sacrificing convenience.”
The added layer of insight provided by Q2 Patrol delivers a better digital banking experience by:
- Requiring end users to further authenticate a digital banking session if that session is deemed suspect based on abnormal behavioral login and device detail.
- Providing regular reporting to FIs for regulatory compliance and risk reduction.
- Supplying session details in the user interface to better involve customers in their own account safety.
Many cases of illegal digital banking activity involve fraudulent sessions before a transaction even occurs. Q2 Patrol prevents fraudsters from executing those preliminary actions—such as changes to security preferences or addresses, updates to user profiles, authorization of external transfers and changes to alert settings—that serve as indicators for a potentially fraudulent transaction. Q2 Patrol then requires the end user to authenticate that action via a token or secure access code.
Q2 Patrol is a member of the Q2 Secure product suite and works in conjunction with but separately from Q2 Sentinel—formerly known as Q2 Risk and Fraud Analytics—which identifies fraudulent transactions in near-real time.