Security Management: New Solutions to Old Problems
T-Mobile get immediate ROI and improved loss prevention capabilities with 3VR
T-Mobile USA, Inc., a national provider of wireless voice, messaging, and data services, operates about 2,000 company-owned stores across the United States and Puerto Rico. According to Joe Davis, CPP, senior manager of loss prevention for the company’s South Region, “Loss prevention is a new organization inside T-Mobile—just over two years old.” The first step, he says, was “to evaluate what systems were in place to reduce loss and fraud.”
He says the team found that “We had video systems in place that were adequate for physical security—watching the front and back doors—but we didn’t have a robust capability for reviewing forensic video and tying people to transactions inside a business.”
For example, an individual might come into a store and conduct a transaction at the register, claiming to be someone they were not. “Shortly, we get notification from the real person, who says, ‘Hey, I just got a bill from T-Mobile for five new phones and $5,000!’” says Davis. At that point, “We’d have to find the overhead video of the register and try to identify the person who came in and conducted the transaction.”
It was a labor-intensive investigative process, and given the volume of incidents, resources weren’t always available to thoroughly research each reported case, Davis says. Then in 2008, the loss prevention team came across a technology that seemed to offer a way to get the job done cost-effectively. It was the 3VR SmartRe corder P-Series, by 3VR Security, Inc., of San Francisco, a hybrid digital video recorder/network video recorder (DVR/NVR) that had software with embedded analytics and biometrics, such as facial recognition, which could intelligently search surveillance video. It could also integrate with POS, intrusion, access control, and other systems.
T-Mobile decided to pilot the system from the autumn of 2008 until early this summer. The system captures the faces of people coming into a store, and applies metadata—data about the context of the image—and it then catalogs each one. The images can also be tied to POS transactions, if desired, in a searchable database that resides on the company’s network so that in the future, if there were an incident of identification fraud, for example, Davis could watch the video and review each face captured in association with that incident. “Then I can search forensically across my network in all locations where I have the system deployed,” explains Davis.