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Breaking News in the Industry: March 7, 2019

Bicycling Bandit Busted

You could call him the Bicycling Bandit. Police arrested a Michigan man early yesterday morning, saying they believe he is a serial burglar who used a bicycle to travel to and from at least 14 businesses where break-ins have occurred since Christmas Eve.  The 52-year-old man is in custody and expected to face numerous charges today, according to Warren Police Commissioner William Dwyer.

“He is a career criminal and all of these break-ins occurred within a bicycle ride from his home,” said Dwyer. “And that is how he was getting to and from the break-ins, on his bike. He doesn’t own a car.” In the most recent incident, around 12:30 a.m. Wednesday, police received a call from the owner of the Mobil gas station on Ryan Road, Dwyer said. “The station was closed but the owner was remotely monitoring a security camera and believed the suspect was staking out his business,” the commissioner said.   [Source: The Detroit News]

Cookie Monster swipes $600 from Girl Scouts

Seattle police are asking for help finding a man who they say robbed a Girl Scout troop in North Seattle Sunday night. Police released a photo of the man, who they say stole around $600, from the troop outside a QFC grocery store in the 11000 block of Roosevelt Way, near Northgate. {The image is hosted on the source website.)

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Jennifer Johanson said she and her daughter’s troop were wrapping up two-hours of sales when a man who had been lingering nearby rushed toward them. “He just started running and he pushed past the girls and he grabbed the money and he took off and he got into a car that was waiting for him,” Johanson said Tuesday.

Johanson said she was stunned when the man grabbed their cash envelope, another mom with her ran after the man but couldn’t catch him. “Little, freezing cold girls, just hocking cookies? Mean, low, the lowest of the low,” she said. Police ask you contact the north precinct detectives at 206.684.5735 if you recognize the man.   [Source: KOMO News]

Amazon plans to build you a robot to fetch packages for you

Amazon has been issued a patent for a robot that would stay at an individual’s home, apartment or office building and retrieve packages for them. The Spoon was the first to report on the patent.  The robot would be able to receive messages from and meet a larger delivery vehicle at a specified time and location. Multiple robots would be able to queue themselves whenever a delivery vehicle arrives, so packages are placed with the correct bot, according to the patent. This kind of autonomous pick-up capability could allow them to expand into earlier deliveries, potentially even between 2 a.m. and 6 a.m., the company says in its filings.   [Source: SupplyChainDIVE]

Botched robbery: ATM… 1, Robbers…0.  Caught on video

It’s 4 a.m. on February 17. Surveillance cameras show three masked suspects barge into the 7-Eleven on New Hampshire Avenue in Silver Spring, Maryland. Montgomery County Police say a fourth suspect used a stolen pick-up truck to smash through the convenient store’s front window to free the ATM.

The suspects then used a hand truck to carry off the ATM, which one employee says weighs about 750 pounds. Here’s where everything goes wrong on this botched heist. One of the suspects trips in the parking lot, with the ATM landing on his leg.

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At this point, they try but fail to pick back up the ATM. A third suspect appears upset at the taller suspect, swinging at him at one point. They flee the scene but before leaving a suspect returns for the hand truck. If you recognize any of these suspects, you are urged to contact Montgomery County Police. (The video is hosted on the source website.)   [Source: ABC7 WJLA News]

Retailers using facial recognition to combat fraud and theft

Global retail shrink is a massive problem, and it’s easy to see why retailers are eager to employ new tools. External shrink (i.e. shoplifting and ORC) costs retailers roughly $35.5 billion each year according to the 2018 Sensormatic Global Shrink Index. External shrink is likely to increase over 2019. Last year, an annual ORC study from the National Retail Federation (NRF) reported that 71% of companies experienced a year-over-year increase in ORC incidents.

Most major retailers agree that hiring in-store loss prevention professionals helps reduce external shrink. But even the best loss prevention professionals can’t remember the names and faces of every documented shoplifter that has ever stolen from the store location — let alone neighboring ones.

Retailers also routinely employ a range of security technologies, including electronic article surveillance (EAS), CCTV and pushout prevention to reduce shoplifting. These technologies can alert loss prevention teams during crimes in progress. While this can lead to apprehensions and merchandise recovery, these solutions still put employees in at risk-situations.

- Digital Partner -

Using facial recognition to prevent crime begins by assembling a database of documented shoplifters, organized retail crime associates, disgruntled ex-employees and other individuals that pose a risk. They can be enrolled from video footage or following an apprehension. Then, the moment that a documented shoplifter returns to a store, a camera enabled with a face recognition algorithm can match that individual’s face against the database of images on file.

In the event of a potential match, in-store security professionals can be alerted instantly. This allows them to either observe the suspected individual or proactively offer customer service. Numerous loss prevention executives have told me that most of the time, simply offering a documented shoplifter customer service is enough to get them to vacate a store without incident.

Even if an individual successfully gets away with committing a crime, face recognition can add tremendous value. An image of the retail criminal can be taken from store CCTV or VMS systems and enrolled in the system. You might not have a clue who the person is, but your loss prevention team will know the moment they return to a store.   [Source: Retail TouchPoints]

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