Have you heard about scan avoidance technology? It may not get a lot of high-profile attention outside of the retail world, but it is literally saving retailers billions at manned and self-checkout counters. We recently had the chance to connect with Malay Kundu, founder and CEO of Cambridge, Mass.-based StopLift, to get some thoughts on why retail chains worldwide are installing StopLift Checkout Vision Systems’ Scan-It-All video recognition technology to detect scan avoidance incidence at both the manned and self-checkout. Working with retailers on four continents, including Tesco in the UK, StopLift has already detected and confirmed more than one million incidents at thousands of checkouts.
These incidents include “sweethearting”, when cashiers pretend to scan merchandise but deliberately bypass the scanner, thus not charging the customer for the merchandise. The customer is often a friend, family member or fellow employee working in tandem with the cashier.
“Our technology has found that shoplifting is as much as five times more likely to happen in the self-checkout lane,” said Kundu.
Big Y, with 60 Massachusetts and Connecticut stores, and Albertsons, with 217 stores in the South and West, did away with self-checkout in 2011 in order to foster more human contact and better customer service rather than having customers struggle with bar codes, coupons and payment. On the other hand, CVS Health implemented self-checkout in some urban markets to make shopping faster and more convenient while saving on labor. BJ’s Wholesale Club has implemented self-checkout at nearly every register.
“Retailers always suspected that self-checkouts would be highly prone to scan-avoidance, and our technology has certainly found this to be the case,” Kundu said. “Furthermore, using the incidents detected from their own stores, retailers are now able to train staff on the signals indicating when customers are either having problems using the self-checkout or are exhibiting suspicious behavior.”
StopLift’s Scan-It-All system finds any incidents of scan-avoidance, where merchandise is not scanned or rung up before being given to the customer. This includes incidents which may be due to mistakes by the cashier or customer at self-checkout as well as items left in the shopping cart.
To watch real scan avoidance incidents – including self-checkout – tracked by StopLift, visit www.StopLift.com.
As soon as a scan avoidance incident occurs, StopLift, which constantly monitors 100% of the security video, flags the transaction as suspicious. It quickly reports the incident, identifying the cashier or customer and the date and time of the theft.
Scan-It-All works with existing off-the-shelf overhead cameras. No special camera equipment needs to be purchased or installed, and no changes have to be made to the checkout.
StopLift’s patented computer vision technology visually determines what occurs during each transaction to immediately identify fraud at the checkout. Dishonest associates are identified on the basis of video evidence the first time they conduct a fraudulent transaction, rather than months or even years down the road, significantly reducing inventory shrinkage, deterring future theft, and boosting profitability. Customers are identified at the self-checkout
The technology eliminates costly, time-consuming human review of video, drastically reduces and deters fraud at the checkout, and significantly improves profitability, Kundu said. Rather than take a one-size-fits-all approach, StopLift develops targeted applications to address the specific needs of retailers from different sectors including general merchandise, grocery, and specialty retail.
Retailers have tried to track sweethearting or scan avoidance through data mining, but, as Kundu notes: “How do you do data mining when there’s no data?”
The U.S. National Retail Federation states that approximately $14 billion of retail shrink is due to sweethearting. Supermarkets, with their lower profit margins, are particularly vulnerable to sweethearting, which has accounted for an almost 35% profit loss industrywide.
StopLift Checkout Vision Systems grew out of Kundu’s Harvard Business School research study “Project StopLift” on Retail Loss Prevention. With technological research insights Kundu developed while at MIT, Project StopLift concluded that video recognition could be used to automate and, thus, make possible the comprehensive examination of surveillance video. Prior to founding StopLift, Kundu developed facial recognition systems for identifying terrorists in airports.