Retailers Are Turning To Face Recognition To Thwart Growing Fraud And Shoplifting Threats

0aaaPeter Trepp FaceFirst

An increasingly large number of retailers are waking up to an unfortunate fact: despite loss prevention and asset protection professionals’ best efforts, organized retail crime and return fraud continue to rise. In order to combat these rising concerns, forward-thinking retailers have started employing facial recognition solutions to protect merchandise, employees and customers from threats. And while this technology is relatively new for retail, it just might prove to be the secret sauce for preventing shrink.

Why More Retailers Are Using Face Recognition

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. Unfortunately, as The D&D Daily regularly reports, hundreds of people die violently each year in retail stores.

However, facial recognition flips the loss prevention script by allowing in-store loss prevention to proactively prevent crimes from occurring.

How Facial Recognition Works

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 security team will know the moment they return to a store.

The Network Effect: How Data Sharing Reduces Crime

Since facial recognition requires an individual being documented in a database in order to recognize them, it does not typically help loss prevention professionals thwart first-time shoplifters. But the biggest source of external shrink by far is perpetrated by organized retail criminals and habitual shoplifters, those who steal expensive items and steal them often.

One of the biggest advantages of facial recognition is that stores within a chain can share a face recognition database. Retailers are currently doing this with increased frequency because organized retail crime gangs are typically quite loyal to their favorite brands, favoring to strike multiple locations within a chain in a single geographic region.

FaceFirst conducted a recidivism study over six months that examined the behavior of documented shoplifters. The study found that 60% of known shoplifters were detected entering at least two separate locations of the same retail chain, while 20% of known shoplifters visited three or more locations of the same retail chain.

Combating Fraud

According to the NRF, 10.8% of returns made each year are fraudulent, costing the retail industry $9.6 billion a year. Facial recognition also has the potential to protect retailers from a specific kind of return fraud.

Many companies have generous return policies that don’t require receipts. But retail criminals are eager to take advantage of these generous policies. From talking to loss prevention executives, I learned that one of the most prevalent return fraud schemes is when dishonest customers take merchandise off shelves and attempt to return it. Facial recognition can be used at return desks to pop a video of that customer entering the store. The clerk can then see whether the customer making the return entered the store with merchandise or not. While this method of return prevention isn’t foolproof, it will provide retailers with a much-needed intelligence layer that can help them assess the validity of returns.

These are just some of the ways that retailers will use face recognition to combat retail crime in 2019. While major retailers already have started using the technology, it’s far from ubiquitous. As adoption increases over the next few years, I expect the retail industry to finally turn the corner on external shrink.


Peter Trepp is the CEO of FaceFirst, a facial recognition solution provider. Trepp has experience as an entrepreneur, advisor and consultant. He has an MBA in finance from UCLA Anderson School of Management. He is based in Encino, Calif. Connect with Trepp on LinkedIn.

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