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Organized Retail Crime is on the Rise, but AI Can Stop This Troublesome Trend

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Organized retail crime (ORC) has been on the rise recently, with retailers like Nordstrom, Macy’s, and Old Navy falling victim to major shoplifting cases. In addition, Target recently announced that theft and ORC were drivers in its $500 million year-over-year decrease in profits.

These publicized reports and incidents have alerted more retail leaders to the very real business threat that ORC poses. However, even with this enhanced attention to the problem, ORC techniques are ever evolving, creating headaches for retailers trying to make sense of their losses.  

Identifying the patterns that can connect an ORC ring can be like finding a needle in a haystack if you don’t have the right data. An AI-based approach to ORC detection is necessary for breaking ORC rings, reducing the likelihood of these crimes and deterring fraudsters from the targeted retailer. 

What is Organized Retail Crime?

Organized retail crime is a professional-grade, multi-person attempt to defraud a retail business. This can occur in the store, throughout the supply chain or via online orders. Common examples of ORC include when merchandise is stolen and returned for profit or sold through online auctions or in-person sales.

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Typically, ORC is reliant on a single entity, like an address, credit card or store account that connects a complex crime ring across retail stores. For example, one delivery address can be the source of 20 online returns across 20 different names, email addresses and credit cards. Once that center entity is detected, retailers can take action, thus breaking the ORC ring. 

Yet breaking the ORC ring is easier said than done. Some retailers have tried utilizing security cameras and ORC-focused employee training to stop the problem. But without the data to link the individuals involved in the ORC ring, retailers may continue to suffer tremendous loss and shrink every year.

Is Organized Retail Crime Getting Worse?

Organized retail crime is a growing problem. According to the 2022 Organized Retail Crime Survey from the National Retail Federation, in partnership with Appriss Retail and the Loss Prevention Research Council, for every $1 billion in revenue that a U.S. retailer makes, more than $700,000 is lost due to ORC. What’s more, these scenarios are also becoming more blatant in recent months as fraudsters have raided and ransacked department stores.

The same retail security survey from NRF found that most retailers experience a shrinkage rate of around 1.4%, which is most often attributed to shoplifting, employee theft and ORC. The report also found that there was a 26.5% increase in ORC incidents between 2021 and 2022.

As ORC continues to make headlines, many retail executives are taking a closer look at their business models, seeking better methods for stopping these criminal activities. As a result, these retail leaders are turning to artificial intelligence to detect and deter ORC.

How can Retailers Protect Themselves from Organized Retail Crime?

The best way for retailers to avoid becoming a victim of organized retail crime is by relying on data and AI-driven fraud detection that can intelligently review every transaction for potential ORC activity. This may be accomplished by linking every transaction to as much identifiable information as possible, like a shopper’s name, delivery address, billing address, purchasing method, receipt number, loyalty card and more. All this data is encrypted, ensuring safety for buyers while still providing retailers with the tools they need to mitigate ORC.

Then, if the retailer is experiencing a rise in crime, AI can analyze recent purchases for any patterns or commonalities that might suggest the presence of an ORC ring. Once identified as ORC, AI can recommend a way to break the ring, like blocking returns from a particular address or revoking loyalty program benefits from a particular user.

In addition, sometimes employees are involved in enabling ORC. This could take the form of an employee who is allowing outsiders to enter warehouses or a cashier that knowingly accepting returns without a receipt. AI-driven fraud detection can monitor which employees are connected to potentially fraudulent transactions, giving retailers the chance to intervene as soon as possible.

Finally, AI can predict future risks of ORC by noticing troublesome patterns early on while the professional fraudsters are still testing the waters.

Early and efficient detection of problematic patterns related to ORC, made possible by AI, can save retailers thousands of dollars every year. For example, in one case AI was able to link together store credits, gift cards and credit cards that were part of an ORC ring that was attempting to return $224,000 of merchandise for income across 215 stores nationwide. 

Early detection of ORC rings can also help the business establish a reputation for being vigilant against crime, which will decrease the likelihood of these incidents over time. For retailers, protecting oneself from ORC is worth the investment in the short- and long-term.

Bring Down Organized Retail Crime for Good

Stopping organized retail crime is a major undertaking, but data and AI provide the information retailers need to act quickly and avoid unnecessary loss. Every time a retailer can take down an ORC ring before it becomes profitable, they are making a statement to other potential fraudsters.

With up to $700,000 and growing at stake for every $1 billion in revenue, retailers should take the next step toward stopping ORC for good by turning to AI-driven fraud detection.


Dr. Adi Raz serves as the VP of Data Science for Appriss Retail. She has more than 25 years of experience managing data scientists and modeling teams, developing and integrating analytical products and building efficiencies in data operations, analytics, and modeling. Raz is responsible for end-to-end management of all operational aspects of the data and analytical products for Appriss Retail across hundreds of global retailers. Before joining the company in 2004, she was the Senior Director of Data Sciences at Washington Mutual and a pricing analyst for Circuit City. Raz earned her Doctorate in Business and an MBA from Pepperdine University.

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