Retailers are ramping up security measures as shoplifting incidents surge, resulting in significant financial losses.
Retailers are ramping up security measures as shoplifting incidents surge, resulting in significant financial losses.
Beyond traditional deterrents like CCTV cameras and security personnel, businesses are adopting artificial intelligence, data analytics, and automated security systems to address both opportunistic theft and organized retail crime.
“As retail logistics evolves to meet booming e-commerce demands and rising consumer expectations, increased demands on the retail value chain means that distribution centers worldwide are prioritizing safety, security, and efficiency,” explains Axis Communications’ Global Sales Marketing Manager, Louise Hobroh, in a
post. “Network video, equipped with AI and analytics, is supporting distribution centers to achieve this.”
Retail’s billion-dollar theft problem
Retail theft has reached unprecedented levels, with the National Retail Federation (NRF) in the US estimating that shrink—encompassing shoplifting, employee theft, and fraud—cost the industry $112.1 billion in 2022, a significant jump from $93.9 billion in 2021.
Retailers have attributed declining profit margins and store closures to the surge in theft. Several major retailers, including big-box chains and department stores, have cited shoplifting as a key driver of rising operational costs.
In some urban areas, stores have resorted to keeping commonly stolen goods, such as personal care products and electronics, behind locked cases, requiring staff assistance for purchases. The rise in organized retail crime (ORC) has also prompted increased law enforcement scrutiny, with the Department of Homeland Security (DHS) launching a nationwide initiative aimed at dismantling criminal networks linked to retail theft.
AI-powered video surveillance: the first line of defense
Artificial intelligence-powered video surveillance has emerged as a frontline tool in theft prevention. AI-driven systems analyze behavioral patterns in real time, detecting suspicious activities such as loitering, concealed merchandise, or coordinated movements among multiple individuals.
These analytics tools, integrated with existing IP-based security cameras, can alert security personnel instantly when theft indicators are detected. Unlike traditional surveillance systems that require constant human monitoring, AI-powered systems automate detection and reduce the reliance on security personnel, allowing for a more proactive approach to loss prevention.
Retailers using AI analytics have reported improved theft detection rates, particularly in high-risk areas such as self-checkout stations and fitting rooms. AI also enables forensic video analysis, helping loss prevention teams quickly review past incidents, identify suspects, and track movement patterns across multiple stores.
Self-checkout theft and automated monitoring
Self-checkout theft has become a significant concern for retailers, with customers exploiting vulnerabilities such as barcode switching or failing to scan items. To combat this, retailers have adopted AI-based self-checkout monitoring systems that analyze scanning behavior, detect anomalies, and trigger alerts when discrepancies arise.
Some systems employ computer vision to ensure that scanned items match those in the shopping cart, while others incorporate weight sensors that cross-reference scanned items with the expected weight on the bagging platform.
These measures, while effective in reducing fraud, have sparked concerns over false positives, with some customers complaining of being wrongly flagged for theft. Retailers are working to refine AI models to minimize errors while maintaining the efficiency of self-checkout operations.
Electronic article surveillance and RFID systems
Electronic Article Surveillance (EAS) and Radio Frequency Identification (RFID) technologies remain staple solutions for theft deterrence. EAS systems rely on tags attached to merchandise that trigger alarms when removed from stores without deactivation, while RFID tags allow for real-time tracking of products throughout the store.
Retailers leveraging RFID for theft prevention have also integrated it into inventory management, reducing stock discrepancies and ensuring better supply chain visibility. RFID adoption has increased in recent years due to falling costs and improved accuracy, making it a viable tool for retailers seeking both security and operational efficiency.
In addition to loss prevention, RFID helps improve stock replenishment, ensuring popular products remain available on shelves without unnecessary overstocking.
License plate recognition and organized retail crime
Beyond in-store surveillance, retailers are deploying advanced monitoring systems in parking lots using license plate recognition (LPR) technology.
LPR systems capture high-resolution images of vehicle license plates and cross-check them against databases of known offenders, allowing security teams to identify repeat shoplifters.
Retailers working in partnership with law enforcement have used LPR technology to track organized retail crime groups that operate across multiple locations. While LPR is an effective deterrent against repeat offenders, its use has raised privacy concerns, particularly regarding data retention and the potential for misuse.
Advocacy groups have warned against the risks of mass surveillance, pushing for stricter regulations governing the storage and sharing of vehicle data.
Retailers turn to data sharing and incident reporting
Retailers facing rising theft-related losses have also turned to incident reporting and data-sharing platforms that allow businesses to document, analyze, and share theft-related intelligence.
These platforms enable loss prevention teams to collaborate, identify emerging theft trends, and coordinate responses with law enforcement agencies.
By pooling data on known offenders, retailers can preemptively flag high-risk individuals and adjust security strategies accordingly.
Law enforcement agencies are increasingly supporting retail data-sharing initiatives, using shared intelligence to track shoplifting patterns across different jurisdictions.
Some retailers are also leveraging predictive analytics to assess theft risk by time of day, store location, and past incident history, optimizing security personnel deployment accordingly.
Balancing security and customer experience
While advanced surveillance technologies have proven effective in curbing theft, concerns over consumer privacy and data security persist. The use of facial recognition technology in retail stores has drawn criticism from civil rights organizations, with opponents arguing that it disproportionately targets marginalized communities and lacks transparency.
Some cities, including San Francisco and Portland, have banned private entities from using facial recognition due to concerns over racial bias and wrongful identifications.
Retailers face a balancing act between tightening security measures and maintaining a positive customer experience.
Excessively restrictive measures, such as placing high-theft items behind locked cases, have led to customer frustration and longer wait times. Some businesses have experimented with alternative deterrents, such as electronic shelf labels that dynamically update pricing and security features or mobile checkout solutions that reduce self-checkout fraud.
Transparency in security policies and customer education on loss prevention initiatives have also been emphasized to maintain trust. Some retailers have started posting clear notices about security measures, reassuring shoppers that monitoring is in place while respecting customer privacy.