Retail’s War on Self-Checkout Misses Where Theft Actually Happens
Retail’s retreat from self-checkout (SCO) is swift and highly visible as major chains are aggressively scaling back lanes. Even local governments are beginning to get involved, with proposed legislation in places like New York City aimed at restricting usage and increasing staffing requirements.
SCO feels like it’s assumed the most significant amount of blame for theft. However, that framing oversimplifies a much more complex problem and risks pushing the industry toward solutions that add friction without meaningfully addressing loss.
Oversimplifying the Issue of Shrink
Shrink remains one of retail’s most persistent challenges, with billions lost annually. In response, many retailers have concentrated loss prevention efforts on identifying theft at the point of payment, investing in technologies and labor designed to monitor scanning behavior and catch errors at checkout. This approach assumes that most theft happens at the register. In reality, it does not.
Retail analytics show that roughly 80 percent of commonly stolen items are concealed before a shopper ever reaches checkout. Items are hidden under clothing, slipped into bags, or nested inside other products, often directly at the shelf. In short, the majority of loss has already occurred by the time a transaction at the register begins.
When theft is relegated to a checkout issue, the solutions naturally focus there. But if the root of the problem lies earlier in the shopping journey, those solutions will always have limited impact.
Why Scaling Back SCO Isn’t the Answer
Reducing or restricting SCO may create a sense of control, but it does little to address where most theft actually occurs. It also introduces trade-offs that are detrimental to the shopping experience.
SCO has become a core part of the modern retail experience, offering speed, convenience and autonomy while providing staffing efficiencies as well. Rolling it back in a significant way will lead to longer lines, more labor strain, and worse customer experience without delivering meaningful improvements. Consider the New York City proposal mentioned above. Limiting items and assigning more oversight means forcibly removing resources from helping in other areas like finding products, restocking shelves, and more.
Loss Prevention Needs a Broader Lens
Retailer loss prevention strategies tend to begin and end with investment in cameras to watch scanners, where the systems flag missed barcodes and associates intervene when something appears off or to help with troubleshooting.
However, shrink is a sequence of events and not just a single act. It can begin when a shopper picks up an item and hides it, continues as they move through the store — potentially nesting an item inside another — and ends when they exit. Systems that only observe the final step operate without visibility into the actions that matter most. The most important shift retailers can make is expanding that field of view.
Advances in computer vision and shelf-level analytics now make it possible to understand the full product journey from shelf to exit while respecting shopper privacy. In many cases, retailers can deploy it using existing security cameras already installed in-store to stay within budget. This broader visibility allows retailers to detect potential concealment earlier in the process, when it actually happens, rather than relying solely on signals at checkout.
Balancing Security and Customer Experience
Improved full store visibility helps solve the problem of shrink where it’s actually happening and helps retailers tailor their response. The proposed NYC legislation is an example of where broad security policies can punish the shopper while having an uncertain effect on reducing shrink
With better visibility into shopper behavior in the aisles, retailers can align interventions at checkout with actual risk and context. In some cases, that may mean a simple automated “reminder” prompt at checkout of an item they intentionally or unintentionally placed in their pocket or left in their bag. In others cases, it may require human intervention.
This approach reduces friction for customers while allowing store teams to focus on higher-risk situations. It also helps avoid the unintended consequences of overly aggressive measures like locked shelves, receipt checks at the door, and other tactics that can erode trust and deter legitimate shoppers.
None of this suggests that SCO security should be ignored as part of a broader loss prevention strategy. However, treating it as the primary driver of shrink risks overlooking opportunities that greater visibility in-store can help to address.
The future of loss prevention lies in store-wide visibility and not blanket checkout restrictions. By focusing on where theft actually occurs and equipping stores with better insight into the full shopping journey, retailers can reduce shrink without sacrificing convenience.
The goal for retailers is to reduce significant loss and intervene less often. The only way to achieve that goal is through greater accuracy and confidence. When retailers can do that, they reduce loss and create a better experience for everyone in the store.
Daniel Gabay is co-founder and CEO of Trigo, a computer vision leader offering retailers full-store, full product journey loss prevention to ensure items picked up from store shelves are scanned at checkout.
Related story: Solving the Anonymity Problem at Checkout
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Daniel Gabay is co-founder and CEO of Trigo, a computer vision leader offering retailers full-store, full product journey loss prevention to ensure items picked up from store shelves are scanned at checkout. Daniel co-founded Trigo in 2018 and led the development of the company’s core AI platform before stepping into the CEO role to scale deployments across major retail chains worldwide. Under his leadership, Trigo has evolved from autonomous store innovation into a multi-layer, real-time loss prevention platform. Daniel has been recognized on the Forbes 30 Under 30 list and is an authority on the future of Physical AI, retail automation, and privacy-safe computer vision at scale. Connect with him on LinkedIn.




