Other Fraud Methods
In this part of the article, we will present theoretical facts: we will describe the main scenarios encountered by us, our clients and partners. Interestingly, merchandisers from different countries use typical fraud methods, so these scenarios can be grouped into several categories.

“It’s Not Me, It’s Him!”
Since field specialists’ work depends on gadgets, software, internet coverage and other force majeure circumstances, there is a strong temptation to shift responsibility for inability to work to external causes. The most common explanations sound like this:

“The app wouldn’t launch/work, so I couldn’t do my job”
“The software drained my phone’s battery and it died”
“Your app froze my phone”
“There was no internet in the store, I couldn’t send anything.”
What can be done:
Track the employee’s equipment status using special software (for example, what battery level they started their shift with). Monitor their actions in the app and the app’s behavior (whether it actually froze).

Location Spoofing
This method is quite old and was used by merchandisers even before retailers implemented image recognition technology. The idea was that specialists would report taking photos in one store while actually being in another. It’s worth noting that such deception was possible until vendors introduced employee geolocation tracking.

In the modern world, this scenario has undergone technological improvement: now field specialists spoof or reset (making real GPS coordinates disappear) their location using apps like “fake GPS” that deceive tracking systems. The motivation for using fake geolocation may be higher scores for visiting certain stores (for example, newly opened ones) or more convenient store locations for the employee.

What can be done:
According to our statistics, about 5% of merchandisers attempt to deceive employers using such programs. The optimal solution currently is continuous monitoring of employee geolocation. Supervisors can see the merchandiser’s movement route between stores with time stamps, thus tracking the movement map and time spent in each store. There is also an alert system where an employee leaving a store early receives a device notification. If they don’t return, a countdown timer starts, and the agent gets another notification that their visit may be marked as potentially fraudulent.

Fake Camera and Object Substitution
Third-party “fake camera” apps are widely available in app stores. These apps have interfaces very similar to real cameras but can’t actually take photos: instead they use fake images (in our case – others’ photos of displays) preloaded to the device storage. Thus, dishonest agents use “fake camera” apps to upload photos of perfect displays from shared databases. Without making any real effort, they get excellent “work” results, high scores and good compensation by the end of the day. Meanwhile, trade marketers remain completely unaware of the actual shelf situation.

What can be done:
Replace the system camera with proprietary software. Develop special algorithms to check image uniqueness. Ban duplicate images in the system.

Staged Photos
This fraud type could be called the most creative. Merchandisers, having material interest in placing certain SKUs (e.g., new products) in premium locations (“golden shelf”, checkout zone), temporarily place SKUs in required spots, photograph them, submit reports, then remove the products from premium placements.

What can be done:
This fraud type is hardest to track, as it’s difficult to programmatically detect photographing one product in multiple store zones, and impossible to prevent merchandisers from moving products.
However, practice shows reduced fraud of this type when changing incentive programs: removing bonuses for premium placements eliminates the motivation.

Low-Tech Fraud
Until now we’ve discussed technology-related fraud. But there’s also “low-tech” fraud, which is quite common. We know cases where an agency hires one employee and gives them two smartphones to do two people’s work, while charging clients for two specialists though only paying one.
Merchandisers themselves sometimes work multiple contracts simultaneously, devoting just a couple hours to each rather than a full workday. This can critically impact merchandising quality in stores.

What can be done:
When possible, prohibit multiple specialists from visiting one store. If this restriction isn’t feasible and clients need multiple reps in one store simultaneously, set other limits: for example, allow hypermarkets but prohibit smaller stores.

#fraud #shelfmatch #recognition #AI #tech

The retail landscape is at a crossroads. As consumer expectations soar and labor challenges persist, a critical question emerges: Can robots solve what humans simply can’t scale? The answer may lie in Nvidia’s groundbreaking robotics initiatives, reigniting a revolution that began years ago—but was derailed by an unexpected disruptor: the pandemic.

The Problem: Precision Meets Pressure

Retail’s Achilles’ heel has long been *inventory accuracy, operational efficiency, and 24/7 consistency. Humans excel at customer interaction, but repetitive tasks like restocking, shelf auditing, and real-time data collection often lead to errors, burnout, and rising costs. Enter *autonomous robots—designed to handle these tasks with surgical precision, freeing staff to focus on what truly matters: the customer.

The Pioneers: A Dream Deferred

Before COVID, companies like Walmart and Amazon led the charge. Walmart famously partnered with Bossa Nova Robotics, deploying shelf-scanning robots to track inventory in real time. These machines roamed aisles, identifying gaps and pricing errors, promising a leap toward the “perfect store.” But the dream hit a wall: Walmart abruptly ended the partnership in 2020, and Bossa Nova, reliant on the retail giant, shuttered operations shortly after.

Lessons learned?
•⁠ ⁠Scalability ≠ sustainability: Early robots struggled with ROI and adaptability.
•⁠ ⁠Over-reliance on single clients can be fatal.
•⁠ ⁠The pandemic accelerated retail’s pivot to survival mode, freezing ambitious tech experiments.

Amazon, meanwhile, doubled down on warehouse automation, proving that some robotics could thrive—but the path was far from smooth.

2025: The Perfect Storm for a Robotic Renaissance

Fast-forward to today. Nvidia’s AI-driven robotics platforms (think Omniverse simulations and Isaac robotics tools) are enabling smarter, faster, and more adaptable machines. These solutions learn from past failures: they’re cheaper, more flexible, and designed to integrate with—not replace—human workflows.

Paired with post-COVID realities—chronic labor gaps, demand for omnichannel agility, and tighter margins—the conditions for a retail robotics reboot have never been better.

Will This Finally Unlock the “Perfect Store”?

Imagine stores where robots:
– Auto-restock shelves before items run out.
– Generate real-time analytics to predict trends.
– Seamlessly blend with human teams to elevate service.

But the ghosts of Bossa Nova linger: Can this generation of robots avoid the pitfalls of their predecessors?

What do YOU think?

1.⁠ ⁠Is the “Walmart-Bossa Nova saga” a cautionary tale or a stepping stone for future tech?
2.⁠ ⁠Will AI-driven robots finally crack the code of the “perfect store,” or are we chasing a sci-fi fantasy?

P.S. If Philip K. Dick were alive today, he might write: “Do algorithms dream of optimized supply chains?”

RetailTech #Robotics #FutureOfWork #AI #Innovation

3) Detecting Photos Re-shot from Screens or Other Photos

In this type of fraud, a merchandiser photographs not an actual product display in a store but an image of the desired display (a photo of a photo). The merchandiser uses the same databases of “correct” product placement images as described in Section 1. Typically, the agent uses two devices—a personal one and a corporate one. They open the target display image on their personal device and then re-photograph it with the corporate device, uploading it into the system “for verification.”

While photos taken from screens were once easily recognizable, advancements in display clarity and smartphone camera quality have made detection much more challenging.

Another source of high-quality images can be printed merchandising guidelines (merch books) that showcase ideal product placements. In our experience, such cases were rare, but this method does exist—even if it seems somewhat absurd at first glance. Here, an employee photographs a printed image and uploads it instead of a real shelf photo. We’ve even encountered cases where merchandisers took screenshots from digital merch books and submitted those.

How We Tackle This Fraud

Our first attempts to train our system to recognize this type of fraud date back several years. At the time, screen-captured photos differed significantly from standard ones. Additionally, we worked with smaller clients, had lower photo processing volumes, and merchandisers were less inventive—making fraud detection relatively straightforward.

However, as technology improved, merchandisers grew more creative, and ShelfMatch™ clients became larger and more demanding. The old “brute-force” training approach no longer delivered the required high accuracy.

The main issue we faced with one of our largest clients was a high false-positive rate. While our neural network excelled at detecting manipulated images, it also frequently flagged legitimate photos as fraudulent. We found that our fake detection accuracy hovered around just 60%.

Since our client wanted not just to detect fraud but also to penalize negligent employees, achieving near-perfect accuracy was critical. While 100% precision is unattainable in real-world data, we needed to get as close as possible.

Our Solution

We researched academic literature and explored various approaches to improve detection.

After studying the problem, we supplemented our dataset with external datasets used in similar fields:

• Rewind Recapture Dataset
• MRGAN for Recaptured Image Detection

This expanded our data pool but did not significantly improve recognition quality—a major hurdle.

Research confirms that neural networks often struggle with domain shift (poor generalization to unseen data). Simply adding external datasets doesn’t help; the training data must closely resemble real-world production images.

Manual labeling of client-submitted photos was an option, but it was too slow to achieve the desired accuracy quickly.

The Breakthrough

We thought: When we lack training images of specific SKUs, we visit stores and take photos ourselves. Why not apply the same logic here?

So, we asked our team to play the role of “inventive merchandisers” and submit fake store display photos.

It worked!

• We added these self-generated images to our training dataset.
• Testing was conducted on real client photo streams.

Results:

• 90% fraud detection accuracy
• Only ~3% false positives

While we continue refining our system, our solution already helps clients detect fraud at scale.

“For every clever merchandiser, there’s an equally clever software developer!” 😊

Need Help Fighting Merchandising Fraud?

If digital fraud detection is a priority for your business, our experts are ready to assist. Contact us—we’ll respond promptly and tailor a solution to your needs.

In the next (final) article, we’ll cover other fraud methods in retail merchandising.