There’s no question that the Coronavirus had an enormous impact on the global economy. In the U.S. alone, The Pew Research Center found that 9.6 million workers between the ages of 16 and 64 lost their jobs during the first three quarters of 2020, pushing the unemployment rate up to 8.6% compared to just 3.8% in 2019.
At the same time, recent research published by the National Retail Federation (NRF) found that e-commerce return rates doubled, with $102 billion of goods – nearly 20% of the $565 billion total sold – being sent back to merchants. Of those returns, $7.7 billion worth, or 7.5%, were deemed fraudulent. This is significantly higher than the overall rate of fraudulent returns, which, says NRF, stood at 5.9% across all retail. That means a retailer loses $5.90 to return fraud for every $100 of merchandise they sell.
While we can’t attribute the rise in fraud directly to the pandemic, economists’ empirical evidence suggest a correlation between job losses and crime rates, so it shouldn’t be surprising to see that fraud is on the rise.
Entrupy Fingerprinting is a solution created to help retailers prevent losses incurred from return fraud. Leveraging Entrupy computer vision and microscopy-based technology, it gives retailers a powerful, non-obtrusive way to identify specific products, enabling retailers to know, instantly, whether an item returned is the same as the item sold. Requiring only a simple handheld scanner, the solution is easily implemented, anywhere and anytime.
With Entrupy Fingerprinting, retailers can “register” high value items by using the Entrupy device to capture a single microscopic image of the product. The image, stored securely in the Entrupy Cloud, acts as a digital fingerprint. At any point in the product lifecycle including at the time of return, that exact item’s identity can be verified.
For example, if a retailer sells a high-end handbag that has been registered using Fingerprinting, then that item is returned, the store or warehouse associate can verify that it is, in fact, the exact same item. The associate need only capture a new microscopic image of the product, which is then compared to the registered fingerprint using Entrupy’s patented artificial intelligence technology. In seconds, the associate will receive the results. If it is a “match,” they can proceed with the return. A “no match” result means the item is not the same and the associate should not accept the return.
As we head into the world’s major shopping seasons – back to school, single’s day, black friday and the whole holiday season – it’s time for retailers to not only consider their own susceptibility to return fraud, but also think about the negative impact of inefficiencies in their current return process.
The NRF predicts that, in the U.S. alone, nearly $10.2 billion of suspicious returns will be processed this holiday season, and according to their survey, nearly 91% of retailers expect that their employees will need at least the same time or more to process the growing number. Not only is this a burden on labor, it can damage customer relationships by delaying refunds for law-abiding customers with legitimate returns. Additionally, overworked employees who are rushing the process may inadvertently allow fraudulent products to be restocked, leading to the potential for reputational disaster.
Armed with Entrupy Fingerprinting, retailers have a 100% accurate method of facilitating faster return processing, resulting in delighted customers and a more secure supply chain. They can effectively manage return policies and overcome a reliance on human expertise, static knowledge and traditional tagging solutions which are prone to exploitation by bad actors.
If you’re a loss prevention professional looking for ways to cut down returns for the upcoming holiday season, we can certainly help you get started with a simple, quick ‘Proof of Concept’ to see how it works in your own ecosystem. Once you see the results yourselves, you can scale at your own pace. Feel free to write to us at [email protected] to schedule a demo.
