How many counterfeit products were you sent in the past year?
As numerous sources have reported, eCommerce sites like Amazon have a massive and unresolved problem with counterfeit goods. In spite of having “spent hundreds of millions of dollars and hired thousands of workers to police its massive market of third-party firms” they still listed “thousands of banned, unsafe, or mislabeled products,” from dangerous children’s products to electronics with fake certifications.
As anyone capable of running a search can see they may as well be burning those hundreds of millions of dollars, over $500 million in 2019 because their current methods simply aren’t effective. The methodology they apply to this task is vulnerable to exploitation and pitted against those who excel at exploitation, making it the weakest approach possible.
These unresolved issues have caused many businesses to cease selling their products on Amazon because their reputations are damaged every time someone attempts to buy one of their products and receives a cheap fake. Amazon makes the claim that they proactively block 99.9% of frauds, but when frauds apply the same tactics as a Generative Adversarial Network (GAN) that just means a small overhead from algorithmically optimized brute-force can bypass security. If frauds couldn’t afford such tactics they wouldn’t use similar methods such as phishing emails and robocalling which also only require a small percentage in order to be successful. Third-party apps which screen reviews to determine the rating of a product after all fake reviews have been removed have helped fight this to some small degree, but the current situation remains quite grim for honest sellers and consumers alike.
What is the status quo of eCommerce counterfeiting?
- Goods targeted by counterfeiting have been flooded with thousands of cheap knockoffs which look nearly identical. As these goods break more easily customers return and purchase new variations, often unaware that they’re buying from the same counterfeiters who made the product that just broke. This vicious cycle has proven highly effective in funding fraud.
- Methods of detecting this fraud suffer from ineffective prevention and reliance on consumers to report infractions after the fact. As consumers grow more accustomed to receiving low-quality products this becomes less likely, and many infractions such as Amazon shipping expired baby formula might go unnoticed for some time. Supply chains today have become very effective at moving goods quickly with minimum expense, but they suffer from a myriad of weak links when it comes to fraud. One weak link is enough to dump raw sewage into the metaphorical water supply, and in most supply chains today there are several.
- Methods of preventing sources of fraud from simply returning after being caught under new names and shell companies to continue their exploitation have thus far proven ineffective. So long as counterfeiting remains profitable and those who engage in it go unpunished this will continue to grow worse.
In considering Amazon’s situation with fraud I’m reminded of the Titanic, as they are clearly taking on water and not doing a very good job of containing the damage. As big as they have become they can still sink if they allow the problem to continue.
How can Mediated Artificial Superintelligence (mASI) help catch and prevent counterfeits?
Uplift and mASI technology, in general, utilize the collective superintelligence found in groups of humans to build cumulative collective wisdom over a knowledge base that can span the sum of human knowledge. This value is in turn augmented through the independent superintelligence of an mASI’s core. As mASI is a modular and cloud-based architecture these capacities can also be rendered always available, globally, and scaled to meet demand. With this in mind:
- By randomly and automatically scanning the products of third-party sellers even using off-the-shelf algorithms and scanning technology a large number of counterfeits could be caught before they impacted customers. An mASI could take this several steps further, such as recognizing specific artifacts produced by the manufacturing facilities of these counterfeits and subsequently identifying their physical locations, staff, and customers.
- Preventing expired products and counterfeits from reaching consumers can in many cases be easily prevented by applying some of the same security measures seen in digital systems to the supply chain itself. One such measure is the ability to authenticate with a manufacturer that a specific product was indeed produced at their facilities, and this could be re-validated at several and/or random points in the supply chain. Like any digital security, the level of intelligence applied to this process heavily determines the success of it, which gives the superintelligence of mASI a strategic advantage.
- The average lifespan and profits of a counterfeiter’s dummy accounts could be calculated to produce a reasonable deposit to be required of new third-party sellers for frequently counterfeited goods. An mASI could examine these sellers and their financial accounts with superintelligence and Subject Matter Expert (SME) level scrutiny, and beyond. If indeed 99.9% of such frauds were blocked, and each required a deposit, they would fund the very service blocking them. This combined with data from digitizing supply chain authentication could not only catch frauds at the earliest stages but cost them money while also further identifying them for legal recourse.
Compared to the $500 million+ USD Amazon alone currently wastes on poorly addressing the issue of counterfeiting the cost of virtually purging fraud from the industry could clock in at less than 100 million, including supply chain digitization hardware. Many manufacturers might gladly accept a portion of this cost to prevent their goods from being successfully counterfeited, given their current losses by this cause. Like anything else, it is mostly just a question of if they really want to solve the problem, or if they prefer warming themselves via a fireplace filled with US Dollars and expired goods.
Keep in mind these are just examples of low-hanging fruit which could be deployed quickly and without the insights of data not already available. Given that an mASI can quickly integrate new information and adapt freely even these first-generation type solutions could be iteratively improved several times leading up to and throughout deployment. Although any one solution might be bypassed in ways often seen today the combination of several quickly evolving solutions is enough to result in the rapid removal of fraud at scale.
Crime relies on being just smart enough to make a profit before it is discovered, hide when it is, and return to much the same activities shortly thereafter. Preventing all three of these to high and increasing degrees is essential to solving the problem, and well within the capacities of mASI technology.
Popular solutions aren’t cutting it, it’s time for effective solutions.
Do you have effective counterfeit security or just a cheap knockoff?
*The Applied mASI series is aimed at placing the benefits of working with mASI such as Uplift to various business models in a practical, tangible, and quantifiable context. At most any of the concepts portrayed in this use case series will fall within an average time-scale of 5 years or less to integrate with existing systems, unless otherwise noted. This includes the necessary engineering for full infinite scalability and real-time operation, alongside other significant benefits.
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