Unified commerce comes to Brazil
In retail circles, much has been written about unified commerce — the use of one technology platform to combine in-store, mobile, and ecommerce business.
This momentous shift shows no sign of slowing. In fact, retail consultants Boston Retail Partners surveyed 500 top North American retailers and found that, by 2020, 81% of companies will employ some sort of unified commerce approach.
But even as savvy retail companies adapt their models to sell across channels, the need for physical locations is far from obsolete.
In fact, in order to survive, it seems retailers must now provide the best of both worlds — an efficient in-store experience, plus a seamless digital one that is independent of a shopper’s location, available payment method or device.
Buying online, picking up in-store? Buying in-store, receiving the goods at home? Buying in-app, returning in-store? The possibilities are dizzying.
Retailers who succeed will go to great lengths to give customers vast options for how to shop and also how to pay.
While many retailers have cleared some obvious unified commerce hurdles, such as centralizing order management and transforming inventories and supply chains, the question remains: With so many transaction types, what is the best way to fight payments fraud?
Adyen has for years been developing a best-in-class unified commerce risk solution. Each individual channel brings its own risks.
Card not present (CNP), phone orders (MOTO) and in-store (CP) transactions all have unique characteristics and risks, and thus retailers have adopted tailored approaches to deal with the challenges.
But because the risks are so varied, it turns out that a single, unified platform is the ideal tool to manage them.
For many of the same reasons a unified platform increases efficiencies on the front end of a payment transaction— it ensures stability, integrity and ultimately, higher authorization rates and performance — a unified platform is also the best tool to fight payments fraud.
Using a single platform breaks up traditional information silos. For example, linking the personal data and purchase history of a customer who shops online and in a physical store can give critical insights, and vice versa.
A single payment system needs to be able to instantly identify, account and link data across the entire omnichannel network.
The balancing act between offering a seamless buying experience to customers without impeding the overall experience (fraud management needs to be invisible to the customer and create no additional friction) is core to the vision of Adyen’s risk management solution RevenueProtect.
Strategy and marketing departments have long used data to track customer behavior and buying habits.
Now the payments fraud community must take this opportunity to start using incremental data in a similar way.
Take the example of a New York City-based merchant.
The store might create heat maps using anonymized data (IP location and billing addresses) of its customers who purchase online and wish to pick up in a physical store.
The patterns give the retailer both a snapshot of customer buying habits and also a metric to detect potential payments fraud.
Any transactions that do not fit the established pattern should be the first to arouse suspicion.
Think about a customer walking into a Brooklyn retail store. The shopper is buying, let’s say, a pair of shoes using a point of sale terminal, verified by PIN (an ideal scenario for any fraud manager, as liability has shifted in the retailer’s favor).
That same night, the customer buys an additional pair of socks on the retailer’s website, with the shopper’s IP address registering just 10 miles away from the store the person visited a few hours earlier.
Even if the customer uses a different credit card for the online payment (which, due to a lack of historical shopper data, could be considered higher risk), the retailer would know to reduce friction for this new purchase, knowing, based on millions of correlated data sets, that the likelihood of the order being genuine is exceptionally high.
In a similar scenario, let’s say a customer who is historically an online shopper one day decides to visit a physical store. Perhaps, on that day, the shopper’s chip-and-PIN combination fails.
But if the payments fraud system detects the shopper’s long history of online purchases, along with a nearby physical address, it might be more willing to accept a simple card swipe (known as magnetic stripe fallback), even though it would not accept this from a completely ‘unknown’ individual.
While a good payments platform can shed light on subtle customer interactions, it should also pick up on less-subtle ones.
For example, when cards used in one location show up hundreds of miles away minutes later, they raise immediate red flags for being fraudulent purchases.
For decades, retailers (and in fact, all organizations accepting payments) developed manual, channel-specific fraud prevention solutions.
Requiring both a chip and a PIN is a pure in-store authentication mechanism, in the same way that the 3D Secure protocol ensures safety for online transactions. When the tools were less sophisticated and ecommerce was still a novelty, manual review and case management would do.
But ecommerce is now a global economic juggernaut, and speed at checkout is critical in both the physical and digital realms. In this new world of blended commerce, there has never been a greater need for a payments system that uses data from all channels to help merchants make intelligent decisions.
Abraham Maslow wrote, “If all you have is a hammer, everything looks like a nail.” But frequently, in ecommerce and retail, a hammer is too blunt a tool.
Managing payments fraud is not only about keeping out the “bad” transactions, but is also an exercise in removing friction from the “good” ones.
Taking a holistic approach to fraud management by consolidating all available data into a single system is not only the most efficient approach to combating payments fraud but also, in the long run, boosts revenue by giving companies a sophisticated toolkit for an intricate problem.
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