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Eileen Shulock, VP eCommerce at Kirna Zabête
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All posts with the tag False Declines

In the past, credit card fraud was commonly determined by checking whether a shopper’s credit card matched their ID. With the advent of online retail, however, catching fraudsters became a more complex task and many merchants resorted to automatically declining orders containing data mismatches.

Initially this approach seemed practical, as the majority of eCommerce stores catered to a domestic market, which meant fewer legitimate reasons for discrepancies between a credit card BIN country and shipping destination (for example). Today, with a rapidly expanding global customer base, there are many situations where mismatches are explainable – or even to be expected – in good orders.

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It’s the 2nd largest eCommerce market in Latin America, with nearly 60 million internet users. More than half of online shoppers frequent international websites, and online purchases are expected to hit $65 billion by 2020. So why are so many eCommerce merchants so cautious with Mexican orders, to the extent of blocking Mexican IPs altogether?

Mexico has a bad reputation when it comes to online fraud. In 2016, the Mexican chargeback rates was 4 times higher than the global average, so it’s hardly surprising that fear of fraud leads many eCommerce merchants to shut their virtual doors to Mexican consumers. In this post, I’ll demonstrate how businesses who block Mexican orders are making a costly mistake – turning away many good customers and a lot of revenue. I’ll also provide tips for managing fraud from this market.

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Fraudsters employ all sorts of tricks to deceive online retailers and get away with eCommerce fraud. In previous Halloween posts we shared best practices for deciphering between good and bad orders, as well as tips for unmasking fraudsters (while keeping false declines to a minimum).

This Halloween, we’ve decided to put our readers to the test! Take our quiz, based on data from real orders we’ve reviewed, to find out if you’re easily tricked, or could cut it as a fraud analyst!

Good luck…

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AVS (Address Verification System) was designed to combat CNP (Card Not Present) fraud. The idea behind AVS is simple: cross-referencing the numeric elements of the billing address provided by the buyer with the numeric portions of the billing address on file at the credit card issuer will enable merchants to verify that the buyer is the rightful cardholder.

Payment processors encourage merchants to set automatic AVS mismatch filters as an anti-fraud measure. However, many merchants who use these filters do not realize that a full AVS match does not ensure a transaction isn’t fraudulent. On the flip side, orders with AVS mismatches are often legitimate. In this post, we will show why rejecting orders solely based on AVS information is a bad idea.

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Managing eCommerce fraud operations is no easy task; whether hiring, training, and managing a manual review team, monitoring approval rates, and optimizing internal rules, a fraud manager’s attention is often drawn to many places at once. With so much on their plate at any given time, it’s easy to understand why merchants are drawn to “silver bullet” solutions to manage and prevent chargebacks.

One “solution” to chargebacks often utilized by merchants is fraud prevention blacklists. When hit with a chargeback, all the transaction details are simply added to a blacklist, so that the next time an order is placed from the same email or IP address, the transaction is automatically declined. While they may seem like a great way to streamline internal operations and to prevent future fraud, blacklists are in fact a misguided way to address chargebacks.

Blacklists block not only fraudsters but also many good customers. Moreover, there are basic methods fraudsters can use to “fool” your blacklists. In this post, I will explain why you should stop relying on blacklists for fraud prevention.

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When it comes to protecting their revenue from fraud, airlines and online travel agencies (OTAs) find themselves in a catch-22. Strict rules and fraud filters reduce chargebacks, but given that clear-cut fraud only occurs in approximately 2% of card-not-present flight orders, these measures also inevitably lead to costly false declines and disgruntled [ex]customers.

In this post I discuss how airlines and OTAs can manage this conflict by using data from a range of sources to evaluate orders containing mismatches, rather than relying on discrete data points to identify and block ‘suspicious’ customers. By investigating online shopping behaviour through less formal fraud detection techniques, online sellers can leverage their data to get ahead of the game and boost online revenue.

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Today’s college students are increasingly going online to spend their money, and retailers go to great lengths to attract this consumer segment. In fact, US college students have an estimated buying power of $545  billion, making them a very lucrative segment for online retailers to targetMany retailers offer deep student discounts and run high-profile ‘back to school’ campaigns in an attempt to capture some of the market.

However,  many retailers fail to realize that fraud prevention measures are holding them back from maximizing online revenue from students. In this post we share key stats about the fraud patterns and online shopping behavior of US students, which we hope will allow merchants to capture more revenue from this consumer segment.

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At this year’s summer Olympics in Brazil, eCommerce retailers and elite athletes will have one important thing in common: opportunity. That’s because athletes and online retailers alike will also be hunting for gold and silver. For merchants, it will come in the form of the payments consumers will be handing out from their pockets via Brazilian digital sales channels. The stakes could not be higher; Brazil’s 2016 retail eCommerce market is estimated to be worth over $20 billion.

However, the Rio Olympics also offer more sinister parallels as well. Many online retailers are wary of Brazilian fraudsters, resulting in hesitation to take full advantage of the country’s market. The result is that – like Russian track & field athletes who saw their opportunity stripped via a blanket competition ban brought on due to a fear of doping – retailers face the prospect of their own chances to make the most out of the 10th largest eCommerce market being taken away over fear of fraud.

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As consumers increasingly go online to make their purchases, luxury fashion retailers are poised to realize huge gains in eCommerce revenue. It’s estimated that the market for online luxury goods will be worth more than 20 billion euros by 2018. Moreover, the move towards digital purchases will only accelerate this trend. Last year, US consumers were expected to spend half of their budgets for luxury fashion and accessories online.

Riskified works with many luxury fashion businesses – including FarFetch, Vestiaire Collective, and Ssense. Our experience with these retailers has given us great insight into what card-not-present (CNP) fraud patterns look like within this industry. In this post, I share these insights and discuss what measures retailers can take to protect themselves without negatively impacting sales.

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As the eCommerce market continues to grow, so does card-not-present fraud. However, many online retailers don’t realize that more money is lost to false positive declines – purchases wrongly identified as fraud attempts – than is lost to actual CNP fraud. For retailers, there’s no simple way to determine how many of the orders declined due to suspected fraud should actually have been approved. But just because it’s not easy to quantify, doesn’t mean it’s not an important problem.

We created this infographic to shed light on false declines – the silent revenue killer. The infographic is based on Riskified’s internal data as well as on research conducted by Javelin Strategy.  By illustrating the scope of the false declines issue, we hope to show retailers how much they stand to gain by working to eliminate this problem. 

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