What Omnichannel Fraud Looks Like

By Rafael Lourenco, Executive Vice President, ClearSale

As retailers add and expand omnichannel operations, their fraud management challenges are growing, too. Organized retail crime (ORC) targets omnichannel retailers in several ways, including “buy-online, pick-up-in-store” e-commerce fraud, online card fraud, return fraud, and even old-fashioned brazen shoplifting. While shoplifting is a matter for in-store security teams and local police, other types of omnichannel fraud can be reduced or prevented by working with experts who deploy some specific screening practices. Let’s look at a few typical omnichannel fraud challenges and ways to prevent them.

Card-testing fraud happens when criminals try to match stolen credit card numbers with the other data they need to complete online purchases: expiration dates and card verification values (CVVs). To do this, they make small “test” purchases with online and omnichannel retailers that don’t limit the number of attempts a customer can make to get their payment data right. Card-testers can also try making purchases of increasingly expensive items until they run up against the card’s limit. Industry experts say card-testing fraud has increased by 200% so far in 2017.

The solution to card testing fraud includes comprehensive screening of IP addresses and velocity checks to limit data-entry attempts at checkout to prevent endless testing.

Card-testing fraud usually leads to chargebacks by the real cardholder, but these aren’t the only chargebacks omnichannel retailers face. So-called friendly fraud occurs when legitimate-seeming customers make a purchase, receive their merchandise, and then contact their card issuer to falsely claim the order never arrived. Then they either keep the merchandise, re-sell it, or pass it off to someone else to make a no-receipt in-store return. In all of these cases, merchants lose the sale, the value of the merchandise shipped, and the cost of the chargeback fee levied by the payment processor. Excessive chargebacks can also lead to higher payment processing fees and account closure.

To prevent friendly fraud, merchants must track their shipments and document delivery of packages. They must also have a screening system that compares customer data to lists of known fraudsters and analyzes each order for a number of risk factors.

Return fraud is another challenge that omnichannel retailers contend with. In 2015 alone, fraudulent returns were a $2.2 billion problem for retailers. There are many variations on return fraud, including returns of items purchased with stolen gift cards or credit cards, no-receipt returns of items that were falsely charged back as undelivered, and returns of items that have had valuable components stripped out for resale.

To get a sense of how much financial damage return fraud can cause, consider this example. A fraudster steals $165 worth of product (via card fraud or shoplifting), returns it to the retailer, and takes their return refund in the form of a gift card worth $165. Now the fraudster offers the gift card for sale at a discounted price of $120. A bargain-hunter snaps it up and spends the full face value of $165 with the retailer. Now the merchant has lost $330 worth of goods to the fraudster and the unsuspecting bargain hunter.

To guard against return fraud, proper order screening is a must to avoid purchases made with stolen cards. As mentioned above, order shipments and deliveries must be tracked and documented. And in-store return staffers should have access to order data, e-receipts, and customer contact information to spot and prevent fraudulent returns.

As you can see, card fraud, chargeback fraud, and return fraud are all interconnected for omnichannel retailers. There are several other types of fraud that omnichannel retailers can suffer, too, including mobile payment fraud, cross-border fraud, and high-volume automated botnet fraud. To reduce your company’s exposure and fraud losses, it’s more necessary now than ever before to work with a fraud prevention specialist that uses machine learning and human intelligence to screen out scammers and spot new types of fraud without alienating good customers or increasing false declines. You can learn more about fraud trends and fraud prevention on the ClearSale blog.