How to Prevent Card-Not-Present Fraud and Avoid False Positives

As online activity surges, card-not-present fraud is hitting merchants with a tricky problem. In order to prevent fraud and keep customers happy, analysts and manual reviewers have to toe a fine line. You need to stop CNP fraud as it rises, but you can’t risk triggering false positives that turn customers away forever.

Fortunately, there are ways to eliminate both of these pricey problems at the same time. Here are the realities of card-not-present fraud along with the best ways to prevent it without setting off false positives.

Fortunately, there are ways to eliminate both of these pricey problems at the same time. Here are the realities of card-not-present fraud along with the best ways to prevent it without setting off false positives.

What Is Card-Not-Present Fraud?

Card-not-present (CNP) fraud takes place when a fraudster makes a criminal purchase without paying with a physical card. In most cases, the fraudster will use phishing schemes to snag a victim’s credit or debit card information. It’s also common for criminals to steal or buy card information on the dark web. From there, they use the stolen card information to purchase items, defrauding the cardholder and the companies they buy from. 

CNP Fraud Is Growing Rapidly.

Now, more than ever, people are shopping and banking from home. With the COVID-19 pandemic changing the typical consumer’s shopping habits, online retailers have seen a surge in customer activity. And that’s catapulted card-not-present transactions. In fact, CNP buys jumped to account for 55% of all credit in the first few months of the pandemic. 

Alongside the upswing in online activity, CNP fraud has leapt. In fact, CNP now makes up 80% of all fraudulent transactions in the United States. Still, the rise in CNP fraud isn’t the only problem ecommerce companies face. They also have to handle a growing risk of sparking false declines as they try to prevent fraud.

Although automated CNP fraud detection systems can segment bad actors from genuine customers, fraud detection isn’t a one-size-fits-all model, and many transactions are falsely declined. In fact, one study found between 30% and 70% of all declined orders are actually false positives. Alarmingly, every false positive has the power to destroy the company’s reputation and frustrate customers. 

How Does Card-Not-Present Fraud Hurt Ecommerce Companies?

Card-not-present fraud can cut into an ecommerce company’s finances both directly and indirectly. Fraud itself can cost the company lost revenue, but companies also absorb an even more severe financial blow when their fraud-prevention techniques set off false positives. In fact, according to an Aite Group report, false declines cost merchants 75 times more money than fraud itself. That’s because false positives can end up frustrating customers, pushing business away and sending lifetime customer value elsewhere. 

What Are the Keys to CNP Prevention?

Within the traditional ways of fighting CNP fraud, it’s easy to set off costly false positives. That’s because most fraud-prevention strategies involve searching for suspicious behaviors, flagging them and investigating activity from one point to the next. To keep manual reviewers from wasting hours digging into every individual’s identity, this process relies on a set of rules, and when a person violates those rules, they’re usually flagged for decline—whether the purchase was actually legitimate or not. 

One way for analysts and manual review teams to avoid false declines and spot CNP fraud is to look for trust—not violations. For instance, imagine a transaction is sent to manual review because its “ship to” address doesn’t match the customer’s payment address. If you’re only relying on suspicious behavior sweeps, this could be enough to set off a decline. 

However, by looking at connections and relationships, social media or other online interactions could reveal the new “ship to” address belongs to a sibling. In this case, your manual reviewer could identify trust between the two shipping address holders and see the transaction was legitimate. In turn, thanks to the manual reviewer, the transaction is accepted, and no revenue is lost.

Unfortunately, although manual false positive prevention is effective, without the right tools, it’s still time-consuming. That’s why companies need to accompany this holistic approach to fraud with advanced tools. For instance, manual reviewers can use modern search tools driven by machine learning to identify trust quickly. 

With a connection-based search tool, the analyst or manual reviewer simply types customer attributes into the search bar, and this technology quickly makes connections and identifies the purchaser’s trust level. That way, you clearly and quickly spot where CNP fraud is taking place as well as those instances where you would be generating costly false flags otherwise. 

Discover Smarter Ways to Prevent Fraud.

Not all analysts and ecommerce leaders realize it, but there are ways to identify trust that can save swaths of time and money. Ready to discover how? Learn more about the latest in fraud prevention, along with modern ways to identify trust, by reading our whitepaper.