Friendly fraud, and the importance of a personalized user experience

Subpar user experiences come with a steep price tag in today’s digital marketplace. As digital banking, event ticketing, and e-commerce traffic continue to rise, customers have more options than ever before — and 88% of online customers are less likely to return to a website after a bad online experience.

But giving customers a smooth and frustration-free user experience isn’t as straightforward as it seems considering the added verification steps many organizations require for security purposes. Improving user experience without sacrificing security layers is a difficult task, particularly when it comes to ambiguous user behaviors — like friendly fraud.

Boy shopping with cart on 2d computer

Friendly fraud: a $20 billion problem

Friendly fraud (or first-party fraud) takes place when a cardholder identifies a purchase on their transaction statement as fraudulent and disputes it, initiating the chargeback process. Sounds innocent enough, right?

For a bit of context, the chargeback system was established by the Fair Credit Billing Act of 1974 to protect customers from credit card fraud. And while the way we shop has evolved since 1974, the chargeback system has largely remained the same, creating loopholes for bad actors to exploit.

Friendly fraud is difficult for merchants to prevent because it can be both intentional or unintentional. For example, imagine a scenario where a mother notices a series of small transactions on her bank statement she doesn’t recognize. She issues a chargeback to her bank and claims she didn’t authorize the purchases. What she doesn’t know is that her young son took her credit card from the kitchen table and used it to buy tokens in a mobile game. If the bank fails to investigate the purchases, the creators of the mobile game will lose revenue on valid transactions.

But in a different scenario, the mother splurges on a mobile game for her son. She spends a little more than intended and issues a chargeback, falsely claiming that someone stole her credit card in order to get her money back. Without any background knowledge, these two scenarios appear similar to the bank. Both transactions come from a trusted user with a recognized credit card and a familiar IP address, and both happen at an unsuspicious time.

It’s gray areas like these that contributed to $20 billion in chargeback losses for eCommerce merchants in 2021 – and this already enormous number is only increasing.

Why context is the key to solving friendly fraud

A seemingly rational response to friendly fraud is to add verification steps for each and every purchase. But this approach ignores the context behind each transaction. Today’s online customers are willing to accept friction in certain situations, but will reject it in others. The key to resolving friendly fraud is identifying these situations and offering the appropriate amount of friction.

For example, if a customer wants to establish a new bank account, they’ll likely feel comfortable providing their social security number at first interaction. Here, the customer understands setting up a bank account is a more complex process than purchasing a mobile app or paying for a bus ticket — and additional security steps are welcome. In fact, if the setup process is too simple, a new banking customer may express concern about how easy it was to establish an account with so little information — and question whether their data is secure.

Out-of-context friction will continue to be a problem if merchants don’t invest in new solutions. Offering personalized user experiences is easier said than done, but intelligent friction can make it easier. And how can merchants offer intelligent friction? By taking advantage of behavioral tools that can flag both normal and suspicious behavior.

How behavioral biometrics provide friction at the right times

Behavioral biometrics go beyond traditional security measures by building user profiles based on inherent human behaviors, including a user’s typing cadence, mouse movement, and average time spent on the webpage. These security tools work in the background, learning more about a user as they continue to interact with an online platform throughout the entire user journey.

In our example with the mother and her devious son, behavioral tools would identify differences in behavior between mother and son, such as their familiarity with the purchase form and input speed, and flag the son’s attempted transaction as risky to the merchant. And if it’s the mother making the transaction, the tools would recognize that the purchase aligns with the profile of her past behaviors — from how quickly she enters her password to how many mistakes she makes when doing so. If the merchant and issuing bank communicate these insights to each other, they can push back on the mother’s claims of fraud with a stronger foundation.

Improved communication between merchants and banks is essential to curb the problem of friendly fraud. Fraudsters already communicate with each other through channels like the dark web on how to sidestep security measures. But with behavioral biometrics, merchants and banks have access to intelligent data they can use to push back on suspicious fraud claims. They won’t disrupt the user experience unless they deem it necessary — and if they do, the friction they add will align with the context of the situation.

Consistent UX attracts new customers and keeps existing customers satisfied

An intuitive user experience opens the door to increased business and more satisfied customers. But harmful cybersecurity trends like friendly fraud present barriers that both merchants and banks are still trying to figure out. Fortunately, behavioral biometrics can flag suspicious or ambiguous behavior during the chargeback process while allowing trusted customers to continue on without disruptions. This combination reduces merchants’ chargeback losses and gives customers a personalized and smooth user journey.

Click here to learn more insights on how behavioral biometrics can prevent friendly fraud from nudata’s Michelle Hafner in our Making Sense of Online Identity video series.