How Does Friendly Fraud Impact False Declines?
by Steve Durney
— when cardholders wrongly dispute purchases they or someone else in their household made — has fueled another costly problem for the payments industry: a sharp rise in false declines. The two may not seem connected, but machine learning and artificial intelligence, both used increasingly in fraud-detection systems, are each highly susceptible to GIGO (garbage in, garbage out).
False declines (also called false positives) are valid transactions that are incorrectly rejected by issuers and merchants as fraudulent. They’re an unintended consequence of the struggle for equilibrium that exists when managing good and fraudulent behaviors at scale. But on Main Street, it’s a frustrating experience for those trustworthy cardholders, and it can take a serious toll on the bottom line for merchants and issuers.
The High Costs of False Declines
Aite Group estimates that issuers falsely rejected $264 billion in U.S. transactions in 2016, forecasting that the amount could grow to $331 billion by 2018. Another study conducted by Ethoca found that upwards of 52% of orders that merchants thought were fraudulent turned out to be good orders that could have been fulfilled. That’s a lot of money being left on the table.
Let’s look at how the growing number of false declines hurts all parties involved:
When transactions are wrongly declined, issuers lose the interchange fees and interest they would have generated on those transactions. Worse, they lose trust and card loyalty (the “back of the wallet” phenomenon).
An issuer may lose credibility with a cardholder who experiences a false decline, and that cardholder may choose to use another payment card and never return to the one that was declined. In fact, research by Javelin has shown that 39% of cardholders will abandon a card post decline, and 25% will move a declined card to “back of the wallet.”
Similarly, both online and brick-and-mortar stores lose valuable revenue due to false declines. Shoppers whose cards are declined may abandon their purchase and leave the store — possibly never to return.
When their cards are falsely rejected, consumers become frustrated or even embarrassed. Some may not have a backup card to pay with, forcing them to abandon their purchase. Ultimately the incident may damage their relationship with both the merchant and issuer as they decide whether to take their business elsewhere.
Friendly Fraud Fuels False Declines
So, how does a friendly fraud claim on shoes purchased online by John Smith create a false decline for Mary Jones one month later?
Merchants and issuers today rely on fraud-detection rules and models that are only as good as the data that feeds them (remember: garbage in, garbage out). When friendly fraud rears its ugly head, it gets treated like true fraud, wrecking rules and models and leading to an increase in false declines.
As a rule, the more friendly fraud a merchant encounters, the more likely they are to suffer a high rate of false declines. For example: Digital goods tend to experience a very high rate of friendly fraud-related disputes — sometimes as high as 90%. Because genuine transactions are being coded as fraudulent, issuers’ models begin to learn and believe that fraudsters are attacking particular MCC codes, regions, or merchant types. As a defense mechanism, AI/ML tools may decline more transactions — including many legitimate ones. Cardholders trying to make digital-good purchases may be wrongly declined for reasons such as making a purchase that falls outside their spending patterns or profile or that inadvertently looks like the incorrectly marked friendly fraud transactions.
Solving the False Decline Debacle
Given the direct relationship between friendly fraud and false declines, it’s imperative that issuers and merchants adopt solutions that stem the tide of friendly fraud, educate consumers and place responsibility for payment without a negative customer experience. The best way to do that: prevent needless card disputes before they happen.
New, real-time collaboration tools between and provide cardholders with detailed and immediate information about their purchases, which can greatly reduce the frequency of friendly fraud — in turn, reducing false declines. For instance, merchants can provide the IP address and geographic location that a purchase was made from. These purchase details appear immediately on the cardholder’s statement via their desktop or mobile banking app. This approach can help the cardholder confirm the purchase as genuine — before they dispute the transaction because it is unrecognizable. Goodbye friendly fraud, goodbye false declines.
Want to learn more about solutions that can help combat the growing friendly fraud and false declines problems? or connect with an Ethoca expert today.