With card issuers making it easier to dispute a transaction in the two years since the Covid-19 pandemic hit, observers say it has become harder for them to distinguish good customers from bad customers, especially when a transaction is disputed post-purchase. The latter case is when friendly fraud can occur, that is, when a consumer disputes an allegedly unrecognized charge on a card statement. And since the pandemic struck, criminals and consumers are disputing some transactions because they have no intention of paying.
To remedy the problem, Chargebacks911, a post-transaction fraud-prevention platform, has partnered with Microsoft Corp. to create a service that combines data and artificial intelligence to detect fraudulent transactions, as well as friendly friend, pre-authorization and post-transaction
The new service leverages Chargebacks911’s friendly fraud analytics and Microsoft’s adaptive artificial intelligence technology, which learns fraud patterns, to optimize fraud controls and reduce losses from post-transaction fraud, also known as fraudulent chargebacks.
“Chargebacks grow about 20% a year, but since Covid, friendly fraud has grown more than 40%,” says Chargebacks911 chief operating officer and co-founder Monica Eaton-Cardone.
A big reason for the explosion in friendly fraud is that card issuers have adopted digital models that enable consumers to dispute a distraction in a few clicks.
Having filters on the front end of the fraud-detection process that weigh post-transaction fraud trends can help to distinguish a consumer who has no intention to pay from a consumer who has simply gotten into the habit of disputing charges he or she doesn’t immediately recognize, Eaton-Cardone says.
“What our solution with Microsoft does is enrich industrywide fraud data with behavioral analytics to create more precision in detecting fraud and reducing false positives, which leads to better decision making and higher acceptance rates,” says Eaton-Cardone. “A lot of consumers that legitimately file chargebacks on a regular basis don’t understand the effect it has downstream with banks and merchants and many wind up getting blacklisted. Our goal is to identify good customers from bad ones by aggregating data to identify the true source of the chargeback.”
E-commerce transactions are the most prone to friendly fraud, as they are about 50 times more likely to be disputed post-transaction via a chargeback claim than a transaction completed at the physical point-of-sale, according to Eaton-Cardone.
“By providing the right data around consumer behavior and infusing it with artificial intelligence, financial institutions can make better decisions around potentially fraudulent transactions pre- and post-transaction,” says Eaton-Cardone.