Online fraudsters have gotten more and more sophisticated, making it tougher or financial institutions (FIs) to tell the difference between customer and scammer.
Knowledge-based authentication tools (passwords, for example) can easily be compromised by fraudsters during CNP transactions, sometimes making it impossible to confirm user identities.
There are a number of practices financial institutions use to curb fraud: verifying user IDs, authenticating consecutive visits, tracking transactions, monitoring watchlists and investigating suspicious behavior.
However, these tools are often costly, complicated, inefficient and insecure. There’s no magic bullet for ending financial crime, but rather a platform that integrates a variety of capabilities.
Behavioral analytics monitors transactions for fraud, using data recognition of a customer’s behavior to spot changes, rather than relying on collective data for an entire population.
With the help of artificial intelligence (AI) and machine learning (ML), the newest adaptive behavioral analytics technologies offer real-time deep learning into customer trends and fraud detection to help FIs understand customer behavior, spot anomalies and suspect activity and prevent fraud before it happens.
Banks and other FIs who employ these approaches can shed a spotlight on fraud risks and still process legitimate transactions, while still offering customers a seamless experience.
The Importance of Mitigating Fraud
With the holiday shopping behind us, the corresponding holiday surge in fraudulent activity will — with luck — disappear as well. That doesn’t mean the threat has vanished. COVID is keeping indoors and shopping online, with card-not-present transactions remaining a popular go-to payment option.
Online scammers will keep employing increasingly advanced cybercrime tactics as this eCommerce activity persists.
Banks can combat this threat by: