Back in mid-December 2013 – at the peak of the holiday shopping season – Target Corporation announced that security around customer debit and credit cards had been compromised and eventually reported that the breach affected 110 million accounts. That’s one in three Americans. While this breach originated within Target’s systems, at the end of the line the buck stops with the banks.
Back in mid-December 2013 – at the peak of the holiday shopping season – Target Corporation announced that security around customer debit and credit cards had been compromised and eventually reported that the breach affected 110 million accounts. That’s one in three Americans. While this breach originated within Target’s systems, at the end of the line the buck stops with the banks.
The Target breach sent a slew of large-scale banks into reactive mode. The sheer scale and scope of the intrusion presented a degree of risk that required immediate and decisive remediation. Unfortunately, many customers learned about the problem when they handed over their cards at the cash register and their transactions were declined. Why? Because banks were forced to impose blanket account restrictions and limits as a knee-jerk reaction to prevent significant losses because they didn’t have real-time visibility into what was happening with individual accounts.
While the potential for staggering losses is significant and industry-wide, providing comprehensive, hassle-free fraud protection to customers is a critical cornerstone of a bank’s value proposition. This puts banks in a precarious pickle around implementing fraud protection measures without unduly inconveniencing customers. Customers want and expect rock-solid fraud protection, unfettered access to their funds and clear, real-time communication about what’s happening with their accounts.
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With real-time analytics, banks can continuously correlate and analyze streams of data from diverse sources – like Target – to immediately spot anomalies indicating potential fraudulent activity. The beauty of real-time analytics with respect to fraud detection lies in the elimination of data latency. Essentially you’re able to detect and halt fraud as it’s happening – not after the fact – to better protect the quality of your customer experience while preventing massive losses.
Consider this typical example. Sally Smith lives in San Francisco. Her account reflects that she’s purchasing large quantities of tires in Houston, a significant departure from her typical spending patterns. With real-time analytics, the fraud prevention department detects the anomaly and automatically sends an alert to customer service to immediately call Sally and confirm the transactions. From Sally’s point of view, it’s far more pleasant to receive a proactive call from her bank than to learn about the fraud at the checkout counter when her transaction is declined.
Now consider this example in scale – Sally Smith and 1 million other customers just like her have experienced a breach at the hands of a third party. Without real-time analytics and automated response protocols, hackers will stream through and fleece their accounts in mass because banks can’t detect and remediate fraud at this magnitude until after the damage is done.
The key takeaway here is that real-time analytics empowers banks to respond to the threat of a large-scale third party breach quickly, decisively and individually, thereby protecting the integrity of hassle-free fraud protection and mitigating losses. It’s good for the bank, and it’s good for customers.