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While banks know that relying on conventional knowledge-based authentication (KBA) can leave customer accounts vulnerable to social engineering attacks, many continue to take that risk anyway. I know change can be difficult, and slow for many industries. But the free flow of personal information over the internet changed all that.
In other words, these personal nuggets are gold to social engineers. The data is collected by fraudsters to build personal profiles they can sell to other criminals, apply for credit cards, or socially engineer banks to takeover customer accounts.
If organizations are compromised, social engineers will have the data they need to have telephone agents reset account passwords en route to taking over legitimate accounts. Contact center agents don’t trust KBA.
Callcenterprofessionals are constantly trying to secure their private data while improving their customer experience. According to the Customer Contact Week special report, “Evaluating CallCenter Authentication,” customer qualification processing flaws continue to lurk across the voice-dominated landscape.
By verifying callers before a phone agent picks up, contact centers can remove the risk of account takeovers and other fraudulent activity. These phone scams use personally identifiable information (PII) to answer telephone security questions to slip passed callcenter’s basic defenses.
For callcenterprofessionals, the rise in fraud in addition to optimizing operations and the workforce from home, are top of mind as businesses move towards remote-work as the standard. . The FTC has already begun warning consumers of medical and finance-related scams; across the pond, the U.K.
But managing its day-to-day operations has become tougher as contemporary callcenter managers have to cope with several challenges including post-covid work scenarios, high attrition rate, increasing competition, and more. How Does a CallCenter Work?
For a callcenterprofessional concerned with fraud mitigation, a real-world form of this bias is an AI systematically ignoring costly fraudster activity and instead focusing on genuine caller behavior and flagging it as suspicious or fraudulent because it doesn’t “fit” the criteria for fraud that the machine has learned. .
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