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#WFH: 3 Tools to Tackle Call Center Fraud, Even From Home

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For call center professionals, 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. . Traditional call center designs encourage agents to be in relatively close quarters. What This Means For You.

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The benefits of reducing KBA dependency

TRUSTID

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.

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Key customer authentication learnings from 2018

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In our recently released 2018 Learnings on Customer Authentication market report , we take what we have seen throughout the year to give more insight into the current status of call center authentication. Contact center agents don’t trust KBA. So what have we learned?

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The threat of open information sharing

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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.

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Rethinking the caller authentication journey

TRUSTID

Call center professionals are constantly trying to secure their private data while improving their customer experience. According to the Customer Contact Week special report, “Evaluating Call Center Authentication,” customer qualification processing flaws continue to lurk across the voice-dominated landscape.

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Advantages of early caller validation

TRUSTID

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 call center’s basic defenses.

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Is Your Contact Center AI Biased?

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For a call center professional 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. .