How Deepfake Detection and Voice Biometrics Protect Contact Centers from AI Voice Fraud

Voice has always been the most human way we communicate. Even with chat, apps and automation everywhere, people still pick up the phone when the stakes are high: resolving a fraud alert, moving money, resetting an account, or getting urgent support from a real person.

That is why the voice channel remains so vital for banks, insurers, telcos and government agencies. It is where trust is built and, increasingly, where that trust is tested by AI driven threats. Deloitte estimates that AI-enabled fraud losses will reach $40 billion in the US by 2027, underscoring how quickly risks are emerging.

In a webinar with Ben Colman (CEO & Co-founder, Reality Defender) and Pat Carroll (CEO & Co-founder, ValidSoft), we explored why voice is one of the most at-risk channels in customer interactions, and why organisations now need stronger ways to verify both who is calling and whether the caller is even human in the first place. Real-time deepfake detection and modern voice identity are becoming essential parts of that answer.

Why Voice Still Matters in a Digital-First World

Voice is “the oldest form of communication” and still the most intuitive way to interact with organizations. Even with chatbots, apps, and self-service portals, customers still rely on a phone call when they need clarity, urgency, or reassurance. It’s personal, real-time, and it works for every demographic.

For a long time, that made voice one of the most trusted channels. And until recently, hearing someone speak was enough for us to believe the person was real. But that assumption no longer holds.

Generative AI now makes it easy to clone a person’s voice, reproduce tone and emotion, and even run full conversations using agentic AI systems. The channel that once felt the most human is suddenly one of the easiest to exploit, not because people stopped trusting voice, but because attackers can now imitate it convincingly. That’s why voice is under such pressure today.

As Ben noted during our discussion:

“What’s new and unique here is that anybody… can make a near-perfect deepfake. They can make a near-perfect voice match to impersonate anyone using just a few seconds of audio.”

The New Threats Reshaping the Voice Channel

The shift we’re seeing in contact centers is already creating real risks for financial institutions and other high-trust organizations. A few issues stand out:

• Voice can be cloned instantly: A few seconds of audio are enough to create a convincing synthetic voice that can impersonate a customer, employee, or executive. Today, voice cloning requires just 3–5 seconds of audio and costs as little as $11 per month.
• AI systems can run entire calls: Agentic AI can place calls, respond naturally, and mimic real speech patterns at scale. High-volume synthetic calls are even being used to overwhelm contact center queues.
• Attackers can alter audio mid-call: We’re seeing cases where payment details or account information are altered mid-call, long after the caller has passed initial authentication.
• Humans can’t hear the difference: Modern synthetic voices sound real. Agents cannot rely on tone, cadence, or intuition to detect that something is wrong. Caller ID, ANI, device checks, and knowledge-based questions assumed that the voice on the line was real.

Why Traditional Systems Can’t Handle AI-Driven Voice Threats

Traditional contact center systems weren’t designed for this. Caller ID, ANI, device checks, knowledge-based questions, and even traditional voice biometrics were built for a world where the caller was assumed to be human. These tools can verify who a caller sounds like, but they can’t detect cloned voices, recognize when AI is driving the conversation, or flag real-time manipulation. As a result, the channel that once felt most trustworthy has become one of the easiest to exploit.

How Biometrics and Deepfake Detection Close the Gap Together

Identity Next: Matching the Verified Human Voice

Once the system knows the voice is authentic, biometrics can do what they do best: verify the identity behind it. Matching a verified human voice to an enrolled customer provides strong, reliable assurance that isn’t dependent on passwords, device checks, or knowledge-based questions that attackers can easily bypass.

A Continuous Layer of Trust Across the Interaction

Together, these capabilities create a continuous layer of trust throughout the entire interaction. Synthetic callers are screened out immediately, legitimate customers move through authentication without added friction, and mid-call manipulation attempts can be detected before they cause harm. Agents no longer have to rely on instinct to spot suspicious calls, and organizations gain visibility into risks that traditional tools simply can’t detect.

“With the rise of agentic AI, voice identity has to evolve. Detection and biometrics working together give you the resilience you need for what’s coming next.” — Pat Carroll

This combined approach also makes it safer to introduce more automation. When both authenticity and identity can be validated at machine speed, self-service and agentic workflows can operate with far less risk. And because both technologies evolve alongside new generative models and attack techniques, they provide a future-ready foundation for securing the voice channel as AI continues to advance.

Rebuilding Trust in the Voice Channel

Voice isn’t going away. If anything, it’s becoming more central as customers turn to the phone for reassurance, complex decisions, and urgent support. At the same time, attackers now have powerful tools that let them imitate real people, automate entire conversations, and manipulate audio in ways that humans can’t detect.

The challenge for organizations now is restoring confidence in a channel that was never built with these threats in mind. That means moving beyond the assumptions that a voice is genuine or that a familiar-sounding caller can be trusted.It requires verifying the authenticity of the audio itself and validating the identity behind it, continuously, across the entire interaction.

The good news is that the technology already exists. By pairing real-time deepfake detection with modern voice biometrics, organizations can safeguard high-value calls, enable safer automation, and stay ahead of rapidly evolving AI-driven attacks.