How real-time voice deepfake detection works in call centers

Published August 13, 2024 Updated October 20, 2026 

Real-time voice deepfake detection analyzes a caller’s speech while the call is in progress and returns a score for how likely it is that the voice is AI-generated. It runs alongside the call path, so the IVR, agent or fraud team can verify, route or escalate before an account change goes through.

The caller clears every security question, sounds exactly like the account holder and asks the agent to add a new payee. Nothing in the call flow flags it, because the voice was AI-generated from recordings of the real customer. Contact centers built their defenses on the idea that a familiar voice proves who’s calling. Identity is now synthetic, and every check that relied on the voice needs a second input.

Why voice checks no longer prove identity

Most contact center authentication rests on three checks. The caller knows the answers, calls from a recognized number and sounds like the customer. AI-generated voices defeat all three at once. Knowledge answers come from breached data, caller ID can be spoofed and a voice clone reproduces the sound a biometric system enrolled.

The FBI has warned that criminals use AI-generated audio to impersonate people and gain access to their bank accounts. In a 2025 Gartner survey of 302 cybersecurity leaders, 43% reported at least one deepfake audio call incident. Deloitte projects that generative AI could push U.S. fraud losses to $40 billion by 2027. Reality Defender’s analysis of the real cost of voice fraud in banks breaks down where those losses land.

Voice biometrics still has a job. It confirms that a voice matches the customer on file. It wasn’t built to confirm that the voice came from a person. Detection adds that second question without replacing the first, which is how deepfake detection and voice biometrics work together. Attackers often pair a cloned voice with social engineering, a pattern covered in how deepfakes amplify vishing in the financial sector.

Where detection sits in the call flow

Detection works on the audio itself, so it needs the call while it’s live. Most deployments receive a copy of the audio stream from the contact center platform or the session border controller. The call keeps moving on its normal path while the detection model listens in parallel.

That parallel position protects the caller experience. Nobody waits on hold for a verdict, and the IVR and agent desktop keep working as configured. The score arrives within seconds of the caller speaking, early enough to inform authentication or the first sensitive request.

Reality Defender connects to the platforms contact centers already run, including NICE, Five9 and Twilio, through stack-native integration. Customers don’t enroll anything, so detection covers first-time callers and callers who never set up voice authentication.

What the detection score tells your team

Detection returns a probability-based risk score rather than a yes-or-no verdict. A high score means the speech shows signs of AI generation. A low score means it doesn’t.

A score is an input, not a decision. Your fraud rules set the thresholds, and those thresholds decide which calls get extra verification, which go to an analyst and which continue untouched. Good thresholds depend on your call mix. A setting that suits a wire transfer line can add needless friction on a balance inquiry line.

The same signal also picks up a different kind of caller. Agentic AI callers are AI-generated by design and often act for legitimate customers. Contact centers treat them as a routing question rather than a fraud question, covered in detecting AI callers in contact centers.

How fraud teams act on a flagged call

Most contact centers use three responses, matched to the request and the score.

• Verify the caller through a method that doesn’t rely on voice, such as a one-time passcode or a callback to the number on file.
• Move the call to a specialist queue where agents trained on social engineering handle the request.
• Flag the call for fraud review so investigators see the score, the recording and the account history together.

The agent sees the alert on the desktop while the call is still live. That timing changes the conversation. An agent who knows the voice may be synthetic can slow down, decline a sensitive change or move the caller to a stronger check without accusing anyone.

What a production deployment looks like

A tier-one global bank ran Reality Defender across its U.S. and Latin American contact center operations in 2024. The deployment analyzed more than 1.7 million customer calls and identified about 960 synthetic voice calls, with alerts reaching about 600 frontline representatives (case study).

The bank started with a review of recorded calls to set a baseline. That review found AI-generated audio its existing systems hadn’t flagged. The production rollout followed, connected to the bank’s call platforms without backend changes, and the fraud team built a new triage and escalation process around the score.

The sequence works as a template. Measure what’s already reaching your agents, then decide where the signal should change the workflow. For the wider shift in how banks defend the voice channel, see how voice AI detection is reshaping financial security.

Voice was never meant to carry the full weight of identity. Detection gives the contact center a second signal at the moment it matters, before a payee is added or a password is reset. Detection is a continuous discipline, and the contact centers that treat it that way keep each decision with the people who can see the risk.