How to Detect AI-Generated and Automated Calls in Your Contact Center

AI-generated and automated calls are increasingly affecting contact centers as inexpensive synthetic speech, real-time voice agents, caller-ID spoofing, and rotating phone numbers enable large-scale campaigns, creating fraud risks that agents cannot reliably identify through listening alone. Potential warning signs include synthetic-audio artifacts, unnatural pauses or interruptions, mismatched responses, highly consistent response times, IVR probing, verification difficulties, caller-ID inconsistencies, and repeated voices or patterns across calls. Effective detection combines acoustic analysis with behavioral, telephony, voice-biometric, and cross-call clustering signals, since voice biometrics verifies a claimed identity but does not establish whether the audio is genuine. The recommended workflow applies risk scoring before calls are answered, during IVR navigation, in live agent conversations, and after calls for investigation, with responses ranging from logging low-risk calls to escalating or restricting high-risk activity. Organizations evaluating vendors should test real-time latency, error rates, codec compatibility, explanations for alerts, deployment and retention controls, and performance on actual telephone audio rather than relying solely on clean-audio benchmarks. A 30-day monitoring-first pilot can establish normal call patterns, validate labels and thresholds, introduce agent alerts, and assess whether detection improves fraud prevention without unnecessarily disrupting legitimate customers.

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