Chipmunk or Pepe? Using Acoustical Analysis to Detect Voice-Channel Fraud at Scale

As organizations are adding security layers to online interactions attackers are targeting the voice-channel to take over an account. That has resulted in a 210% increase in voice fraud since 2013. This talk will outline the voice fraud landscape including the profile of the attackers, the tools they use including ANI spoofing (Pepe), credential stuffing (Mr. Roboto) and voice distortion (Chipmunk). It will then show how to detect these using a variety of acoustical analysis techniques including features that exist in the non-voiced audio (e.g. spectral characteristics), voiced audio (e.g. speaker recognition) and call signaling (e.g. ANI velocity). We will also look at the architecture and algorithm modifications required to do audio feature extraction and machine learning at scale that currently handles over 5 million calls minutes every single day. Finally, we will talk about the open challenges that still exist in identifying these attacks.