Streaming A.I. voice vishing
With the recent $25 million damage from the deepfake conference call, it's clear that banks must prioritize investing in real-time voice vishing defence using streaming A.I. This threat is worsened by sophisticated voice cloning technology, like that from ElevenLabs, recently backed by a $80 million Series B investment. These attacks exploit call center vulnerabilities through social engineering and voice impersonation, demanding tech-driven solutions for early detection.
Voice Vishing Detection A.I. should encapsulate the unique features of a speaker's voice, such as pitch, tone, and tempo. The process begins with feature extraction from audio clips, where characteristics like Mel-frequency cepstral coefficients (MFCCs) are derived. These features serve as inputs to train Voice Vishing Detection A.I. to capture their distinct voiceprint effectively.
With limited voice data for each speaker, banks can leverage a robust foundation using the Universal Background Model, a model trained on a broad range of voice data from numerous speakers which represents the general traits of human speech, with Maximum A Posteriori (MAP) to hone in on the unique attributes of each speaker's voice create speaker-specific models.
Voice Vishing Detection AI, designed for real-time call center audio like Genesys, demands a unique streaming AI architecture with continuous learning to adapt to voiceprint variations due to aging and seasons. Like our human intelligence, the streaming data platform can function as the central nervous system, connecting all sensory inputs of the diverse data sources to the neurons of the AI models, ensuring the AI instantly processes data, assesses, and responds to live threats, effectively identifying and addressing vishing activities on the spot.
The streaming data platform is crucial in enhancing the AI's ability to identify sophisticated vishing tactics by integrating live voice streams and historical context. It ensures robust data governance and a comprehensive data catalogue, which are essential for AI's effective decision-making and learning capabilities. This infrastructure supports the AI's continuous adaptation and learning from voice and conversational patterns, crucial for pinpointing nuanced vishing activities.
Moreover, the platform's ability to analyze conversational elements like tone and pace in real time significantly boosts the AI's vishing detection accuracy, making it a key player in fraud prevention. It underscores the platform's role as the backbone of the AI system, enabling it to detect and adapt to threats efficiently, ensuring its ongoing effectiveness and reliability in a fast-evolving technological environment.