Solutions
Voice fraud detection and governance
Query-targeted resources for compliance, fraud, and technology teams evaluating deepfake audio detection, voice liveness, and explainable governance for regulated voice channels.
Deepfake audio detection for high-trust voice channels
Yes: Sonotheia detects synthetic and deepfake audio using physics-based spectral envelope trajectories and source excitation phase analysis. Unlike black-box score-only APIs, every verdict ships with an explainable decision trace and review-ready compliance artifacts.
Read solution →Voice liveness verification with explainable decision trace
Sonotheia provides voice liveness verification by analyzing physics-based signal metrics on live and recorded audio streams. Every liveness verdict includes sensor-level measurements, triggered rules, and a documented threshold history, not just a pass/fail score.
Read solution →Voice fraud governance for regulated financial services
Sonotheia is voice fraud governance infrastructure for financial services firms where a single voice-driven decision can move money or change account control. We pair spoof detection with decision trace, calibration history, and FINRA-aligned review artifacts for FinCEN SAR support and EU AI Act transparency expectations.
Read solution →Synthetic voice detection for banking and wire-fraud channels
Sonotheia detects AI-generated and converted voice in banking and FINRA-supervised workflows (wire authorizations, callback verification, and high-value instructions) using physics-based signal analysis validated on ASVspoof5 benchmarks. Every alert includes an explainable decision trace for fraud ops and financial services compliance review.
Read solution →Resources
How to evaluate voice deepfake detection vendors
Evaluate the best deepfake audio detection services on four dimensions: benchmark transparency (ASVspoof or equivalent), explainability under audit, deployment data boundaries, and governance artifact quality. Score-only APIs fail compliance review even when detection accuracy is acceptable.
Read resourceVoice fraud detection benchmark methodology and metrics
Physics-based anti-spoofing means measuring acoustic signal properties (spectral envelope trajectories and source excitation phase patterns) that synthetic pipelines struggle to reproduce consistently, then documenting those measurements for audit. Sonotheia reports Equal Error Rate (EER) and minimum Detection Cost Function (minDCF) on the ASVspoof5 evaluation partition (spoof attack types A17-A32).
Read resourceTelephony channel validation: codec survival for voice fraud controls
Voice fraud occurs over phone lines, not studio-grade microphones. Sonotheia validates acoustic signal survival through lossy telephony channels, specifically calibrating our physics-based sensors for wideband, G.711, and AMR-NB networks.
Read resourceVoice fraud SAR evidence guide: documenting synthetic media events
FINRA-supervised firms need auditable documentation when voice fraud involves deepfakes. The Sonotheia Voice Fraud SAR Evidence Guide helps compliance officers document synthetic voice events in Suspicious Activity Reports (SARs) with decision-trace evidence aligned to FinCEN and FINRA expectations.
Read resourceHow to prove a voice call was synthetic without storing biometrics
You do not need a voiceprint to show a call was likely synthetic. You need measurements of the audio, documented thresholds, and a record an examiner can reconstruct. Sonotheia analyzes acoustic behavior in memory and discards the audio after the run, creating no voiceprints and no biometric templates. The output is a forensic risk event with acoustic tags, reason codes, and a decision trace rather than a standalone confidence score.
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