Real-time video analytics engine that detects deepfakes, analyzes sentiment, recognizes behavioral patterns, and scores fraud risk -- flagging suspicious sessions before they are completed.
BASEKYC's video analysis module runs continuously during every live KYC session, processing the video stream frame by frame through multiple AI models simultaneously. It is not just watching -- it is understanding. The system detects synthetic media (deepfakes), reads customer sentiment and emotional state, identifies behavioral anomalies, and computes a real-time fraud probability score.
When something triggers a threshold -- an unnatural facial texture suggesting a deepfake, elevated stress indicators during identity questions, or a behavioral pattern matching known fraud profiles -- the system alerts the agent immediately. Suspicious sessions can be paused, escalated, or flagged for review before any verification decision is finalized.
Advanced neural networks analyze facial texture, micro-movements, lighting inconsistencies, and compression artifacts to detect GAN-generated faces, face swaps, and video replay attacks. The model is continuously updated against emerging deepfake techniques to stay ahead of evolving threats.
Track customer emotional state throughout the verification session. The AI reads facial expressions, voice tone, and speech patterns to detect stress, confusion, nervousness, or signs of coercion. Unusual emotional shifts during critical verification moments are flagged for agent attention.
Aggregate multiple risk signals -- deepfake probability, sentiment anomalies, device fingerprint, geolocation mismatch, and session behavior -- into a single composite fraud score. Configure thresholds that trigger automatic escalation, additional verification steps, or session termination when risk exceeds acceptable levels.
The moment a video session begins, multiple AI models attach to the video and audio streams. Frame-by-frame analysis runs in parallel -- deepfake detection, facial expression tracking, voice analysis, and behavioral monitoring -- all without adding any latency to the live call.
Each model generates continuous risk signals that feed into the composite fraud scoring engine. The system correlates signals across models -- for example, a deepfake detection spike combined with unusual emotional patterns raises the composite score faster than either signal alone.
Based on the composite score and your configured thresholds, the system either gives the agent a green light to proceed, displays warning indicators for heightened attention, or triggers an automatic escalation. Post-session, a detailed analysis report with timestamped annotations is generated for compliance records.
As RBI and SEBI increasingly acknowledge synthetic media threats to video-based verification, BASEKYC's deepfake detection provides a proactive defense layer. The system flags GAN-generated faces, face-swap attacks, and video replay attempts in real time, generating evidence logs that demonstrate your organization's due diligence against evolving digital fraud techniques mandated under PMLA guidelines.
Every AI analysis decision is recorded with full traceability -- model version, input frame reference, confidence score, risk classification, and the action taken. These structured audit logs satisfy RBI's technology risk management guidelines and support compliance reviews by SEBI KRA requirements for registered intermediaries conducting digital onboarding.
Fraud detection events automatically generate Suspicious Transaction Reports (STRs) formatted for FIU-IND submission as required under PMLA. The system captures all relevant evidence -- session recording, AI analysis report, fraud score timeline, and agent notes -- in a structured package ready for regulatory filing and law enforcement cooperation.
Continuous monitoring of AI model accuracy, false positive rates, and detection coverage ensures the system performs within acceptable thresholds. Monthly model performance reports are generated automatically, supporting your organization's AI governance framework and satisfying RBI's expectations for ongoing technology risk assessment in digital KYC processes.
Processing Speed
Frame-by-Frame Analysis
Sentiment Scoring
Anomaly Detection
Deepfake Detection Rate
Uptime SLA
Alert Delivery
Data Encryption
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