AI FOR THE IDENTIFICATION OF PARTICIPANTS

• Authentication: AI-driven facial recognition systems can authenticate participants by comparing their live facial images with stored ones, ensuring only authorized individuals have access.
• Enrollment: During the initial registration, AI can capture and store participants' facial data for future verification.

• Secure Access: AI can analyze voice patterns to authenticate participants over the phone or in virtual meetings.
• Fraud Detection: By identifying inconsistencies in voice patterns, AI can flag potential fraud or impersonation attempts.

• Continuous Authentication: AI can monitor participants' behavior such as typing patterns, mouse movements, and interaction habits to continuously verify their identity during sessions.
• Anomaly Detection: Behavioral analytics can detect unusual behavior that might indicate a security breach or unauthorized access.

• Identity Proofing: AI can automate the verification of identification documents (e.g., passports, driver’s licenses) by extracting and cross-referencing information with databases.
• Anti-Fraud Measures: Advanced AI algorithms can detect forged or altered documents with high accuracy.

• Enhanced Security: AI can facilitate MFA by integrating biometric data (facial, fingerprint, voice) with traditional methods (passwords, OTPs) for robust participant verification.
• Adaptive Authentication: AI can adjust the level of authentication required based on the risk profile of the participant’s activity.

• Pattern Recognition: Machine learning can identify patterns in data that help distinguish between legitimate participants and potential threats.
• Predictive Analytics: AI can predict potential security breaches by analyzing historical data and current trends, thereby preemptively tightening identification protocols.

• Chatbot Verification: AI chatbots equipped with NLP can interact with participants to verify their identity through conversational cues and cross-referencing with stored data.
• Sentiment Analysis: Analyzing the sentiment and context of communications to identify suspicious or unusual interactions.