User Scenario
Detecting and storing fraudulent attempts to access or open accounts (e.g., identity theft, fund theft).
Why
To stop fraudsters from opening multiple accounts or gaining access to steal funds or identities.
Target Sectors
Banking, financial services, eCommerce, government, super, insurance, education, real estate, and digital marketplaces—any industry with long-term customer relationships.
Key Customer Needs
• Trust: Strong verification during onboarding and for re-use.
• Accuracy: Ability to detect deepfakes or synthetic fraud to reduce risk.
• Seamless CX: Low-friction, legitimate user detection.
How Biometry Solves This
• Biometric Comparison: Detects if a biometric has been enrolled under a different identity, flagging potential fraud by comparing new attempts to stored templates.
• Fraud Signals: Gathers passive fraud signals (e.g., mobile device data, SIM swap details, geolocation) to detect anomalies.
• Continuous Learning: AI adapts to evolving fraud techniques, keeping your accounts secure.
Benefits:
• Multimodal Defence: Combines biometrics and fraud signals to make bypassing security more difficult.
• Fraud Risk Reduction: Detects impersonation fraud across any customer touchpoint.
• Accurate: Anchors the user’s voice to their face and their face to their identity, improving accuracy.
Steps to Detect Fraud
- Speech & Movement Detection: Biometry’s technology recognises both speech and movement to ensure authentic interactions. If a deepfake or account takeover fraud is detected, the system intervenes.
- Multiple Attempt Detection: If someone tries to enroll with a different identity using a previously stored biometric, Biometry flags the attempt as fraud and denies access.
- In both cases, Biometry stores the fraudulent biometric for future comparison, preventing the fraudster from re-enrolling.