Increase the overall security level and prevent fraud, decrease risks of insider attacks, and implement more effective authentication systems for their customers and employees.
Neuro.net voice biometrics technology is based on Text Independent Engine with a typical error rate (EER) below 2%. The algorithm uses voiceprints to produce unique identification for every individual, using physical and behavioral factors. These include pronunciation, emphasis, speed of speech, accent, timbre.
The AI automatically analyzes the person's voice calling the contact center in less than 20 seconds and matches it with the pre-recorded voiceprint. While the person describes his or her problem, the system conducts automatic verification that reduces the time spent on this task by 66%.
For the contact center usage, the system can update the CRM's customer profile assigning the corresponding flags. For example, red is a recognized imposter, and green is for the verified customer. If there is not enough data for the system to decide on the caller's security level, it can run additional verification by asking more custom questions.
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