Method for training a spoofing detection model using biometric clustering

A method of training a spoofing detection model, which comprises:

• receiving a plurality of customer call interactions;
• classifying, by a spoofing engine, each of the plurality of customer call interactions as a spoofed call or a non-spoofed call using a spoofing detection model;
• generating, by a proactive fraud exposure (PFE) engine, a voiceprint for each of the plurality of customer call interactions;
• comparing, by the PFE engine, the generated voiceprints;
• grouping, by the PFE engine, the generated voiceprints into one or more clusters based on the comparing, wherein each cluster represents a single speaker;
• locating a cluster containing a spoofed call and a non-spoofed call, thereby indicating that the non-spoofed call was misclassified by the spoofing detection model;
• and
• updating the spoofing detection model with the non-spoofed call.

A non-transitory computer-readable medium having stored thereon computer-readable instructions executable by a processor to perform operations which comprise:

• receiving a plurality of customer call interactions;
• classifying, by a spoofing engine, each of the plurality of customer call interactions as a spoofed call or a non-spoofed call using a spoofing detection model;
• generating, by a proactive fraud exposure (PFE) engine, a voiceprint for each of the plurality of customer call interactions;
• comparing, by the PFE engine, the generated voiceprints;
• grouping, by the PFE engine, the generated voiceprints into one or more clusters based on the comparing, wherein each cluster represents a single speaker;
• locating a cluster containing a spoofed call and a non-spoofed call, thereby indicating that the non-spoofed call was misclassified by the spoofing detection model;
• and
• updating the spoofing detection model with the non-spoofed call.