Abstract: A system and method may adjust the threshold or other settings in a biometric comparison system, or provide a report or display of parameters. Over a series of comparisons of authentication biometric samples (e.g. authentication VPs) to enrollment biometric samples (e.g. enrollment VPs), the authentication samples may be stored, and scores resulting from the biometric comparisons may be stored in a first set of scores. A second set of biometric comparisons may be created, each using a pairing of a stored authentication biometric sample and an enrollment biometric sample, each biometric comparison resulting in a score, the scores forming a second set of scores. The first and second sets of scores may be combined to produce a third set of scores, and an iterative process may be performed over the third set of scores to update the parameters of the Gaussian distribution of the third set of scores.

Abstract: A computer-implemented method for analyzing call interactions in an interactions database by a Proactive Fraud Exposure (PFE) engine is provided herein. The computer-implemented method may generate a voiceprint for each call interaction; (ii) use a machine learning technique to group the call interactions into one or more clusters based on respective voiceprints in the voiceprints database; (iii) store the one or more clusters; and (iv) rank and classifying the one or more clusters to yield a list of potential fraudsters. The computer-implemented method may further transmit the list of potential fraudsters to a user to enable the user to review said list of potential fraudsters and to add fraudsters from the list to a watchlist database.

Abstract: Systems and methods for generating and implementing a real-time multi-factor authentication policy across multiple channels, are configured to: during a pre-authentication stage: receive, via a user interface, information defining one or more scenarios; receive, via the user interface, information defining one or more authentication flows; for each of the one or more scenarios, map one of the one or more authentication flows to a given scenario; and generate a multi-factor authentication policy associated with each of the one or more scenarios; and during a real-time authentication stage: upon receiving an interaction, identify, by a decision engine, a relevant scenario of the one or more scenarios; implement, by the decision engine, the multi-factor authentication policy associated with the relevant scenario; and determine, by the decision engine, an authentication result.

Abstract: Systems for and methods of training a spoofing detection model include receiving a plurality of customer call interactions; classifying each of the plurality of customer call interactions as a spoofed call or a non-spoofed call using a spoofing detection model; generating a voiceprint for each of the plurality of customer call interactions; comparing the generated voiceprints; grouping 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.

Abstract: Systems and methods for generating and implementing a real-time multi-factor authentication policy across multiple channels, are configured to: during a pre-authentication stage: receive, via a user interface, information defining one or more scenarios; receive, via the user interface, information defining one or more authentication flows; for each of the one or more scenarios, map one of the one or more authentication flows to a given scenario; and generate a multi-factor authentication policy associated with each of the one or more scenarios; and during a real-time authentication stage: upon receiving an interaction, identify, by a decision engine, a relevant scenario of the one or more scenarios; implement, by the decision engine, the multi-factor authentication policy associated with the relevant scenario; and determine, by the decision engine, an authentication result.

Abstract: Systems for and methods of training a spoofing detection model include receiving a plurality of customer call interactions; classifying each of the plurality of customer call interactions as a spoofed call or a non-spoofed call using a spoofing detection model; generating a voiceprint for each of the plurality of customer call interactions; comparing the generated voiceprints; grouping 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.

Abstract: Systems and methods for generating and implementing a real-time multi-factor authentication policy across multiple channels, are configured to: during a pre-authentication stage: receive, via a user interface, information defining one or more scenarios; receive, via the user interface, information defining one or more authentication flows; for each of the one or more scenarios, map one of the one or more authentication flows to a given scenario; and generate a multi-factor authentication policy associated with each of the one or more scenarios; and during a real-time authentication stage: upon receiving an interaction, identify, by a decision engine, a relevant scenario of the one or more scenarios; implement, by the decision engine, the multi-factor authentication policy associated with the relevant scenario; and determine, by the decision engine, an authentication result.

Abstract: A system and method may adjust the threshold or other settings in a biometric comparison system, or provide a report or display of parameters. Over a series of comparisons of authentication biometric samples (e.g. authentication VPs) to enrollment biometric samples (e.g. enrollment VPs), the authentication samples may be stored, and scores resulting from the biometric comparisons may be stored in a first set of scores. A second set of biometric comparisons may be created, each using a pairing of a stored authentication biometric sample and an enrollment biometric sample, each biometric comparison resulting in a score, the scores forming a second set of scores. The first and second sets of scores may be combined to produce a third set of scores, and an iterative process may be performed over the third set of scores to update the parameters of the Gaussian distribution of the third set of scores.

Abstract: A computer-implemented method for analyzing call interactions in an interactions database by a Proactive Fraud Exposure (PFE) engine is provided herein. The computer-implemented method may generate a voiceprint for each call interaction; (ii) use a machine learning technique to group the call interactions into one or more clusters based on respective voiceprints in the voiceprints database; (iii) store the one or more clusters; and (iv) rank and classifying the one or more clusters to yield a list of potential fraudsters. The computer-implemented method may further transmit the list of potential fraudsters to a user to enable the user to review said list of potential fraudsters and to add fraudsters from the list to a watchlist database.

Abstract: A computer-implemented method for proactive fraudster exposure in a customer service center having multiple service channels is provided herein. The computer-implemented method may collect call interactions based on predefined rules by a calls collection engine and store the collected call interactions. The computer-implemented method may further analyze the call interactions by a Proactive Fraud Exposure engine by: (i) generating a voiceprint for each call interaction; (ii) using machine learning technique to group the call interactions into one or more clusters based on respective voiceprints in the voiceprints database; (iii) storing the one or more clusters; and (iv) ranking and classifying the one or more clusters to yield a list of potential fraudsters. The computer-implemented method may further transmit the list of potential fraudsters to a user to enable the user to review said list of potential fraudsters and to add fraudsters from the list to a watchlist database.

Abstract: A computer-implemented method for proactive fraudster exposure in a customer service center having multiple service channels is provided herein. The computer-implemented method may collect call interactions based on predefined rules by a calls collection engine and store the collected call interactions. The computer-implemented method may further analyze the call interactions by a Proactive Fraud Exposure engine by: (i) generating a voiceprint for each call interaction; (ii) using machine learning technique to group the call interactions into one or more clusters based on respective voiceprints in the voiceprints database; (iii) storing the one or more clusters; and (iv) ranking and classifying the one or more clusters to yield a list of potential fraudsters. The computer-implemented method may further transmit the list of potential fraudsters to a user to enable the user to review said list of potential fraudsters and to add fraudsters from the list to a watchlist database.

Abstract: Systems and methods for generating and implementing a real-time multi-factor authentication policy across multiple channels, are configured to: during a pre-authentication stage: receive, via a user interface, information defining one or more scenarios; receive, via the user interface, information defining one or more authentication flows; for each of the one or more scenarios, map one of the one or more authentication flows to a given scenario; and generate a multi-factor authentication policy associated with each of the one or more scenarios; and during a real-time authentication stage: upon receiving an interaction, identify, by a decision engine, a relevant scenario of the one or more scenarios; implement, by the decision engine, the multi-factor authentication policy associated with the relevant scenario; and determine, by the decision engine, an authentication result.