Abstract: A digital audio quality monitoring device uses a deep neural network (DNN) to provide accurate estimates of signal-to-noise ratio (SNR) from a limited set of features extracted from incoming audio. Some embodiments improve the SNR estimate accuracy by selecting a DNN model from a plurality of available models based on a codec used to compress/decompress the incoming audio. Each model has been trained on audio compressed/decompressed by a codec associated with the model, and the monitoring device selects the model associated with the codec used to compress/decompress the incoming audio. Other embodiments are also provided.

Abstract: A system and method may identify a fraud ring based on call or interaction data by analyzing by a computer processor interaction data including audio recordings to identify clusters of interactions which are suspected of involving fraud each cluster including the same speaker; analyzing by the computer processor the clusters, in combination with metadata associated with the interaction data, to identify fraud rings, each fraud ring describing a plurality of different speakers, each fraud ring defined by a set of speakers and a set of metadata corresponding to interactions including that speaker; and for each fraud ring, creating a relevance value defining the relative relevance of the fraud ring.

Abstract: A computer-implemented method for proactive fraudster exposure in a customer service center according to content analysis and voice biometrics analysis, is provided herein. The computer-implemented method includes: (i) performing a first type analysis to cluster the call interactions into ranked clusters and storing the ranked clusters in a clusters database; (ii) performing a second type analysis on a predefined amount of the highest ranked clusters, into ranked clusters and storing the ranked clusters; the first type analysis is a content analysis and the second type analysis is a voice biometrics analysis, or vice versa; (iii) retrieving from the ranked clusters, a list of fraudsters; and (iv) transmitting the list of potential fraudsters to an application to display to a user said list of potential fraudsters via a display unit.

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 computer-implemented method for proactive fraudster exposure in a customer service center according to content analysis and voice biometrics analysis, is provided herein. The computer-implemented method includes: (i) performing a first type analysis to cluster the call interactions into ranked clusters and storing the ranked clusters in a clusters database; (ii) performing a second type analysis on a predefined amount of the highest ranked clusters, into ranked clusters and storing the ranked clusters; the first type analysis is a content analysis and the second type analysis is a voice biometrics analysis, or vice versa; (iii) retrieving from the ranked clusters, a list of fraudsters; and (iv) transmitting the list of potential fraudsters to an application to display to a user said list of potential fraudsters via a display unit.

Abstract: A computer-implemented method for proactive fraudster exposure in a customer service center according to content analysis and voice biometrics analysis, is provided herein. The computer-implemented method includes: (i) performing a first type analysis to cluster the call interactions into ranked clusters and storing the ranked clusters in a clusters database; (ii) performing a second type analysis on a predefined amount of the highest ranked clusters, into ranked clusters and storing the ranked clusters; the first type analysis is a content analysis and the second type analysis is a voice biometrics analysis, or vice versa; (iii) retrieving from the ranked clusters, a list of fraudsters; and (iv) transmitting the list of potential fraudsters to an application to display to a user said list of potential fraudsters via a display unit.

Abstract: In a system and method for audio analysis in a cloud-based computerized an authentication (RTA) manager micro-service may send an audio packet to a voice processor micro-service. The voice processor may extract features of the audio. The RTA manager may obtain the extracted features from the voice processor; calculate, based on the extracted features, a quality grade of the audio packet, and send the extracted features to an at least one voice biometrics engine if the quality grade is above a threshold. Each of the at least one voice biometrics engines may be configured to generate a voiceprint of the audio packet, based on the extracted features of the audio packet and to perform at least one of: authenticate a speaker, detect fraudsters, and enrich a previously stored voiceprint of the speaker with the voiceprint of the audio packet.

Abstract: A system and method may identify a fraud ring based on call or interaction data by analyzing by a computer processor interaction data including audio recordings to identify clusters of interactions which are suspected of involving fraud each cluster including the same speaker; analyzing by the computer processor the clusters, in combination with metadata associated with the interaction data, to identify fraud rings, each fraud ring describing a plurality of different speakers, each fraud ring defined by a set of speakers and a set of metadata corresponding to interactions including that speaker; and for each fraud ring, creating a relevance value defining the relative relevance of the fraud ring.

Abstract: In a system and method for audio analysis in a cloud-based computerized an authentication (RTA) manager micro-service may send an audio packet to a voice processor micro-service. The voice processor may extract features of the audio. The RTA manager may obtain the extracted features from the voice processor; calculate, based on the extracted features, a quality grade of the audio packet, and send the extracted features to an at least one voice biometrics engine if the quality grade is above a threshold. Each of the at least one voice biometrics engines may be configured to generate a voiceprint of the audio packet, based on the extracted features of the audio packet and to perform at least one of: authenticate a speaker, detect fraudsters, and enrich a previously stored voiceprint of the speaker with the voiceprint of the audio packet.

Abstract: A computer-implemented method for proactive fraudster exposure in a customer service center according to content analysis and voice biometrics analysis, is provided herein. The computer-implemented method includes: (i) performing a first type analysis to cluster the call interactions into ranked clusters and storing the ranked clusters in a clusters database; (ii) performing a second type analysis on a predefined amount of the highest ranked clusters, into ranked clusters and storing the ranked clusters; the first type analysis is a content analysis and the second type analysis is a voice biometrics analysis, or vice versa; (iii) retrieving from the ranked clusters, a list of fraudsters; and (iv) transmitting the list of potential fraudsters to an application to display to a user said list of potential fraudsters via a display unit.

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 computerize method for voice authentication of a customer in a self-service system is provided. A request for authentication of the customer is received and the customer is enrolled in the self-service system with a text-independent voice print. A passphrase from a plurality of passphrases to transmit to the customer is determined based on comparing each of the plurality of passphrases to a text-dependent or text-independent voice biometric model. The passphrase is transmitted to the customer, and when the customer responds, an audio stream of the passphrase is received. The customer is authenticated by comparing the audio stream of the passphrase against the text-independent voice print.

Abstract: A computer-implemented method for proactive fraudster exposure in a customer service center according to content analysis and voice biometrics analysis, is provided herein. The computer-implemented method includes: (i) collecting call interaction; (ii) storing the collected call interactions; (iii) performing a first type analysis to cluster the call interactions into ranked clusters and storing the ranked clusters in a clusters database; (iv) performing a second type analysis on a predefined amount of the highest ranked clusters, into ranked clusters and storing the ranked clusters; the first type analysis is a content analysis and the second type analysis is a voice biometrics analysis, or vice versa; (v) retrieving from the ranked clusters, a list of fraudsters; and (vi) transmitting the list of potential fraudsters to an application to display to a user said list of potential fraudsters via a display unit.

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 computerize method for voice authentication of a customer in a self-service system is provided. A request for authentication of the customer is received and the customer is enrolled in the self-service system with a text-independent voice print. A passphrase from a plurality of passphrases to transmit to the customer is determined based on comparing each of the plurality of passphrases to a text-dependent or text-independent voice biometric model. The passphrase is transmitted to the customer, and when the customer responds, an audio stream of the passphrase is received. The customer is authenticated by comparing the audio stream of the passphrase against the text-independent voice print. If the customer is authenticated, then storing the audio stream of the passphrase and the topic of the passphrase.

Abstract: Methods and systems for transforming a text-independent enrollment of a customer in a self-service system into a text-dependent enrollment are provided. A request for authentication of a customer that is enrolled in the self-service system with a text-independent voice print is received. A request is transmitted to the customer to repeat a passphrase and the customer's response is received as an audio stream of the passphrase. The customer is authenticated by comparing the audio stream of the passphrase against the text-independent voice print and if the customer is authenticated then a text-dependent voice print is created based on the passphrase, otherwise discard the audio stream of the passphrase.

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 according to content analysis and voice biometrics analysis, is provided herein. The computer-implemented method includes: (i) collecting call interaction; (ii) storing the collected call interactions; (iii) performing a first type analysis to cluster the call interactions into ranked clusters and storing the ranked clusters in a clusters database; (iv) performing a second type analysis on a predefined amount of the highest ranked clusters, into ranked clusters and storing the ranked clusters; the first type analysis is a content analysis and the second type analysis is a voice biometrics analysis, or vice versa; (v) retrieving from the ranked clusters, a list of fraudsters; and (vi) transmitting the list of potential fraudsters to an application to display to a user said list of potential fraudsters via a display unit.