Nice Ltd

Nice Systems Ltd.

Matan Keret

Roman Frenkel

Amnon Buzaglo

Amit Sharon

Peter S Cardillo

Itay Kalman Harel

Sigal Lev

Galya Julya Bar

Yaara Bar

Roman Shternharts

Itay Harel

Michal Daisey Momika

William Mark Finlay

Anat Malin

Omer Kochba

Natan Katz

Alon Menahem Shoa

Jeremy Hoyland

Levan Michaeli

Zvika Weingarten

Shunit Metz

Michal Daisey Lerer

Salil Dhawan

Rahul Vyas

Jon Augustus Arrowood

Efim Kolodizner

Tal Zur

Roni Krivoshey

Sergii Lebid

Olga Chukhriaieva

Zvika Horev

Or Gershon

Eran Moshe Talbi

Vadim Ilyasov

Yoav Bahat

Guy Earman

A computerized-method for training and applying a Machine Learning (ML) textual behavioral-identification-model to authenticate an agent, in a digital multi-channel environment, is provided herein. The computerized-method may include: (i) training a ML-textual-behavioral-identification-model using retrieved textual responses of each agent in one or more historical-interactions which were conducted in a controlled environment, as a training dataset. The ML-textual-behavioral-identification-model may be configured to process the retrieved textual responses of each agent to generate a profile-identity-data for each agent to be used to authenticate identity of the agent; (ii) receiving a textual-response of each agent, when the agent starts an interaction with a customer; (iii) applying the textual-response of the agent to the ML-textual-behavioral-identification-module to authenticate an identity of the agent in real-time by calculating an imposter-probability score for the agent and; (iv) sending the imposter-probability score to a file-management-system to take one or more actions when the imposter-probability score is above a preconfigured-threshold.

Computerized-method and computerized-system for training and applying a machine learning (ML) textual behavioral identification model to authenticate an agent, in a digital multi-channel environment

A system and method for real-time fraud detection with a social engineering phoneme (SEP) watchlist of phoneme sequences may perform real-time fraud prevention operations including receiving incoming call interactions and grouping the call interactions into one or more clusters, each cluster associated with a speaker's voice based on voiceprints. For a pair of voiceprints in a cluster, a phoneme sequence is extracted for each voice print. From the extracted phoneme sequences, a similarity score is then calculated to determine if a match exists between the extracted phoneme sequences based on a threshold. If determined a match exists, the phoneme sequence may be added to a SEP watchlist.

System and method for real-time fraud detection in voice biometric systems using phonemes in fraudster voice prints

A system is provided for real-time fraud detection with a fraudster voice print watchlist of repetitive fraudster phrases. The system includes a processor and a computer readable medium operably coupled thereto, to perform fraud prevention operations which include detecting a voice communication session having an audio signal of a user, accessing the fraudster voice print watchlist comprising a plurality of fraudster voice prints of the repetitive fraudster phrases, generating a voice print of the user using the audio signal, monitoring the user for real-time fraud detection using the fraudster voice print watchlist and the voice print of the user, and determining, based on the monitoring, whether the voice print of the user meets or exceeds a scoring threshold for matching with one or more of the plurality of fraudster voice prints from the fraudster voice print watchlist during the voice communication session.

Real-time fraud detection in voice biometric systems using repetitive phrases in fraudster voice prints

A computerized 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.

Authentication via a dynamic passphrase

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.

Computerized monitoring of digital audio signals

A data storage system configured to optimize selection of a plurality of data storage devices. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium including a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform storage device selection operations which include detecting and gathering storage device information for storing data recordings to the plurality of data storage devices, determining, by a storage load balancer, a plurality of storage efficiency scores for the plurality of data storage devices using a loss function and the gathered storage device information, generating a storage efficiency table, and assigning, by the storage load balancer, a first data recording to one of the plurality of data storage devices based on the storage efficiency table and an efficiency score threshold for the plurality of storage efficiency scores.

Automatic storage management in networked data storage systems using storage device monitoring

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.

System and method for updating biometric evaluation systems

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.

Method and system for proactive fraudster exposure in a customer service channel

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.

System and method for generating and implementing a real-time multi-factor authentication policy across multiple channels

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.

System and method for detecting fraud rings

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.

System and method for performing voice biometrics analysis

A method for separating and authenticating speech of a speaker on an audio stream of speakers over an audio channel may include receiving audio stream data of the audio stream with speech from a speaker to be authenticated speaking with a second speaker. A voiceprint may be generated for each data chunk in the audio stream data divided into a plurality of data chunks. The voiceprint for each data chunk may be assessed as to whether the voiceprint has speech belonging to the speaker to be authenticated or to the second speaker using representative voiceprints of both speakers. An accumulated voiceprint may be generated using the verified data chunks with speech of the speaker to be authenticated. The accumulated voiceprint may be compared to the reference voiceprint of the speaker to be authenticated for authenticating the speaker speaking with the second speaker over the audio channel.

Method and system for separating and authenticating speech of a speaker on an audio stream of speakers

A computerized 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 the audio stream of the passphrase and the topic of the passphrase may be stored.

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.

Method for training a spoofing detection model using biometric clustering

A system and method for authenticating a caller may include receiving an incoming call from the caller, determining a gender of the caller, and selecting, based on the determined gender, to search for the caller in one of: a watchlist of untrustworthy female callers, and a watchlist of untrustworthy male callers.

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Oulu, Finland

Matan Keret