Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Disclosed is a method for enrolling a voiceprint in a fraudster database, the method comprising: a) defining a fraud model comprising at least one hypothesis indicative of a fraudulent transaction; b) processing audio data based on the fraud model to identify at least one suspect voiceprint in the audio data suspected of belonging to a fraudster; and c) enrolling the at least one suspect voiceprint in the fraudster database.

Abstract: Disclosed is a method for enrolling a voiceprint in a fraudster database, the method comprising: a) defining a fraud model comprising at least one hypothesis indicative of a fraudulent transaction; b) processing audio data based on the fraud model to identify at least one suspect voiceprint in the audio data suspected of belonging to a fraudster; and c) enrolling the at least one suspect voiceprint in the fraudster database.

Abstract: In one embodiment, the invention provides a method, comprising: (a) maintaining a database of known fraudsters, wherein each fraudster has a voice signature and metadata associated therewith; (b) performing a screening operation to match a candidate with a fraudster in the database based on matching a voice signature and metadata for the candidate with a voice signature and metadata for the fraudster in the database.

Abstract: Systems, methods, and media for disambiguating call data are provided herein. Some exemplary methods include receiving, via a fraud notification system, notification of a fraud event associated with a customer account, the fraud event comprising a time stamp, determining, via a call selection module, unique voice samples or models from call events obtained within a time frame that is temporally proximate the fraud event, and generating a timeline presentation that includes each unique voice sample or model identified in the call events based upon a time stamp associated with the call events.

Abstract: Systems, methods, and media for determining fraud risk from audio signals and non-audio data are provided herein. Some exemplary methods include receiving an audio signal and an associated audio signal identifier, receiving a fraud event identifier associated with a fraud event, determining a speaker model based on the received audio signal, determining a channel model based on a path of the received audio signal, using a server system, updating a fraudster channel database to include the determined channel model based on a comparison of the audio signal identifier and the fraud event identified, and updating a fraudster voice database to include the determined speaker model based on a comparison of the audio signal identifier and the fraud event identifier.

Abstract: Systems, methods, and media for generating fused risk scores for determining fraud in call data are provided herein. Some exemplary methods include generating a fused risk score used to determine fraud from call data by generating a fused risk score for a leg of call data, via a fuser module of an analysis system, the fused risk score being generated by fusing together two or more uniquely calculated fraud risk scores, each of the uniquely calculated fraud risk scores being generated by a sub-module of the analysis system; and storing the fused risk score in a storage device that is communicatively couplable with the fuser module.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: In one embodiment, the invention provides a method, comprising: (a) maintaining a database of known fraudsters, wherein each fraudster has a voice signature and metadata associated therewith; (b) performing a screening operation to match a candidate with a fraudster in the database based on matching a voice signature and metadata for the candidate with a voice signature and metadata for the fraudster in the database.

Abstract: Disclosed is method for screening an audio for fraud detection, the method comprising: providing a User Interface (UI) control capable of: a) receiving an audio; b) comparing the audio with a list of fraud audios; c) assigning a risk score to the audio based on the comparison with a potentially matching fraud audio of the list of fraud audios; and d) displaying an audio interface on a display screen, wherein the audio interface is capable of playing the audio along with the potentially matching fraud audio, and wherein the display screen further displays metadata for each of the audio and the potentially matching fraud audio thereon, wherein the metadata includes location and incident data of each of the audio and the potentially matching fraud audio.

Abstract: In one embodiment, the invention provides a method, comprising: (a) maintaining a database of known fraudsters, wherein each fraudster has a voice signature and metadata associated therewith; (b) performing a screening operation to match a candidate with a fraudster in the database based on matching a voice signature and metadata for the candidate with a voice signature and metadata for the fraudster in the database.

Abstract: Disclosed is method for screening an audio for fraud detection, the method comprising: providing a User Interface (UI) control capable of: a) receiving an audio; b) comparing the audio with a list of fraud audios; c) assigning a risk score to the audio based on the comparison with a potentially matching fraud audio of the list of fraud audios; and d) displaying an audio interface on a display screen, wherein the audio interface is capable of playing the audio along with the potentially matching fraud audio, and wherein the display screen further displays metadata for each of the audio and the potentially matching fraud audio thereon, wherein the metadata includes location and incident data of each of the audio and the potentially matching fraud audio.

Abstract: Disclosed is a method for generating a fraud risk score representing a fraud risk associated with an individual, the method comprising: a) determining a telephony channel risk score from at least one of audio channel data and non-audio channel data of the individual; and b) generating the fraud risk score based on at least one of the telephony channel risk score, the audio channel data, and the non-audio channel data.

Abstract: Disclosed is a method for enrolling a voiceprint in a fraudster database, the method comprising: a) defining a fraud model comprising at least one hypothesis indicative of a fraudulent transaction; b) processing audio data based on the fraud model to identify at least one suspect voiceprint in the audio data suspected of belonging to a fraudster; and c) enrolling the at least one suspect voiceprint in the fraudster database.

Abstract: An automated method for determining user satisfaction with a call comprising (1) maintaining a count of the number of times a user was asked for input during the call (the “actual count”), (2) maintaining a count of the number of times a user should have been asked for input based on the dialog path the user traversed (the “ideal count”), and (3) associating no difference between the actual count and the ideal count with maximum user satisfaction and associating increasing differences between the counts with decreasing user satisfaction. In one embodiment, if the difference between the actual count and ideal count reaches a certain threshold, the call system changes the interaction with the user.

Abstract: A system, method and computer program product are provided for collecting information utilizing speech recognition. After receiving a specification, a voice application is configured based on the specification. During use, the voice application is capable of collecting information utilizing speech recognition.

Abstract: According to one aspect of the invention there is provided a method, comprising collecting voiceprints of callers; identifying which of the collected voiceprints are associated with fraud; and generating a whitelist comprising voiceprints corresponding to the collected voiceprints not identified as associated with fraud.

Abstract: In one embodiment the invention provides a method, comprising: (a) obtaining an audio recording corresponding to a legal proceeding of interest; (b) extracting from the audio recording audio belonging to of the defendant of the legal proceeding; and (c) enrolling the extracted audio into a voiceprint database.

Abstract: In one embodiment, the invention provides a method, comprising: (a) maintaining a database of known fraudsters, wherein each fraudster has a voice signature and metadata associated therewith; (b) performing a screening operation to match a candidate with a fraudster in the database based on matching a voice signature and metadata for the candidate with a voice signature and metadata for the fraudster in the database.

Abstract: According to one aspect of the invention there is provided a method, comprising (a) maintaining a collection of voice signatures, at least a subset of which is organized to form a list of voice signatures, each belonging to a disqualified candidate; (b) obtaining a voice sample for a candidate; (c) comparing the voice sample with the voice signatures in the list; and (d) if the voice sample matches a signature in the list, then returning a status of disqualified for the candidate. According to another aspect of the invention there is provided a method, comprising receiving a request form a merchant to perform a fraud detection operation in connection with a credit card transaction by a consumer; responsive to the request, collecting a voice sample from the consumer; comparing the collected voice sample with voice signatures of known fraudsters; and notifying the merchant of a result of the comparing.