Abstract: An authentication system identifies and mitigates the effectiveness of a Multi-factor Authentication (MFA) Fatigue attack. The attack may be detected based on the number of requests for authentication received for a user within a predetermined time window, or through user or administrator input. Once detected, the authentication system is placed in a safe mode to mitigate the MFA Fatigue attack. In safe mode, all pending requests for authentication are automatically rejected. Subsequent requests for mitigation may be automatically rejected or the authentication process may be modified to accurately differentiate authentication attempts from a legitimate user from authentication attempts that are part of the MFA Fatigue attack. To further mitigate the effectiveness of MFA Fatigue attacks, the authentication system may use simulated authentication attempts to identify and train users who are susceptible to MFA Fatigue attacks.

Abstract: A process of confirming an originator of an electronic mail (email) includes receiving a request for verification of the identity of an alleged originator of the email, which may be sent from the actual originator of the email or a recipient of the email. The alleged originator of the email may be determined, e.g., by parsing the email to determine the name of the sender in the email. Verification is obtained from a biometric-enabled client that is associated with the send whether the email was in fact sent by the sender as well as confirmation of the identity of the sender based on a previously stored identity-proofed biometric.

Abstract: A process of confirming an originator of an electronic mail (email) includes receiving a request for verification of the identity of an alleged originator of the email, which may be sent from the actual originator of the email or a recipient of the email. The alleged originator of the email may be determined, e.g., by parsing the email to determine the name of the sender in the email. Verification is obtained from a biometric-enabled client that is associated with the send whether the email was in fact sent by the sender as well as confirmation of the identity of the sender based on a previously stored identity-proofed biometric.

Abstract: A process of confirming an originator of an electronic mail (email) includes receiving a request for verification of the identity of an alleged originator of the email, which may be sent from the actual originator of the email or a recipient of the email. The alleged originator of the email may be determined, e.g., by parsing the email to determine the name of the sender in the email. Verification is obtained from an biometric-enabled client that is associated with the send whether the email was in fact sent by the sender as well as confirmation of the identity of the sender based on a previously stored identity-proofed biometric.

Abstract: An automated process is disclosed for improving the functionality of computer systems and electronic commerce in user identity-proofing. Steps include verifying that a user who is electronically seeking identity proofing is on an electronic directory of persons eligible for such identity proofing; creating an attest list for the user that includes associates who can vouch for his or her identity; collecting a video or other data from the user; sending the video or data to the associates and asking them for a confirmation or a disavowal of the identity of the user; deriving a biometric from the video or data upon receiving the confirmation; and saving the biometric as an identity-proofed biometric.

Abstract: An automated process is disclosed for improving the functionality of computer systems and electronic commerce in user identity-proofing. Steps include verifying that a user who is electronically seeking identity proofing is on an electronic directory of persons eligible for such identity proofing; creating an attest list for the user that includes associates who can vouch for his or her identity; collecting a video or other data from the user; sending the video or data to the associates and asking them for a confirmation or a disavowal of the identity of the user; deriving a biometric from the video or data upon receiving the confirmation; and saving the biometric as an identity-proofed biometric.

Abstract: An automated process is disclosed for improving the functionality of computer systems and electronic commerce in user identity-proofing. Steps include verifying that a user who is electronically seeking identity proofing is on an electronic directory of persons eligible for such identity proofing; creating an attest list for the user that includes associates who can vouch for his or her identity; collecting a video or other data from the user; sending the video or data to the associates and asking them for a confirmation or a disavowal of the identity of the user; deriving a biometric from the video or data upon receiving the confirmation; and saving the biometric as an identify-proofed biometric.

Abstract: An automated process is disclosed for improving the functionality of computer systems and electronic commerce in user identity-proofing. Steps include verifying that a user who is electronically seeking identity proofing is on an electronic directory of persons eligible for such identity proofing; creating an attest list for the user that includes associates who can vouch for his or her identity; collecting a video or other data from the user; sending the video or data to the associates and asking them for a confirmation or a disavowal of the identity of the user; deriving a biometric from the video or data upon receiving the confirmation; and saving the biometric as an identify-proofed biometric.

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: Enhanced diarization systems and methods of use are provided herein. Some exemplary methods may include applying one or more rules that affect separation of the call audio data into segments, the rules being associated with the at least one call schema, separating the call audio data into segments according to the one or more rules, grouping segments of call audio data associated with a speaker, and storing in a storage media an identifier and the grouped segments for the speaker.

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: 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: Systems, methods, and media for disambiguating call data are provided herein. Some exemplary methods include receiving notification of a fraud event including a customer account identifier and a fraud time stamp; determining a time frame that is proximate the fraud time stamp; collecting call events associated with the customer account identifier that occur during the determined time frame, each call event including a unique call event identifier, a voice sample, and a call event time stamp; identifying a first call event belonging to a first speaker and a second call event belonging to a second speaker; and generating a timeline presentation that includes the first call event and call event timestamp and an identification of a first voice sample as belonging to the first speaker, the second call event and call event timestamp and an identification of a second voice sample as belonging to the second speaker.

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: 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: 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: 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 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: 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: 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.