Abstract: The disclosed computer-implemented method for utilizing user profile data to protect against phishing attacks may include (i) detecting a target user profile associated services accessed by a network-based application, (ii) determining identifiers associated with each of the services, (iii) extracting, for each of the identifiers, feature vectors describing exploitable screen elements in the network-based application associated with phishing attacks, (iv) updating, based on the feature vectors, previously extracted feature vectors in a data repository storing additional profiles for other users associated with the services, (v) predicting, utilizing a machine-learning model, phishing attack threats for target profile user based on a similarity with the additional profiles, and (vi) performing a security action that protects against the phishing attack threats. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Systems and methods for identifying accurate locations of in-person payment card transactions to detect location-based payment card anomalies. Some embodiments disclosed herein may enable identifying accurate locations of in-person payment card transactions to detect location-based payment card anomalies. In some embodiments, purchase data for a plurality financial transaction by a consumer that are performed in-person with a payment card may be received. The purchase data may identify merchant locations that are associated with each financial transaction. The merchant locations may be analyzed to determine whether they represent true physical locations of the financial transactions. Once a plurality of true physical locations has been identified, distances between them may be determined and a security action may be performed if the distances exceed a threshold.

Abstract: The disclosed computer-implemented method for detecting unauthorized online transactions may include correlating, by at least one processor, one or more reported financial activities to one or more online financial activities tracked in network telemetry on one or more authorized devices. The method may additionally include identifying, by the at least one processor based on the correlation, at least one of the reported financial activities that was initiated by an unauthorized device. The method may also include performing, by the at least one processor, a security action in response to the identification. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for detecting websites that perpetrate at least one of scams or frauds may include correlating online interaction data with financial transaction data. The online interaction data may include information on suspicious websites obtained through an online interaction analysis, and the financial transaction data may include sources of suspicious financial activity obtained through a transaction trend analysis. The method may additionally include detecting at least one of online scams or frauds based on the correlation. The detection may include detecting that an online interaction is suspicious based on correlation thereof to a suspicious financial transaction, and/or detecting that a financial transaction is suspicious based on correlation thereof to a suspicious online interaction. The method may also include performing a security action in response to the detection. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for detecting fraudulent shopping websites may include (i) identifying a shopping website that advertises a plurality of allegedly available payment options for completing transactions on the shopping website, (ii) determining, based at least in part on an analysis of the plurality of allegedly available payment options that at least one of the plurality of allegedly available payment options is suspicious, (iii) calculating a trustworthiness score for the shopping website that is based at least in part on the determination that at least one of the allegedly available payment options is suspicious, and (iv) displaying an alert to a user based on the trustworthiness score of the shopping website. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Protecting against an impersonation scam in a live video stream. In some embodiments, a method may include periodically extracting and storing signature features from verified video streams of verified streamers, identifying an unverified live video stream of an unverified streamer being viewed by one or more users, extracting and storing signature features from the unverified live video stream, computing overall distance scores between the signature features of the unverified live video stream and the signature features of the verified video streams, determining whether the unverified streamer is impersonating one or more of the verified streamers by determining whether one or more of the overall distance scores are less than a distance threshold, determining whether one or more text signature features of the unverified live video stream include an impersonation scam, and performing a remedial action to protect the one or more users from the impersonation scam.