Abstract: In some implementations, a transaction card may include a card body having a first surface and a second surface opposite the first surface. The transaction card may include an integrated circuit (IC) chip embedded in the card body, and a plurality of first electrical contact sets electrically accessible from the first surface of the card body, where the plurality of first electrical contact sets have electrical connections to the IC chip. The transaction card may include a plurality of first magnetic stripe areas readable from the first surface of the card body. The transaction card may include a plurality of second electrical contact sets electrically accessible from the second surface of the card body, where the plurality of second electrical contact sets have electrical connections to the IC chip. The transaction card may include a plurality of second magnetic stripe areas readable from the second surface of the card body.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving input data from at least one external data aggregator; utilizing a trained machine learning algorithm to generate a database of known queries; receiving subsequent input data from the at least one external aggregator; automatically updating the database of known queries associated with the plurality of users; utilizing the trained machine learning algorithm to perform a cross-reference analysis to determine a presence of a data record within the database of known queries; dynamically removing the data record from the database of known queries; utilizing the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record; and instructing a computing device to initiate a verification of the presence of the at least one data record.

Abstract: The present disclosure provides an exemplary method, system, and computing device that may include the steps of receiving a first indication that information of a user has been detected at one or more dark web resources; classifying the item of the compromised information into an information type category; receiving a permission indicator to detect communications by the computing device; receiving a second indication of a communication; receiving a third indication that the user engages an interaction with the communication; instructing the computing device to execute a technique to obtain data for the communication; receiving the data for the communication; determining the communication is a spam communication; determining a current information type category being discussed during the spam communication; making a determination that the current information type category corresponds to the information type category; and instructing a graphical user interface to display an alert to the user.

Abstract: Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

Abstract: A computer-implemented method comprising: instructing a computing device to obtain a permission from the user to monitor a plurality of activities executed within the computing device; receiving a call session request from a computing device associated with a second user; utilizing a trained machine learning algorithm to confirm an identity of the user based on the call session interaction parameter to confirm the call interaction session as a verified call interaction session; generating a fictitious call session interaction parameter that differs from the at least one call session interaction parameter; modifying the call session request into a verified call session request by replacing the call session interaction parameter with the fictitious call session interaction parameter; utilizing a telecommunication network to transmit the verified call session request with the at least one fictitious session interaction parameter to the first calling-enabled computing device; automatically instructing the first c

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from the user to monitor a plurality of activities executed within the computing device; receiving monitoring data of the activities executed within the plurality of computing devices for a predetermined period of time; identifying incoming interaction sessions across the plurality of computing devices; verifying one common session parameter associated with the incoming interaction sessions to identify the incoming interaction sessions as suspect interaction sessions; determining a frequency metric for the suspect interaction sessions; determining a threshold value for the frequency metric; receiving new monitoring data; determining that the new incoming interaction session has at least one common session interaction parameter with the suspect interaction sessions; automatically generating an interaction notification for transmission to the computing device; receiving a response to the int

Abstract: In some implementations, a reliability modeler may receive a plurality of webpages associated with a first entity from an Internet scraping device. The reliability modeler may detect, within the plurality of webpages, at least one of a logo, a font, or a color. The reliability modeler may apply a model, trained on a set of guidelines associated with the first entity, to the logo, the font, or the color. Accordingly, the reliability modeler may determine, based on output from the model, that the plurality of webpages are unlikely to be authorized by the first entity. The reliability modeler may transmit, to a user device, an alert based on determining that the plurality of webpages are unlikely to be associated with the first entity.

Abstract: In some implementations, a system may receive, at a front-end device and from an augmented reality (AR) device associated with a user, a request to use the front-end device. The system may transmit, to the AR device, a request to access an optical sensor of the AR device. The system may receive, from the AR device, an output, from the optical sensor, that indicates a hand gesture performed by the user of the AR device. Accordingly, the system may determine, based on the hand gesture, a corresponding input key of the front-end device. The system may perform an action based on the corresponding input key.

Abstract: Systems and methods of location-aware caller identification via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may include: utilizing a trained call annotation machine learning model to determine one or both of an annotating condition and annotating location granularity, and associate one location of a user with one phone number of the user based at least on one or both of the annotating condition and the annotating location granularity; receiving second transactional information of one transaction associated with a first user; extracting second location information from the second transactional information of the one transaction; utilizing the trained call annotation machine learning model to automatically annotate one phone number record of one phone number of the first user, with the second location information at the annotating location granularity to form at least one user-specific location-specific annotated phone number record.

Abstract: Methods and systems are described herein for manipulating a communication acceptance screen, manipulating an interactive communication acceptance icon, and restricting access to accounts based on voice communication parameters. In particular, when a communication is detected from one device to another device, that communication may be risky. Thus, the risk is mitigated by giving a user information about the source of the communication to give a user a chance to reject the communication. In addition, in instances where the user accepts the communication, the system enables monitoring the communication and restricting any accounts that are disclosed within the communication.

Abstract: In some implementations, an augmented reality (AR) device may receive first images representing a webpage, an email, a product, or a store associated with a first entity. The AR device may detect, within the first images, a logo, a font, and/or a color. The AR device may apply a model, trained on a set of guidelines associated with the first entity, to the logo, the font, and/or the color. Accordingly, the AR device may receive, from the model, a first score associated with the webpage, the email, the product, or the store. The AR device may transmit an alert based on the first score. In some implementations, the AR device may further receive second images and apply the model to receive a second score associated with the webpage, the email, the product, or the store. Accordingly, the AR device may transmit an additional alert based on the second score.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving input data from at least one external data aggregator; utilizing a trained machine learning algorithm to generate a database of known queries; receiving subsequent input data from the at least one external aggregator; automatically updating the database of known queries associated with the plurality of users; utilizing the trained machine learning algorithm to perform a cross-reference analysis to determine a presence of a data record within the database of known queries; dynamically removing the data record from the database of known queries; utilizing the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record; and instructing a computing device to initiate a verification of the presence of the at least one data record.

Abstract: A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission to monitor a plurality of activities executed within the second computing device; continuously monitoring the plurality of activities executed within the second computing device for a predetermined period of time; receiving an indication of an incoming interaction session within the predetermined period of time; automatically verifying at least one session interaction parameter associated with the incoming interaction session to identify the incoming interaction session as a suspect interaction session; determining when a duration of time associated with the suspect interaction session meets or exceeds a predetermined duration threshold; utilizing a trained detection machine learning model to model a confidence value associated with the suspect interaction session; identifying at least one future activity subsequent to the suspect interaction session; and automatically instructing the firs

Abstract: Transaction cards having a security cover to selectively obscure transaction information and related methods are disclosed. A transaction card may include a card body having a first layer and a second layer secured to each other, and a cover portion pivotably movable relative to the second layer about a cover rotation axis coplanar with the first layer via a hinge portion, wherein, when in an obfuscation orientation, the cover portion is coplanar with the first layer such that one or more elements of transaction information is obfuscated when viewing the transaction card.

Abstract: Aspects discussed herein may relate to techniques for authenticating a user using transaction-based authentication questions. The transaction-based authentication questions may be provided to the user. The user may provide audible responses to the transaction-based authentication questions that may be captured by a smart device, such as an always on listening device. Authentication of the user may be based on voice analysis of the audible responses to the transaction-based authentication questions and/or based on the answers to the transaction-based authentication questions.

Abstract: In some embodiments, validation of network operations related to use of a token via a token request based on entity characteristics may be facilitated. In some embodiments, a token request indicating use of a token to access an item associated with an entity may be detected, where the token is bound to one or more entity characteristics. Based on the token request, an entity identifier associated with the entity may be obtained. Using the entity identifier, one or more item characteristics of items available via the entity may be determined. Determining whether the entity satisfies the one or more entity characteristic to which the token is bound may be determined based on the one or more item characteristics. Access to the item associated with the entity may be validated based on the determination indicating that the entity satisfies the one or more entity characteristics to which the token is bound.

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).

Abstract: In some implementations, a device may utilize a machine learning model to determine a set of candidate manufacturing entities associated with an exchange of an item. The device may cause a first automated action to be performed. The first automated action may include providing information identifying the set of candidate manufacturing entities to a device of a user that is associated with the exchange. The device may receive a response indicating the manufacturing entity associated with item. The device may identify a block in a blockchain associated with the manufacturing entity. The device may map the exchange information to the block to enable the item information associated with the item to be associated with the exchange.

Abstract: Methods, systems, and apparatuses are described herein for authenticating access to an account using questions which allow users to provide textual information about transactions conducted by an account. A machine learning model may be trained to predict the guessability of merchants. A request for access to an account may be received, and transaction data for that account may be received. An authentication question may be presented. For example, a user may be prompted to list places they have shopped recently. A user response comprising text data may be received. The text data may be processed to identify one or more merchants. If the one or more merchant(s) are represented in the transaction data, the machine learning model may be used to determine the guessability of the one or more merchants. An authentication score may be determined based on the guessability, and access to the account may be provided.