Abstract: Aspects described herein may allow for authenticating a user by generating a customized set of authentication questions based on patterns that are automatically detected and extracted from user data. The user data may include transaction data collected over a period of time. By automatically detecting user patterns that correspond to user behavior over a period of time, an authentication system may be able to generate information that is recognizable to an authentic user but difficult to guess or circumvent for any other user.

Abstract: In some implementations, a system may receive interaction data corresponding to an interaction associated with a particular user of a plurality of users. The system may determine, based on the interaction data, one or more interaction categories associated with the interaction. The system may store the interaction data as historical interaction data associated with historical interactions of the particular user. The system may determine, based on the historical interaction data, experience statuses associated with the particular user in at least a subset of the plurality of categories. The system may receive, from a user device of the particular user, review data indicating a review associated with a historical interaction of the particular user. The system may store the review as historical review data. The review may be associated with one or more experience statuses of the particular user in the one or more interaction categories associated with the historical interaction.

Abstract: Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.

Abstract: Provided are approaches for customizing transaction cards using a pressure sensitive modification area. In some approaches, a transaction card may include a body having a first main side opposite a second main side, wherein a chip is coupled to the first main side of the body, wherein a first outer layer extends along the first main side, and wherein a second outer layer extends along the second main side. The transaction card may further include an internal layer between the first and second outer layers, and a plurality of microspheres and a pigmented substance positioned between the internal layer and the first outer layer.

Abstract: Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.

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: A payment instrument comprising a bottom layer; one or more intermediate layers, wherein at least one of the one or more intermediate layers comprises: a chip comprising a processor and memory; an antenna coupled with the chip; an absorbent medium; and one or more reservoirs comprising beads, each of the beads comprising dyes of one or more colors, the beads configured to release the dyes onto the absorption medium. The dyes cure on the absorption medium at a rate based on a curing agent applied to the absorbent medium or included in beads of the reservoirs. The payment instrument may also comprise a top layer having at least a partially translucent portion above the absorption medium, wherein the absorption medium is at least partially visible through the at least one partially translucent portion.

Abstract: Methods, systems, and apparatuses are described herein for improving the security of personal information by preventing attempts at gleaning personal information from authentication questions. A computing device may receive a request for access to an account associated with a user. The request may comprise candidate authentication information. Based on comparing the candidate authentication information with the account data, the computing device may generate a synthetic authentication question. The synthetic authentication question may be generated as if the candidate authentication information is valid. A response to the synthetic authentication question may be received, and the request for access to the account may be denied.

Abstract: Methods, systems, and apparatuses are described herein for improving computer authentication processes by using physical environment information. A computing device may receive video depicting a physical environment and process that video to identify one or more first objects. The computing device may receive a request for access to an account. The computing device may send a prompt for a physical environment description and receive, in response, an indication of one or more second objects. The indication of the one or more second objects may comprise video of the physical environment, a verbal description of the physical environment, and/or text describing the physical environment. The computing device may grant the user device access to the account based on comparing the one or more first objects to the one or more second objects.

Abstract: Methods, systems, and apparatuses are described herein for improving the accuracy of synthetic authentication questions by analyzing third party account data. A request for access to a first account associated with a user may be received. The first account may be managed by a first organization. A transactions database might be queried for first account data. Second account data corresponding to a second account associated with the user might be received. That second account may be managed by a second organization different from the first organization. One or more second transactions, unique to the second account, may be identified. A synthetic transaction, configured to be different from transactions in the first account and the one or more second transactions, may be generated. An authentication question may be generated based on the synthetic transaction. Access to the first account might be provided based on a response to the authentication question.

Abstract: Aspects described herein may relate to techniques for detecting login activity to a financial account during a knowledge-based authentication process. The login activity may be related to access to an online interface for the financial account. The detection of login activity during the authentication process my indicate that the integrity of the authentication process is compromised as login access may provide an individual with transaction data that may be used to answer transaction-based authentication questions. As a result of detecting login activity, an alternative authentication process may be initiated or an authentication request related to the financial account may be denied.

Abstract: Methods, systems, and apparatuses are described herein for improving computer authentication processes using vocal confidence processing. A request for access to an account may be received. An authentication question may be provided to a user. Voice data indicating one or more vocal utterances by the user in response to the authentication question may be received. The voice data may be processed, and a first confidence score that indicates a degree of confidence of the user when answering the authentication question may be determined. An overall confidence score may be modified based on the first confidence score. Based on determining that the overall confidence score satisfies a threshold, data preventing the authentication question from being used in future authentication processes may be stored. The data may be removed when a time period expires.

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; continually monitoring the plurality of activities executed within the computing device for a predetermined period of time; identifying an indication of an incoming interaction session within the predetermined period of time; automatically verifying a session interaction parameter associated with the incoming interaction session; determining a plurality of inquires associated with the research interaction session based on the at least one session interaction parameter; identifying the plurality of demographic informational items associated with the user; and automatically updating the programmable GUI element to display the plurality of inquires associated with the interaction session and the plurality of demographic informational items associated with the user.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a trained spam upsurge detection machine learning model that determines when a current frequency associated with spam communications received by a current user exceeds a baseline frequency associated with the current user; receiving a permission indicator identifying a permission by the user to detect communications being received by the computing device; receiving an indication of at least one communication being received; determining the at least one communication as a particular spam communication; updating a frequency at which spam communications have been received by the user based at least in part on the particular spam communication; utilizing the trained spam upsurge detection machine learning model to determine that the frequency exceeds a baseline frequency associated with the user; and initiating a scan of one or more dark web resources.

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: 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: 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 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: 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