Abstract: Systems and methods of context-aware caller identification via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: obtaining a trained call verification machine learning model that determines when a phone number is associated with a service provider that is associated with a delivery portion of a user activity with an entity within a time window; receiving, from a computing device of a first user, an indication of a communication being received from a second user; receiving activity information of a particular activity associated with the first user and a particular entity; utilizing the trained call verification machine learning model to determine, based at least in part on the activity information and the communication, that the second user is the particular service provider that is associated with a delivery portion of the particular activity associated with the particular entity.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from a user to monitor a plurality of activities; receiving monitoring data of the plurality of activities for a first period of time; verifying at least one common session parameter to identify an incoming interaction sessions; utilizing a software application to access an interaction session database; identifying an interruption associated with the software application for a second period of time; utilizing a machine learning algorithm to verify the interruption; determining a plurality of authentication rules; adjusting the plurality of authentication rules, receiving a response to an interactive communication; and updating the database of known session interaction parameters.

Abstract: Systems and methods of video password based user authentication via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: receiving a request to register a video password from a first user; establishing at least one user-specific authentication criterion for the first user based on first video password data; receiving a login attempt from a second computing device associated with a second user who submits to be the first user, the login attempt comprising second video password data; utilizing a trained video password authentication machine learning model to determine a first comparison result and a second comparison result based on the first and second video password data, and accepting or rejecting the login attempt based on at least one of the first comparison result and the second comparison result.

Abstract: Systems and methods of context-aware caller identification via machine learning techniques are disclosed.

Abstract: Systems and methods of context-aware caller identification via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: obtaining a trained call verification machine learning model that determines when a phone number is associated with a service provider that is associated with a delivery portion of a user activity with an entity within a time window; receiving, from a computing device of a first user, an indication of a communication being received from a second user; receiving activity information of a particular activity associated with the first user and a particular entity; utilizing the trained call verification machine learning model to determine, based at least in part on the activity information and the communication, that the second user is the particular service provider that is associated with a delivery portion of the particular activity associated with the particular entity.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from a user to monitor a plurality of activities; receiving monitoring data of the plurality of activities for a first period of time; verifying at least one common session parameter to identify an incoming interaction sessions; utilizing a software application to access an interaction session database; identifying an interruption associated with the software application for a second period of time; utilizing a machine learning algorithm to verify the interruption; determining a plurality of authentication rules; adjusting the plurality of authentication rules, receiving a response to an interactive communication; and updating the database of known session interaction parameters.

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: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining, a permission from each user to monitor a plurality of activities; continually receiving monitoring data of the plurality of activities; identifying a plurality of related incoming interaction sessions within the predetermined period of time; automatically verifying at least one common session parameter; utilizing a machine learning algorithm to determine at least one transaction; determining a plurality of fraudulent transactional rules; automatically adjusting the plurality of fraudulent transactional rules; utilizing a natural language processing algorithm to automatically generate at least one interaction notification; receiving a response to the interactive notification; and automatically updating the database of known session interaction parameters based on the response to the interactive notification.

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 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; verifying at least one session interaction parameter associated with the incoming interaction session to identify the incoming interaction session as a repeat interaction session; dynamically retrieving at least one relation-specific notation from a plurality of relation-specific notations to display; instructing an input GUI element to display input data associated with the repeat interaction; automatically updating the plurality of relation-specific notations associated with the historical data relationship; and instructing at least one programmable output GUI to display a notif

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from a user to monitor a plurality of activities; receiving monitoring data of the plurality of activities for a first period of time; verifying at least one common session parameter to identify an incoming interaction sessions; utilizing a software application to access an interaction session database; identifying an interruption associated with the software application for a second period of time; utilizing a machine learning algorithm to verify the interruption; determining a plurality of authentication rules; adjusting the plurality of authentication rules, receiving a response to an interactive communication; and updating the database of known session interaction parameters.

Abstract: Systems and methods of provisioning post-interaction values are disclosed.

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 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; verifying at least one session interaction parameter associated with the incoming interaction session to identify the incoming interaction session as a repeat interaction session; dynamically retrieving at least one relation-specific notation from a plurality of relation-specific notations to display; instructing an input GUI element to display input data associated with the repeat interaction; automatically updating the plurality of relation-specific notations associated with the historical data relationship; and instructing at least one programmable output GUI to display a notif

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: Systems and methods of caller identification differentiation via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may include: receiving a permission indicator identifying a permission by the user to detect calls being received by a computing device; receiving an indication of a current call being received; utilizing a trained call differentiation machine learning model to determine a likelihood that the current call is of a first call type or a second call type, where the first call type is associated with a first type of activity and the second call type is associated with a second type of activity.

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 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: The present embodiments include a system and method for provisioning, storing, and deriving a cryptocurrency wallet. The system includes a card, a user device, an administrator processor, and a server. The method includes generating a private key and a public key over a user datum, encrypting the keys over a key-encryption-key (KEK), and transmitting the keys to a card. The keys can be optionally stored on a server in encrypted form. Alternatively, the keys can be derived from the user datum if the user has lost them.