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: 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 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: A system and method for providing a temporary credit line increase to a customer awaiting or expecting a refund or replenishment of funds is disclosed. The process can be initiated by a creditor or at the request of a customer. In the latter scenario, the customer provides evidence of the expected refund in the form of a shipping label, return receipt, or other communication. In some cases, additional shipping information is acquired from a shipping company associated with the shipping label. Based on the information received from the customer as well as customer account information, a temporary credit line increase amount is calculated. The customer is then informed of the temporary credit line increase and any terminating conditions. The temporary credit line increase remains available to the customer until one of the terminating conditions has been satisfied.

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: 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 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: 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

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

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: 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: Disclosed embodiments pertain to systems and methods of facilitating product return. A product purchased by a user from a merchant can be determined from transaction data of the user. A return window for the product can be predicted from one or more of data from merchant websites, industry standards, or transaction data. Further, the likelihood that a product is a candidate for return can be predicted based on the transaction data. If the product is determined to be a candidate for return, the user can be notified of the return window for the product and a confidence score associated with the window. Refund timeliness can also be determined or inferred based on transaction data and provided to the user. Subsequently, transaction data can be monitored, and the user can be alerted if credit is not received after a predetermined time or the credit is less than expected.

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: 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: Disclosed herein are system, method, and computer program product embodiments for training and deploying a machine learning model to generate an assessment of risks and mitigations in response to a novel initiative request. After generating labeled data from a corpus of prior risk assessments, a machine learning model may be trained to programmatically generate a risk assessment in response to a novel initiative request. The system may provide for centralized control over the creation, review, and approval of initiative requests. The system may further analyze consumer-facing applications deployed by an organization and train the machine learning model to algorithmically determine consumer-facing applications potentially affected by an initiative request.

Abstract: Disclosed herein are system, method, and computer program product embodiments for training and deploying a machine learning model to generate an assessment of risks and mitigations in response to a novel initiative request. After generating labeled data from a corpus of prior risk assessments, a machine learning model may be trained to programmatically generate a risk assessment in response to a novel initiative request. A risk auditor may subsequently review the risk assessment generated using the machine learning model to provide various feedback reflecting the accuracy of the risks and mitigations. The machine learning model may then be retrained based on the feedback provided by the risk auditor to provide more accurate risks and mitigations in response to future initiatives.