Abstract: Aspects of the disclosure relate to using machine-learning models to authenticate users and protect enterprise-managed information and resources. In some embodiments, a computing platform may receive user interaction data from enterprise computing infrastructure and may train one or more authentication models based on this data. Subsequently, the computing platform may receive, from a first application server, a request to authenticate a first user to a first user account in a first usage session hosted by the first application server. In response to receiving this request, the computing platform may identify whether session-specific interaction data for the first usage session is valid based on the one or more authentication models. If the interaction data is identified as being valid, the computing platform may generate and send one or more commands directing the first application server to allow the first user to access the first user account in the first usage session.

Abstract: Aspects of the disclosure relate to using secure authentication tokens to grant power of attorney. A computing platform may receive user input indicating a task and requesting that a power of attorney be granted to an individual. The computing platform may generate an authority token granting the individual the power of attorney to perform the task, and may send the authority token, along with task information indicating the task, to the individual. The computing platform may receive a validation request including the authority token and the task information. Based on successful validation, the computing platform may send validation information to an event processing system. After receiving confirmation that the task has been performed by the individual, the computing platform may update dashboard information to indicate that the task has been completed, which may cause the user device to display a dashboard indicating completion of the task.

Abstract: Aspects of the disclosure relate to using secure authentication tokens to grant power of attorney. A computing platform may receive user input indicating a task and requesting that a power of attorney be granted to an individual. The computing platform may generate an authority token granting the individual the power of attorney to perform the task, and may send the authority token, along with task information indicating the task, to the individual. The computing platform may receive a validation request including the authority token and the task information. Based on successful validation, the computing platform may send validation information to an event processing system. After receiving confirmation that the task has been performed by the individual, the computing platform may update dashboard information to indicate that the task has been completed, which may cause the user device to display a dashboard indicating completion of the task.

Abstract: Aspects of the disclosure relate to using machine learning to modify account privacy settings. A computing platform may identify initial account settings for an individual of a plurality of individuals. Using a settings optimization model, the computing platform may identify account data and third party data for the individual. Using the settings optimization model, the computing platform may identify discrepancies between the initial account settings for the first individual and account settings for a subset of the plurality of individuals having common characteristics with the individual. Based on the discrepancies, the computing platform may identify settings modifications for the individual, and may determine that a modification of the settings modifications applies to a detected interaction of the individual. The computing platform may direct an enterprise data source to perform the modification, which may cause the enterprise data source to modify the initial account settings based on the modification.

Abstract: Arrangements for controlling data relations and data redundancies are presented. In some aspects, data may be received from a plurality of sources. The data may then be analyzed to determine a score or value associated with the data. Machine learning may be used to analyze the data and/or determine the score associated with the data. In some examples, a type of data may be identified. Based on the type of data, and the data, one or more connections to other data or types of data may be identified. In some examples, the data, as well as any identified connections, any determined score, and the like may be stored in a data container associated with the identified type of data. Stored data may be analyzed to determine whether one or more redundancies exist. If one or more redundancies are identified, one or more data redundancy actions may be identified and executed.

Abstract: Aspects of the disclosure relate to an information reduction platform. The information reduction platform may generate data silos within a distributed ledger system. The information reduction platform may receive a data use case request from a client device corresponding to a first user. The information reduction platform may identify relevant data silos corresponding to the data use case request. The information reduction platform may direct the distributed ledger system to grant the client device access to the relevant data silos. The information reduction platform may monitor the efficiency of the relevant data silos. The information reduction platform may generate data velocity values (DVVs) for each relevant data silo. The information reduction platform may train a machine learning model to select a subset of relevant data silos for a particular data use case. The information reduction platform may create an iterative feedback loop to update the first machine learning model.

Abstract: Arrangements for forecasting data surges are presented. In some aspects, data may be received from, for instance, a computing system internal to an enterprise organization. In some examples, contextual data may be received. The contextual data may be analyzed, with the received data, to identify a score for the received data. Machine learning may be used to identify or determine the score. In some examples, additional data may be received via a plurality of data streams. The additional data may be analyzed to identify topics or trends in data. The topics or trends may be used to identify potential data surges. Machine learning may be used to analyze the data and forecast a potential data surge. In response to forecasting a potential data surge, one or more computing and/or data storage resources may be identified, configured, and deployed to accommodate the forecast potential data surge.

Abstract: Aspects of the disclosure relate to using machine learning to modify account privacy settings. A computing platform may identify initial account settings for an individual of a plurality of individuals. Using a settings optimization model, the computing platform may identify account data and third party data for the individual. Using the settings optimization model, the computing platform may identify discrepancies between the initial account settings for the first individual and account settings for a subset of the plurality of individuals having common characteristics with the individual. Based on the discrepancies, the computing platform may identify settings modifications for the individual, and may determine that a modification of the settings modifications applies to a detected interaction of the individual. The computing platform may direct an enterprise data source to perform the modification, which may cause the enterprise data source to modify the initial account settings based on the modification.

Abstract: Aspects of the disclosure relate to logical map generation and injection. A computing platform may train a key value engine to assign logical map data priority values based on data use cases. The computing platform may receive first data and a first use case, which the computing platform may input into the key value engine to produce the logical map data priority values for the first data. The computing platform may generate, based on the logical map data priority values, a logical map for the first data. The computing platform may modify the first data by embedding, into a header of the first data, the logical map. The computing platform may send, to a recipient computing device, a portion of the modified first data and one or more commands to re-assemble the first data using the logical map stored in the portion of the modified first data.

Abstract: Aspects of the disclosure relate to using secure authentication tokens to grant power of attorney. A computing platform may receive user input indicating a task and requesting that a power of attorney be granted to an individual. The computing platform may generate an authority token granting the individual the power of attorney to perform the task, and may send the authority token, along with task information indicating the task, to the individual. The computing platform may receive a validation request including the authority token and the task information. Based on successful validation, the computing platform may send validation information to an event processing system. After receiving confirmation that the task has been performed by the individual, the computing platform may update dashboard information to indicate that the task has been completed, which may cause the user device to display a dashboard indicating completion of the task.

Abstract: Apparatus and methods are provided for executing a rules-based authentication of an electronic transaction. The method may include receiving first and second payment authorization parameters and second payment authorization parameters. The method may also include receiving from a Point of Sale device a request for payment information and a purchase amount. The method may also include, determining if first or second parameters are satisfied. The first parameters may include a first time range, a pairing of the electronic device to a first apparatus, a first geolocation of the electronic device, and a first maximum transaction value. The second parameters may include a second time range, a pairing of the electronic device to a second apparatus, a second geolocation of the electronic device, and a second maximum transaction value, wherein the second time range does not include the first time range.

Abstract: Aspects of the disclosure relate to using secure authentication tokens to grant power of attorney. A computing platform may receive user input indicating a task and requesting that a power of attorney be granted to an individual. The computing platform may generate an authority token granting the individual the power of attorney to perform the task, and may send the authority token, along with task information indicating the task, to the individual. The computing platform may receive a validation request including the authority token and the task information. Based on successful validation, the computing platform may send validation information to an event processing system. After receiving confirmation that the task has been performed by the individual, the computing platform may update dashboard information to indicate that the task has been completed, which may cause the user device to display a dashboard indicating completion of the task.

Abstract: A method for identifying a traveler at a kiosk for international travel is provided. The method may include reading, at a biometric reader included in the kiosk, a biometric identifier of a traveler. The method may include generating a digital rendering of the biometric identifier. The method may include receiving, at an electronic input receiver, a PIN number from the traveler. The method may include transmitting the digital rendering and the PIN to a central server. The method may include validating the digital rendering and the PIN at the central server. The method may include retrieving information associated with the traveler at the central server. The information may include look and feel preferences. The method may include transmitting the retrieved information, including the look and feel preferences, to the kiosk. The method may include displaying the look and feel preferences on a display included in the kiosk.

Abstract: Apparatus and methods are provided for executing a rules-based authentication of an electronic transaction. The method may include receiving first and second payment authorization parameters and second payment authorization parameters. The method may also include receiving from a Point of Sale device a request for payment information and a purchase amount. The method may also include, determining if first or second parameters are satisfied. The first parameters may include a first time range, a pairing of the electronic device to a first apparatus, a first geolocation of the electronic device, and a first maximum transaction value. The second parameters may include a second time range, a pairing of the electronic device to a second apparatus, a second geolocation of the electronic device, and a second maximum transaction value, wherein the second time range does not include the first time range.

Abstract: A blockchain-based travel security system is provided. The system increases travel security. The system includes an electronically stored user's itinerary file with a user's pre-registered travel itinerary. The itinerary file can be stored on a blockchain. The system also includes an electronically stored user's medical history file stored on the blockchain. A single trusted information source provides the itinerary file and the medical history file to the blockchain. The system includes a dual track information storage and retrieval system which is configured to store the itinerary file and to store the recent medical history file. The dual track information storage and retrieval system comprises a first track related to the user's recent medical history file and a second track related to the itinerary file.

Abstract: Aspects of the disclosure relate to using machine learning to modify account privacy settings. A computing platform may identify initial account settings for an individual of a plurality of individuals. Using a settings optimization model, the computing platform may identify account data and third party data for the individual. Using the settings optimization model, the computing platform may identify discrepancies between the initial account settings for the first individual and account settings for a subset of the plurality of individuals having common characteristics with the individual. Based on the discrepancies, the computing platform may identify settings modifications for the individual, and may determine that a modification of the settings modifications applies to a detected interaction of the individual. The computing platform may direct an enterprise data source to perform the modification, which may cause the enterprise data source to modify the initial account settings based on the modification.

Abstract: Systems for dynamically controlling authentication data are presented. In some examples, registration data may be received. The registration data may include user data, contact information, and authentication data which may include deoxyribonucleic acid (DNA) data of a user. In some arrangements, a request to process an event may be received. The request may include event details which may be used to determine or identify an authentication tier of the event. Based on the identified authentication tier, one or more authentication factors for request may be dynamically identified. The request for authentication factors may be transmitted to a user device and authentication response data may be received. The authentication response data may be compared to the authentication data received at registration (or otherwise pre-stored) to determine whether a match exists. If the data matches, the event may be authorized for processing. If not, the event process request may be denied.

Abstract: Aspects of the disclosure relate to using machine-learning models to authenticate users and protect enterprise-managed information and resources. In some embodiments, a computing platform may receive user interaction data from enterprise computing infrastructure and may train one or more authentication models based on this data. Subsequently, the computing platform may receive, from a first application server, a request to authenticate a first user to a first user account in a first usage session hosted by the first application server. In response to receiving this request, the computing platform may identify whether session-specific interaction data for the first usage session is valid based on the one or more authentication models. If the interaction data is identified as being valid, the computing platform may generate and send one or more commands directing the first application server to allow the first user to access the first user account in the first usage session.

Abstract: Aspects of the disclosure relate to using secure authentication tokens to grant power of attorney. A computing platform may receive user input indicating a task and requesting that a power of attorney be granted to an individual. The computing platform may generate an authority token granting the individual the power of attorney to perform the task, and may send the authority token, along with task information indicating the task, to the individual. The computing platform may receive a validation request including the authority token and the task information. Based on successful validation, the computing platform may send validation information to an event processing system. After receiving confirmation that the task has been performed by the individual, the computing platform may update dashboard information to indicate that the task has been completed, which may cause the user device to display a dashboard indicating completion of the task.

Abstract: Apparatus and methods are provided for executing a rules-based authentication of an electronic transaction. The method may include receiving first and second payment authorization parameters and second payment authorization parameters. The method may also include receiving from a Point of Sale device a request for payment information and a purchase amount. The method may also include, determining if first or second parameters are satisfied. The first parameters may include a first time range, a pairing of the electronic device to a first apparatus, a first geolocation of the electronic device, and a first maximum transaction value. The second parameters may include a second time range, a pairing of the electronic device to a second apparatus, a second geolocation of the electronic device, and a second maximum transaction value, wherein the second time range does not include the first time range.