Bank of America Corporation

Albena Noveva Fairchild

Manu Jacob Kurian

Elena Kvochko

Crystal M Sundaramoorthy

Brandon Sloane

Siten Sanghvi

Kevin A Delson

Dipika Jain

Jinna Zevulun Kim

Jayachandra Varma

Monika Kapur

Ganesh Bonda

John Ryan Bowling

Dimitra J Tsagris

Gopikrishna Nemalikanti

Aeric John Solow

Antoan Izmirliev

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.

Predictive value engine for logical map generation and injection

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.

Settings optimization engine using artificial intelligence to enhance client privacy

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.

Dynamic information reduction using a velocity based machine learning model

Systems, computer program products, and methods are described herein for self-interrogating data and correcting data storage implementations in an electronic network. The present disclosure is configured to identify at least one data storage component, wherein the at least one data storage component comprises data; determine at least one current requirement of the data; collect at least one current variable of the data; and determine whether the at least one current variable of the data matches the at least one current requirement of the data, wherein, in an instance where the at least one current variable of the data matches the at least one current requirement, store the data in the at least one data storage component, or wherein, in an instance where the at least one current variable of the data does not match the current requirement, generate at least one change variable for the data.

Systems, methods, and apparatuses for self-interrogating data and correcting data storage implementations in an electronic network

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.

Multi-computer system for controlling data relation and redundancy

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.

Coordination platform for generating and managing authority tokens

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.

Using machine-learning models to authenticate users and protect enterprise-managed information and resources

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.

Trusted pair authentication with edge-computing devices

The system provides the ability for primary users (e.g., parents, or the like) to assign actions to dependent users (e.g., kids, or the like) through the use of devices located at the location of the dependent users (e.g., home, or the like). The actions may be tasks for the dependent users to complete using devices (e.g., appliances or other devices). Moreover, the primary users can monitor if the dependent users are undertaking the assigned actions using the devices, and in response, may control the use of other devices at the same location (e.g., entertainment devices) until the dependent users undertake the actions (e.g., start, complete, etc. the actions). Assignment of the actions and control of the devices may be made automatically in order to aid in achieving the one or more desired actions. Notification of the actions and/or instructions on how to perform the actions may pushed to the devices.

Device control based on action completion

Aspects of the present disclosure are directed to electronic computer implemented methods of data communication. The method includes receiving a plurality of virtual nodes with EDI data payload including a node attribute, a GPS location attribute and a biometric ID attribute and at least one data element associated with the GPS location attribute. In further aspect, the method includes electronically via a data communications network, processing the EDI data payloads including the node attribute, the GPS location attribute and the biometric ID attribute and the at least one data element associated with the GPS location attribute. Then, electronically processing the EDI data payloads in a network with machine learning and providing an electronic message responsive thereto. In yet a further aspect, the data communications method includes a step of processing the smart data set in the network with machine learning and providing an electronic message responsive thereto.

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Indian Trail, NC, United States of America

Albena Noveva Fairchild