Bank of America Corporation

Morgan Stewart Allen

Stephen Thomas Shannon

Siten Sanghvi

Manu Jacob Kurian

Pratap Dande

Matthew E Carroll

Gilbert M Gatchalian

Tamara S Kingston

Farrukh Zaidi

Husam Abu-Zaydeh

Robert Bosi

Brian Edward Doyle

George Albero

Naga Vamsi Krishna Akkapeddi

William August Stahlhut

Sridhar M Seetharaman

Sunil R Bangad

Jason A Alizzi

Benjamin N Wu

Maharaj Mukherjee

Kevin A Delson

Lalit Dhawan

James Heddleson

Ashish Arora

Christopher Lamont Rice

Susan J Moss

Kamesh R Gottumukkala

Melissa Ortiz-Munoz

ChellaPrabhu Andrew Mariadoss

Elizabeth Votaw

Alicia C Jones-McFadden

Eric Dryer

Linda Haddad

Willard H Waldron, Iii

Christine D Black

Jason Daniel Latta

Thomas A Sodano

Swetha Ravi

Rohit Sawkar

Michael Edwin Feeny

Arrangements for secure user authentication through hardware analysis and monitoring are provided. In some aspects, a computing platform may receive, from one or more hardware devices, user interaction data. The computing platform may establish a baseline indicating an expected usage pattern of the user. The computing platform may receive, from the one or more hardware devices, subsequent user interaction data and determine whether the subsequent data is unauthorized. Based on the subsequent data not being unauthorized, the computing platform may calculate an updated baseline in real-time as the subsequent data is received. The computing platform may monitor for additional user interaction data from the one or more hardware devices and compare the additional user interaction data with the updated baseline. In response to the additional user interaction data deviating from the updated baseline by a predetermined threshold, the computing platform may identify an anomaly and transmit a security notification.

Secure user authentication through hardware analysis and monitoring

Systems and methods for multi-dimensional voice-based digital authentication are provided. Methods include receiving, at a central server from a remote user device, a request to access a protected digital resource; displaying a prompt, at the device, requesting a spoken password expression; capturing, at the device, an expression spoken in response to the prompt; determining that the expression satisfies predetermined attributes of the password expression; calculating a voice pattern representation of the expression; determining that the voice pattern representation of the expression satisfies a threshold similarity to a voice pattern representation of the user that is stored in a database at the central server; and, in response to the expression satisfying the predetermined attributes of the password expression and satisfying the threshold similarity to the voice pattern representation of the user that is stored in the database at the central server, authorizing the device to access the protected digital resource.

Multi-dimensional voice-based digital authentication

Aspects of the disclosure relate to detecting usage issues on enterprise systems and dynamically providing user assistance. In some embodiments, a computing platform may receive, from an automated teller system, user presence information indicating that a user has been detected at an automated teller machine associated with the automated teller system. Subsequently, the computing platform may identify, based on the user presence information received from the automated teller system, that the user has a need for assistance with the automated teller machine associated with the automated teller system. In response to identifying that the user has the need for assistance, the computing platform may generate and send one or more commands directing the automated teller machine associated with the automated teller system to execute one or more automated assistance actions corresponding to the need for assistance.

Detecting usage issues on enterprise systems and dynamically providing user assistance

Arrangements for providing multi-party exchange functions are provided. In some aspects, a request for exchange may be received by a computing platform. The request for exchange may include identification of parties involved in the exchange, identification of goods, services, property, or the like, involved in the exchange, and the like. In some examples, the computing platform may determine a value of property, goods, or services associated with the exchange. The computing platform may request additional exchange data from one or more other parties. For instance, data associated with the exchange and another party to the exchange may be requested and received. In some examples, unique exchange identifiers may be generated linking each party to the exchange to the goods, services or property being exchange, a value, or the like. An indication of acceptance may be received and one or more exchange processing functions may be executed.

Multi-party exchange platform

Aspects of the disclosure relate to machine learning based automated management of a user account. A computing platform may determine, via a computing device and based on historical user activity of a user, a pattern of the user activity. Subsequently, the computing platform may detect a deviation from the pattern of the user activity. Then, the computing platform may identify an anticipated transaction of the user. Then, the computing platform may retrieve, from a repository of user data, one or more user-defined preference rules associated with the anticipated transaction. Then, the computing platform may determine whether the one or more preference rules apply to one or more attributes of the anticipated transaction. Subsequently, the computing platform may trigger, based on a determination that the one or more preference rules apply to the one or more attributes of the anticipated transaction, an action associated with the anticipated transaction.

Machine learning based automated management of customer accounts

A bot automatically compiles transaction information for a consumer from merchants that sell products in various categories and merchants that sell products in only a single category. The bot categorizes the consumer's purchases from the merchants and determines percentages of consumer spending in each category. The bot may obtain a category for purchases from merchants selling a single category of items based on a merchant identifier such as a merchant code. For other merchants, the bot may obtain category information from the merchant or from an analysis of a category code, or a product or service description or identifier. The categories and percentages of the consumer's purchases may be provided by the bot to the consumer or to a third party. The bot may provide special offers and promotions to a consumer based on the compiled category information and percentage spending by the consumer by category.

Automated debundling and categorization of purchases

A computer system for optimizing an application for monitoring operation of a network is provided. The application may utilize Artificial Intelligence (AI). The computer system may be configured to detect a network transaction volume level that exceeds a first threshold level. In response to a detection of the network transaction volume level that exceeds a first threshold level, the system may utilize the AI to determine whether to implement one or more over-threshold-level mitigation techniques. When the AI instructs the processor to implement the one or more over-threshold-level mitigation techniques, the system may query the AI for an instruction directing an implementation of the over-threshold-level mitigation techniques.

Pro-active digital watch center

A system includes first and second subsystems. The first subsystem receives a validation number request, transmitted by a first device in response to the entry of an account number into a first field of a webpage. In response to receiving the request, the first subsystem randomly generates the validation number, stores a copy in memory, and transmits it to a second device. The second subsystem receives a transaction request that includes the validation number from the first device, and transmits the received number to the first subsystem. The first device transmitted the request in response to the second device receiving the validation number and its subsequent entry into a second field of the webpage displayed on the first device. In response to receiving the validation number, the first subsystem determines that it matches the stored copy and transmits a message to the second subsystem authorizing the transaction.

Identity verification through multisystem cooperation

Aspects of the disclosure relate to automated pairing of customers and businesses. A computing platform may determine, based on historical user activity of a user, a pattern of the user activity, and may identify, based on the pattern of the user activity, an anticipated purchase activity of the user. Then, the computing platform may determine a sales offering by a vendor. Then, the computing platform may match the anticipated purchase activity with the sales offering. Then, the computing platform may retrieve user-defined preference rules associated with the anticipated purchase activity. Then, the computing platform may determine whether the preference rules apply to one or more attributes of the anticipated purchase activity. Subsequently, the computing platform may trigger, based on a determination that the preference rules apply to the one or more attributes of the anticipated purchase activity, an action associated with the anticipated purchase activity.

Machine learning based automated pairing of individual customers and small businesses

A method is provided to reduce the number of duplicates of each document that is stored within entity databases. The method may include creating discrete links and/or pointers to the location of the document already stored within an entity. The method may also include separating the document into different classification levels. The method may include storing the different parts of the documents in different locations within the entity.

Edge computing data reproduction and filtering gatekeeper

This application describes apparatus and methods for using edge-computing to control resource distribution among access channels, such as a retail banking center. Edge-nodes may be configured to move a product display in response to detected or expected customer traffic flow in or near a retail location. Edge-nodes may be configured to redirect resources provided by a cloud computing environment to or away from the retail location. Based on customer traffic flow, edge-nodes may direct customers/resources to a retail location and ensure the retail location provides a predetermined quality of service.

Edge-node controlled resource distribution

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Charlotte, NC, United States of America

Morgan Stewart Allen