Bank of America Corporation

Jesse Strickland Newsom, Iii

Kevin A Delson

Gilbert M Gatchalian

Patrick Nicholas Lawrence

Victoria Lynn Dravneek

Jinna Zevulun Kim

Jayachandra Varma

Hitesh Shah

John Christopher Checco

Yogi Ahuja

Elena Kvochko

William August Stahlhut

Jennifer T Renckert

Kevin Luong Tran

Kevin Tran

Nicolas Kabse

James D Ilmonen

Mayakoothan Krishnan

A computing system includes a first computing platform and a virtual exchange computing platform, where the first computing platform provides an application and a first electronic value token and the virtual exchange computing platform is communicatively coupled to the first computing platform. The virtual exchange computing platform receives the information corresponding to the first electronic value token and calculates, via API functions linking to banking as a service (BaaS) functionality processed by a valuation computing platform, a valuation of the first electronic value token. The virtual exchange computing platform causes presentation, via a network and on a first user device, a user interface screen presenting a visualization of a value of the electronic value token and comprising an input and triggers, based on a received trade input from the user interface, a virtual exchange of value involving the first electronic value token.

Banking as a service enabled virtual exchange computing platform

Various aspects of the disclosure relate to automated compliance verification systems for authenticating and verifying compliance associated with electronic transactions. A compliance verification platform may be an intermediary between an application for managing and/or recording transactions and a transaction processing platform for processing a transaction. Based on successful compliance verification and authentication, the compliance verification platform may send notifications to the transaction processing platform to process a transaction requested via the application.

Application programming interface (API)-enabled automated compliance verification and processing

An apparatus that matches one or more affiliates to a user includes a memory, a geofencing engine, an artificial intelligence engine, and processing circuitry communicatively coupled to each. The geofencing engine determines a current geofenced area of a mobile device of the user. The artificial intelligence engine determines anticipated needs of the user. The processing circuitry determines, using the affiliate database, a list of candidate affiliates for the current geofenced area, and transmits a query to an affiliate feed for the list of candidate affiliates, where the query is based at least in part on the anticipated need of the user. The processing circuitry determines a first affiliate and a second affiliate based on the query and receives query results for the first affiliate and the second affiliate from the affiliate feed. The results are transmitted to the mobile device for presentation in a user interface.

Provisioning services based on geolocation data and activity recognition

Systems for using cognitive automation techniques to detect unauthorized events are provided. In some examples, a request for a product or service (e.g., event processing) may be received. The request may be received electronically, via a telephone communication, in person, or the like. The request may be analyzed (e.g., using a cognitive automation model) to determine whether it is potentially unauthorized. If so, one or more requests for additional information may be generated or identified. For instance, requests for authentication information, responses to questions or a series of questions, or the like, may be generated. In some examples, the additional information requested may be identified using the cognitive automation model. Responses to the request for additional information may be analyzed (e.g., using the cognitive automation model) to determine whether the request for the product or service is unauthorized. If so, processing the request may be prevented.

Cognitive automation platform for dynamic unauthorized event detection and processing

Aspects of the disclosure relate to a machine learning based identification and classification of database commands. A computing platform may retrieve, by a computing device and from a first database of a plurality of databases, a database command. Subsequently, the computing platform may identify, by the computing device and for the database command and based on a machine learning model, one or more database commands from the plurality of databases, wherein the one or more database commands perform operations similar to the database command. Then, the computing platform may determine, by the computing device and for the database command, a security score indicative of a level of vulnerability associated with the database command. Subsequently, the computing platform may provide, via an interactive graphical user interface, the database command and the security score.

Machine learning based identification and classification of database commands

A method for authenticating to a network comprising a plurality of Internet of Things ('IoT') devices is provided. The method may include using a mobile telephone apparatus, a wrist-worn apparatus and a head-worn apparatus to monitor the level of at least one of a wearer's pulse, body temperature, voice, gait and/or other biorhythmic indicator. One of the aforementioned apparatus may operate as a hub apparatus. The method may further include using the hub apparatus to assign a federated biometric marker based at least in part on the first, second and third biometric markers. The method may also include using artificial intelligence to monitor for one or more outliers with respect to historical monitoring. Each of the one or more outliers may include a magnitude that exceeds a security threshold difference between the current magnitude and the historically monitored magnitude. When the difference in magnitude exceeds a security threshold difference between the current magnitude and the historically monitored magnitude the method may quarantine apparatus associated with the outlier.

Multi-biometric-factor, internet of things (IoT), secured network

Systems and methods for establishing a protected, multi-hop, extensible channel of wireless communication using IoT devices are disclosed. A plurality of IoT devices may relay, via hardware and/or software embedded in each device, wireless messages from one device to another. The IoT devices may form a chain, each IoT device within wireless communication range of at least one other IoT device. The chain may be extended by adding IoT devices to the chain. A message may hop from one IoT device in the chain to the next, and may ultimately be transmitted across the full span of the chain. Hardware and/or software in each IoT device may emit an electromagnetic masking signal at each hop. The masking signal may introduce electromagnetic noise into the environment and protect the message from interception.

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Charlotte, NC, United States of America

Jesse Strickland Newsom, Iii