Bank of America Corporation

Stephen Jack Williams

Richard Scot

Nathan Alexander Dalpini

Rebecca Lynn Pietro

Abelardo Espinoza

Vani Reddy Nareddy

John Shelton

Christopher Daniel Birch

Susan R Hart

Sophie Morgan Danielpour

Son Huynh

Shannon Sabina Rolinger

Tyler Keith Prothro

Amado Jose Martinez Suarez

Embodiments of the present invention provide a system for identifying and redirecting incoming unauthorized data access requests. The system is configured for continuously monitoring one or more incoming data access requests from one or more sources, identifying that a data access request of the one or more incoming data access requests is an unauthorized request, redirecting the data access request to a controlled environment, performing one or more actions in the controlled environment to capture one or more interactions associated with the data access request within the controlled environment, and automatically generating one or more controls to secure a real-time environment based on the one or more interactions captured within the controlled environment.

System and method for identifying and redirecting incoming unauthorized data access requests

Embodiments of the present invention provide a system for implementing authentication control protocols via components of entity device. In particular, the system may be configured to determine initiation of a resource interaction by a user at an entity device, control one or more components of the entity device to cause the entity device to capture a real-time image of the user, compare the real-time image captured via the entity device with an updated enhanced user image stored in a data file of the user, in response to comparing the real-time image with the updated enhanced user image, determine if the real-time image matches the updated enhanced user image, and implement one or more authentication control protocols based on determining if the real-time image matches the updated enhanced user image.

System and method for implementing authentication control protocols via components of an entity device

A system for predicting an anomalous request comprises a processor associated with a server. The processor is configured to generate a first set of data objects associated with a first user profile. The processor is configured to compare the first set of the data objects to approved data and audit data to generate a second set of data objects with a set of anomalous data indicators for the first user profile. The processor is further configured to process the second set of the data objects through an anomaly learning model to determine a predictive degree of approval associated with the user profile. The processor is further configured to determine to how to process the user profile based on the predictive degree of approval. The processor is further configured to assign a profile indicator to the user profile.

System and method for predicting anomalous requests and preventing anomalous interactions in a network

A system for predicting an anomalous request comprises a processor associated with a server. The processor is configured to parse a user profile from a plurality of user profiles to generate a first set of data objects associated with the first user profile. The processor is configured to compare the first set of the data objects to approved data and audit data to generate a second set of data objects with a set of anomalous data indicators for the first user profile. The processor is further configured to process the second set of the data objects through an anomaly learning model to determine a predictive degree of approval associated with the user profile. The processor is further configured to determine to approve, flag or disapprove the user profile based on the predictive degree of approval. The processor is further configured to assign a profile indicator to the user profile.

A system for predicting an anomalous request comprises a processor associated with a server. The processor is configured to parse a user profile from a plurality of user profiles to generate a first set of data objects associated with the first user profile. The processor is configured to compare the first set of the data objects to approved data, audit data, disapproved data to generate a second set of data objects with a set of anomalous data indicators for the first user profile. The processor is further configured to process the second set of the data objects through an anomaly learning model to determine a predictive degree of approval associated with the user profile. The processor is further configured to determine to approve, flag or disapprove the user profile based on the predictive degree of approval. The processor is further configured to assign a profile indicator to the user profile.

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Charlotte, NC, United States of America

Stephen Jack Williams