Bank of America Corporation

Rebecca Lynn Pietro

Richard Scot

Nathan Alexander Dalpini

Stephen Jack Williams

John Shelton

Abelardo Espinoza

Vani Reddy Nareddy

James J Siekman

Neal Aaron Slensker

Benjamin F Tweel

John Raymond Omernik

Shannon Sabina Rolinger

Poppy Marie Kimball

Christopher Nicholas King

Todd E Ratts

Christopher Thomas Kopechessi

Robert Marshall Voyles, Iii

Arrangements for comprehensive threat mitigation are provided. In some aspects, an indication of threat or potential threat may be received from an external entity. In some examples, the threat may include a cybersecurity threat. In response to receiving the indication of threat, identifying data associated with the external entity may be extracted from the indication and used to retrieve pre-stored customizations associated with the desired mitigating actions of the external entity. The one or more mitigating actions may be identified and instructions to execute the one or more mitigating actions may be generated and transmitted to one or more computing devices for execution.

Multi-computer system for comprehensive threat detection and mitigation

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.

A system provides multichannel aggregation of electronic data via a centralized integration hub. In particular, the system may aggregate data associated with a user and provide various processes and/or functions via an integration hub that is accessible by the user via a user device. In this way, the system may allow the client to efficiently navigate the various features offered by the entity for account optimization purposes.

System for multichannel aggregation of electronic data via a centralized integration hub

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Fort Mill, SC, United States of America

Rebecca Lynn Pietro