Bank of America Corporation

Jeffrey Brian Bashore

Craig Douglas Widmann

Scott Anderson Sims

Andrew DongHo Kim

Dharmender Kumar Satija

Michael Joseph Carroll

Jennifer Quillen

Ronnie Joe Morris, Jr

Brian H Corr

Elliot Piatetsky

Sai Kishan Alapati

Holly J Martinez

Tracy R Regehr

Jeffrey David Finocchiaro

Pavan Kumar Reddy Kotlo

Aron Megyeri

Randy J Nelson

James F Stevens

Joel Filliben

Michael Shih

Christopher J Cooley

Lizabeth Rosenberg

Akhilendra Reddy Kotha

The present invention is generally related to systems and methods for providing an improved authentication and verification system through the use of compiled user data and user location or traffic data from multiple channels of input. Multiple devices may be utilized by the system in order to receive and process data to authenticate user identities and verify the validity of account activity.

Systems and methods for geolocation security using biometric analysis

Systems, computer program products, and methods are described herein for detection and classification of intrusion using machine learning techniques. The present invention is configured to electronically receive, from a computing device of a user, an indication that the user has initiated a first resource interaction; retrieve information associated with the first resource interaction, wherein the information comprises at least one or more parameters associated with the first resource interaction; initiate a machine learning model on the one or more parameters associated with the first resource interaction; and classify, using the machine learning model, the first resource interaction into one or more classes, wherein the one or more classes comprises one or more access types.

System for detection and classification of intrusion using machine learning techniques

Embodiments of the invention are directed to a system, method, or computer program product for cross-channel network security with tiered adaptive mitigation operations. In this regard, the invention is structured for dynamic detection of security events associated with network devices and resources, and triggering real-time mitigation operations across a plurality of resource channels. The invention provides a novel method for employing activity data to construct and implement mitigation actions for de-escalating authorization tiers that are adapted to the specific attributes of the activity data, in order to prevent security exposure associated with the activity. Another aspect of the invention is directed to determining whether to continue the tiered adaptive mitigation actions and/or trigger a security proceed signal.

Cross-channel network security system with tiered adaptive mitigation operations

Systems, computer program products, and methods are described herein for intrusion detection using resource activity analysis. The present invention is configured to receive, from a computing device of a user, an indication that the user has accessed a resource allocation portfolio of a customer; determine a geographic information of the user; retrieve a geographic information of the customer; determine that the geographic information of the user does not match the geographic information of the customer; determine an exposure level associated with the user access of the resource allocation portfolio of the customer; determine that the exposure level is greater than a predetermined threshold; and automatically trigger a transmission of a notification to a computing device of an administrator indicating that the exposure level associated with the user access of the resource allocation portfolio of the customer is greater than the predetermined threshold.

System for intrusion detection using resource activity analysis

Embodiments of the present invention provide an innovative system, method, and computer program product for automated device activity analysis in both a forward and reverse fashion. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns associated with particular user devices is provided. The system is also designed to generate a historical query of user device touch points or interaction points with entity systems across multiple data vectors, and generate system alerts as patterns or potential issues are identified. Common characteristics of data may be used to detect patterns that are broadened in scope and used in a generative neural network approach.

Systems and methods for automated device activity analysis

Growing community of inventors

Saint Augustine, FL, United States of America

Jeffrey Brian Bashore