Bank of America Corporation

Ruchira Ghosh

John Henry Denning

Kevin P Thomsen

Geoffrey George Aslaksen

Levi Weldon Mcvay

W Scott Hammet

Alexander Thomas Edwards

Maria Konyakhina Smith

Russell Scott Nejdl, Jr

Embodiments of the invention are directed to a system, method, or computer program product for probing and identifying unique transaction characteristics associated with digital resource exchange platforms. The system identifies the unique characteristics and stores them as digital resource exchange identifiers capable of being cross-referenced with real-time or stored transaction data from multiple transaction channels in order to intelligently identify transactions involving one or more entity resource accounts and digital resource exchanges. Various decisioning steps are described which may be automated following the identification of a transaction or entity resource account involving one or more digital resource exchanges.

Specialty application electronic exchange mitigation platform

Embodiments of the invention are directed to systems, methods, and computer program products for intelligent transaction information sharing, issue notification, and remedial response. By utilizing a common platform for information sharing between multiple financial entities, the invention provides a channel of communication between these entities in order to provide preemptive information as to potential problematic resource transfers. The system is also adaptive in that it can be adjusted based on the needs or goals of the entity or team utilizing it, and may adapt over time in an intelligent fashion to identify and alert of patterns developing over a cross-transaction dataset in real time.

Systems and methods for expedited resource issue notification and response

Systems for dynamically detecting and controlling unauthorized events are presented. In some examples, data may be received from one or more computing systems. In some examples, the computing systems may each be associated with an enterprise unit within an enterprise organization. The data may include, in some examples, processed unauthorized activity event data, such as account takeover event data. The data received may be aggregated and analyzed (e.g., using machine learning) to identify potential threats and threat outputs. In some examples, the threat output may include a user interface indicating the threat or potential threat, systems or applications potentially impacted, enterprise units impacted, and the like. Based on the threat output, one or more mitigation actions may be identified and executed. The mitigation actions may include modifying operation of one or more systems, modifying authentication requirements, and the like.

Dynamic unauthorized activity detection and control system

Systems for dynamically evaluating and controlling access to applications and systems are presented. In some examples, registration data may be received. In some examples, unauthorized activity data may be received. The unauthorized activity data may be analyzed to identify one or more vulnerable applications. Users of the one or more vulnerable applications may then be identified. Entitlements for the one or more vulnerable applications may be identified and, in particular, entitlements having privileged access may be identified. Users having entitlements with privileged access may be identified and one or more modified authentication requirements may be implemented.

Dynamic access evaluation and control system

A system includes a plurality of servers hosting a plurality of software applications, a data lake, and a vulnerability assessment tool. The data lake is configured to store data related to vulnerabilities in the servers and applications. The data lake also stores data on the number of intrusion events detected on each application. The vulnerability assessment tool is configured to generate logic tables relating the data on server vulnerabilities, application vulnerabilities, and application intrusion counts. The vulnerability assessment tool may use the extrapolations from the newly ordered data to flag applications or servers for prioritized security improvements.

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Southlake, TX, United States of America

Ruchira Ghosh