Bank of America Corporation

Brian H Corr

Youshika C Scott

Charlene L Ramsue

Scott Nielsen

Thomas G Frost

Allison Zimmer

Craig Douglas Widmann

Scott Anderson Sims

Andrew DongHo Kim

George Albero

Dharmender Kumar Satija

Michael Joseph Carroll

Elliot Piatetsky

Jeffrey Brian Bashore

Sai Kishan Alapati

Ronnie Joe Morris, Jr

Holly J Martinez

Jennifer Quillen

Aron Megyeri

Tracy R Regehr

Kolt Bell

Alvino Sarran

Alex Stapleton

Jeff Zusi

Nicole Ryan

Aspects of the disclosure relate to information masking. A user device may receive a request to access information that includes personal identifiable information (PII) and retrieve source data comprising the PII. The user device may mask, within the source data and based on a data management policy, the PII, resulting in masked information. The user device may display the masked information. The user device may receive a request to unmask the masked information and unmask the PII, resulting in unmasked PII. The user device may display the unmasked PII and send unmasking event information to a PII footprint modeling platform, which may cause the PIT footprint modeling platform to: log the request to unmask the masked information in an unmasking event log, 2) apply a machine learning model to the unmasking event log to identify malicious events, and 3) trigger remediation actions based on identification of the malicious events.

Insight generation using personal identifiable information (PII) footprint modeling

Aspects of the disclosure relate to voiceprint tracking and anomaly detection. A computing platform may detect voice information from a call management system. The computing platform may establish voiceprints for employees and clients of an enterprise. The computing platform may detect a call between an employee and a caller attempting to access a client account. The computing platform may identify a first voiceprint corresponding to the employee and a second voiceprint corresponding to the caller. The computing platform may compare the second voiceprint to a known voiceprint corresponding to the client. Based on the comparison of the second voiceprint to the known voiceprint, the computing platform may determine that the second voiceprint does not match the known voiceprint. The computing platform may identify that the second voiceprint corresponds to another employee of the enterprise, and may send a security notification indicating potential unauthorized account access to an enterprise computing device.

Voice analysis platform for voiceprint tracking and anomaly detection

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

Aspects of the disclosure relate to information masking. A user device may receive a request to access information that includes personal identifiable information (PII) and retrieve source data comprising the PII. The user device may mask, within the source data and based on a data management policy, the PII, resulting in masked information. The user device may display the masked information. The user device may receive a request to unmask the masked information and unmask the PII, resulting in unmasked PII. The user device may display the unmasked PII and send unmasking event information to a PII footprint modeling platform, which may cause the PII footprint modeling platform to: log the request to unmask the masked information in an unmasking event log, 2) apply a machine learning model to the unmasking event log to identify malicious events, and 3) trigger remediation actions based on identification of the malicious events.

Aspects of the disclosure relate to information masking. A computing platform may receive, from a user computing device, a request to access information that includes personal identifiable information (PII). The computing platform may retrieve source data comprising the PII and mask, within the source data and based on a data management policy, the PII. The computing platform may send the masked information in response to the request to access the information. The computing platform may receive a request to unmask the masked information and unmask the PII. The computing platform may log the request to unmask the masked information in an unmasking event log and send the unmasked PII in response to the request to unmask the masked information. The computing platform may apply a machine learning model to the unmasking event log to identify malicious events and trigger remediation actions based on identification of the malicious events.

Aspects of the disclosure relate to a system to monitor devices for unauthorized access of an external account. A computing platform may receive, via a communication interface, and from a first user device, metadata comprising first user device identification information and accessed account information. The computing platform may determine, based on the first user device identification information, whether the first user device is one of a plurality of internal user devices. The computing platform may identify, based on the accessed account information, an action indicating the first user device is accessing an account. The computing platform may determine, based on the action and the accessed account information, whether the action is authorized. The computing platform may receive information indicating unauthorized use of the account by the first user device. The computing platform may transmit the information indicating unauthorized use of the account by the first user device.

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Chandler, AZ, United States of America

Brian H Corr