Bank of America Corporation

Wells Fargo Bank, N.a.

Michael E Toth

Carrie Anne Hanson

David M Grigg

Peter John Bertanzetti

Hitesh Shah

Joseph Neil Johansen

Elizabeth Votaw

Xianhong Zhang

Charles Jason Burrell

Daniel Lynn Carpenter

Hood Qaim-Maqami

Dipika Jain

Matthew Alexander Calman

William Earnest Kelley

Chris Purvis

Srinivasa Rao Goriparthi

Dennis M Osborne

Craig Douglas Widmann

Scott Anderson Sims

Andrew DongHo Kim

Dharmender Kumar Satija

Michael Joseph Carroll

Ashish Arora

Kolt Bell

Elliot Piatetsky

Sai Kishan Alapati

Stephen M Schneeweis

Alicia C Jones-McFadden

Andrew T Keys

Bryan D Hall

Apeksh M Dave

Muniraju Jayaramaiah

Kalyan V Pasumarthi

Preston Wesley Ports, Iii

Archie Agrawal

Holly J Martinez

John Zemaitis

Matthew M Choiniere

Erin McCullen

Yu Fu

Kenneth B Norris

Chris Mathes

Sangeeta Varma Martinez

Melissa K Van Leuven

Dawn M Friscia

Jothimuthu Palanisamy

Deborah D Jackson

Jeffrey Jacoby

Rakesh Juyal

Aspects of the disclosure relate to dynamically generating activity prompts to build and refine machine learning authentication models. A computing platform may process a first set of login events associated with a first user account and may build a first user-specific authentication model for the first user account. Then, the computing platform may process a second set of login events associated with a second user account and may build a second user-specific authentication model for the second user account. The computing platform also may build a population-level authentication model for a plurality of user accounts. Thereafter, the computing platform may identify one or more activity parameters associated with at least one authentication model for refinement. Subsequently, the computing platform may generate and send one or more activity prompts to one or more client computing devices to request at least one user response.

Dynamically generating activity prompts to build and refine machine learning authentication models

Aspects of the disclosure relate to processing authentication requests to secured information systems based on machine-learned user behavior profiles. A computing platform may receive an authentication request corresponding to a request for a user of a client computing device to access one or more secured information resources associated with a user account. The computing platform may capture behavioral parameters associated with the client computing device and may evaluate the behavioral parameters using a behavioral profile associated with the user account to determine a behavioral deviation score. Based on the behavioral deviation score, the computing platform may select an authentication action from a plurality of pre-defined authentication actions. Subsequently, the computing platform may generate commands directing an account portal computing platform to allow access, conditionally allow access, or prevent access based on the selected authentication action. Then, the computing platform may send the commands to the account portal computing platform.

Processing authentication requests to secured information systems based on machine-learned user behavior profiles

Aspects of the disclosure relate to processing authentication requests to secured information systems using machine-learned user-account behavior profiles. A computing platform may receive an authentication request corresponding to a request for a user of a client computing device to access one or more secured information resources associated with a user account. The computing platform may capture one or more behavioral parameters and activity data associated with one or more interactions with one or more non-authenticated pages. Then, the computing platform may evaluate the one or more behavioral parameters and the activity data using a behavioral profile associated with the user account. Based on this evaluation, the computing platform may identify the authentication request as malicious and may generate and send one or more denial-of-access commands to prevent the client computing device from accessing the one or more secured information resources associated with the user account.

Processing authentication requests to secured information systems using machine-learned user-account behavior profiles

A system provides for automated image authentication and external database verification. In particular, the system may perform automatic authentication of an image by extracting data and/or metadata from the image for subsequent analysis. For instance, the extracted data and/or metadata may be compared with historical data to drive authorization processes (e.g., past instances of unauthorized activity). In some embodiments, the system may further use external databases which may be hosted by a trusted third party to perform additional authentication steps and/or verification. In this way, the system may provide a reliable and efficient way to perform authentications.

System for automated image authentication and external database verification

Aspects of the disclosure relate to preventing unauthorized access to secured information systems using advanced biometric authentication techniques. A computing device may receive input requesting to login to a user account associated with a mobile banking application installed on the computing device. The computing device may send, to a client authentication computing platform, a first authentication request and receive, from the client authentication computing platform, one or more authentication prompt commands. The computing device then may present one or more authentication prompts and collect, from one or more linked wearable devices, one or more advanced biometrics. After validating the one or more advanced biometrics, the computing device may send, to the client authentication computing platform, authentication response data. Thereafter, the computing device may receive mobile banking user interface information and present one or more mobile banking user interfaces.

Preventing unauthorized access to secure information systems using advanced biometric authentication techniques

Processing authentication requests to secured information systems based on user behavior profiles

Aspects of the disclosure relate to processing authentication requests to secured information systems based on machine-learned event profiles. A computing platform may receive an authentication request corresponding to a request for a user of a client computing device to access one or more secured information resources associated with a user account in a client portal session. The computing platform may capture one or more behavioral parameters and may generate one or more authentication prompts. Thereafter, the computing platform may receive one or more authentication prompt responses and may evaluate an event pattern. Based on evaluating the event pattern and validating the one or more authentication prompt responses, the computing platform may generate and send one or more authentication commands directing an account portal computing platform to allow access to the one or more secured information resources associated with the user account in the client portal session.

Processing authentication requests to secured information systems based on machine-learned event profiles

Aspects of the disclosure relate to processing authentication requests to secured information systems using machine-learned user-account behavior profiles. A computing platform may receive an authentication request corresponding to a request for a user of a client computing device to access one or more secured information resources associated with a user account. The computing platform may capture one or more behavioral parameters and may authenticate the user of the client computing device to the user account based on the one or more behavioral parameters and one or more authentication credentials. The computing platform then may generate and send one or more authentication commands directing an account portal computing platform to allow access to the one or more secured information resources. Subsequently, the computing platform may capture activity data associated with one or more interactions in a client portal session and may update a behavioral profile associated with the user account.

Systems for detecting unauthorized user and controlling dynamic user interface functionality are provided. The system may receive a request to access functionality that may include login credentials of a user. The request may also include additional information associated with a computing device from which the request is received. The request and additional data may be analyzing using one or more machine learning datasets to determine whether a user requesting access is an authorized user or an unauthorized user. If the user is an authorized user, the user may be authenticated to the system an authentic user interface having enabled functionality may be generated. If the user is an unauthorized user, a decoy user interface having functionality disabled may be generated.

Controlling dynamic user interface functionality using a machine learning control engine

Aspects of the present disclosure are directed to electronic computer implemented methods of data communication. At least one method includes, via a data communications network, receiving one or more mobile EDI token datasets associated with each of one or more mobile devices; each of the mobile EDI token datasets including a mobile online ID attribute data element, a beacon attribute data element and biometric ID attribute data element; via a data communications network, receiving a matching score attribute data element associated with at least one of the mobile EDI token datasets; electronically processing and authenticating the least one mobile EDI token dataset based on the received matching score attribute data element; and via a data communications network, transmitting the mobile online ID attribute associated with the authenticated mobile EDI token dataset.

Method and system for data communication and frictionless authentication

Aspects of the disclosure relate to preventing unauthorized access to secured information systems using advanced pre-authentication techniques. A computing platform may receive, from a local traffic manager, a first enriched access request associated with a first remote computing device. Then, the computing platform may apply a pre-authentication classification model to the first enriched access request associated with the first remote computing device. Thereafter, the computing platform may determine that the first enriched access request associated with the first remote computing device is likely malicious. Then, the computing platform may generate one or more first pre-authentication response commands directing client portal server infrastructure to process the first enriched access request associated with the first remote computing device as a malicious request. Subsequently, the computing platform may send the one or more first pre-authentication response commands to the client portal server infrastructure.

Preventing unauthorized access to secure information systems using advanced pre-authentication techniques

Systems for providing secure access to systems are provided. A computing device may receive a request to access functionality which may include login credentials of a user. Upon receiving the request to access functionality, the computing device may execute a scan of an area surrounding the computing device to detect any wearable devices within proximity of the computing device that are linked to the computing device. The authenticating information and, in some examples, detected, linked wearable device, may be validated. Based on the validation, authentication response data may be generated and transmitted to an authentication computing platform which may cause the authentication computing platform to validate the authentication response data and cause the computing device to connect to a client interface computing platform. After a connection between the computing device and client interface computing platform is established, interface data may be transmitted from the client interface computing platform to the computing device and one or more interfaces may be displayed on the computing device.

Preventing unauthorized access to secure information systems using multi-factor, hardware based and/or advanced biometric authentication

An authentication device that includes an authentication engine configured to detect devices proximate to a terminal and to identify a user profile based on the detected one or more devices. The user profile identifies at least one of the detected devices in a device registry. The authentication engine is further configured to receive a data access request for a data resource and to identify authentication requirements for a multifactor authentication process for the user based on the detected devices. Identifying the authentication requirements includes setting types of authentication and a number of authentication levels that are used for performing multifactor authentication with the user. The authentication engine is further configured to execute the multifactor authentication process for the user, to determine whether the user has satisfied the authentication requirements, and to provide access to the data resource in response to determining the user has satisfied the authentication requirements.

Data access control using multi-device multifactor authentication

Growing community of inventors

Charlotte, NC, United States of America

Michael E Toth