Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems, computer program products, and methods are described herein for elevated authentication model using cross-channel data. The present invention is configured to receive one or more exposure events from a detection system, wherein at least one of the one or more exposure events indicates that a user has failed an authentication requirement in at least one communication channel associated with the detection system; store the one or more exposure events in a centralized repository; determine one or more other communication channels across the one or more detection systems available for access to the user; and increase an authentication requirement of the user in the one or more other communication channels, wherein increasing the authentication requirement further comprises increasing an authentication level required to enable the user to access at least one of the one or more functions associated with at least one of the one or more applications.

Abstract: Systems, computer program products, and methods are described herein for the creation of an exposure identification and distribution system for across silo channel integration. In this way, the system allows team integration of data across rails and silos for exposure identification. As such, the system generates a centralized repository for data and a communication linkage across the various systems used for identifying exposure and manipulated for linkage via a specific taxonomy overlay. Furthermore the system also includes logic that, when identification of a potential exposure indicate occurs, the system may communicate via distribution over the rails for integration and sharing for cross entity exposure identification.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.