Capital One Services, LLC

Zainab Zaki

Jackson Cheek

Jeffrey Brown

Clayton Johnson

Jeffrey Carlyle Wieker

Manuel Vincente Vivo

Laura Lee Boykin

Joanna Weber

Systems, apparatuses, and methods for verifying physical presence of a mobile device as part of an electronic transaction. Tags may be generated and displayed on one or more display devices. Those tags, which may be time-limited, may be read by a mobile device to verify physical presence of the mobile device as a prerequisite to initiating, continuing with, or completing an electronic transaction, and/or as a decision point in how the electronic transaction is to be conducted.

Presence verification for electronic transactions

A computer-implemented method for executing a user instruction may include obtaining identification data of a user via a device associated with the user, wherein the identification data comprises at least a password, a user name, and biometric data of the user; determining, via the one or more processors, a login status based on the identification data; demonstrating, to the user, historical account data based on the login status, wherein the historical account data comprises at least historical biometric data associated with one or more historical logins; receiving, via the one or more processors, the user instruction based on the historical account data, wherein the user instruction comprises at least one of revoking a historical login, changing password, or signing out a historical device associated with a historical login of the one or more historical logins; and executing, via the one or more processors, the user instruction.

Methods and systems for executing a user instruction

Systems and methods for adaptive verification may include a memory and a processor. The memory may be configured to store a plurality of animation templates. The processor may be configured to perform a first challenge process to request a first user image from a first predetermined distance, receive the first user image, request a second user image from a second predetermined distance, receive the second user image, transmit the first user image and the second user image for a verification process, the verification process including identification of one or more user attributes, receive a third user image associated with the one or more user attributes identified during the verification, and display the third user image including an adaptation, wherein the adaptation is generated for at least one of the plurality of animation templates, the adaptation illustrating the one or more user attributes.

Systems and methods for secure adaptive illustrations

A node of a network receives account access information (AAI) for an account that a user has with a system. The node causes a first record to be added to a distributed ledger that is used for managing access to the account, wherein the first record includes the AAI or an encrypted storage identifier that identifies a storage location for the AAI. The node determines a likelihood that the account is being accessed by the user based on the AAI and/or historical AAI for one or more accounts, that include the account, that are used to access the system. The node causes a second record that includes a value representing the likelihood to be added to the distributed ledger. The node causes one or more other nodes of the network to be alerted that the first record and the second record have been added to the distributed ledger.

Account access security using a distributed ledger and/or a distributed file system

Example embodiments of systems and methods for telephone call regulation include the use of a predictive model. An example method includes receiving, by a predictive model, a telephone number from a user application comprising instructions for execution on a user device and determining, by the predictive model, a spam factor for the received telephone number. The example method further includes determining, by the predictive model, a classification of the received telephone number based on the spam factor and transmitting, by the predictive model, the classification of the received telephone number to the user application. The example method further includes receiving, by the predictive model, a feedback from the user application, the feedback indicative of a corrective classification of the received telephone number and modifying, by the predictive model, the spam factor for the received telephone number based on the corrective classification.

Systems and methods for telephone call regulation based on spam factor and user input

An exemplary system, method, and computer-accessible medium can include, for example, receiving at a first entity from a second entity, a first request for encrypted user information associated with at least one user, wherein the first request includes a user identifier associated with the at least one user, and wherein the first entity is different from the second entity, sending, from the first entity, a second request to a user device associated with the at least one user, wherein the second request includes a notification to the at least one user for an affirmation of the first request or a denial of the first request, receiving at the first entity from the user device, the affirmation or the denial, and sending, from the first entity to the second entity, (i) the encrypted user information if the affirmation is received or (ii) a denied notification if the denial is received.

System, method, and computer-accessible medium for actionable push notifications

Systems and methods for telephone call regulation include the use of a predictive model. An example method includes receiving, by a predictive model, a telephone number from a user application comprising instructions for execution on a user device and determining, by the predictive model, a spam factor for the received telephone number. The example method further includes determining, by the predictive model, a classification of the received telephone number based on the spam factor and transmitting, by the predictive model, the classification of the received telephone number to the user application. The example method further includes receiving, by the predictive model, a feedback from the user application, the feedback indicative of a corrective classification of the received telephone number and modifying, by the predictive model, the spam factor for the received telephone number based on the corrective classification.

A system for identifying suspicious logins. The system may include a memory storing executable instructions, and at least one processor configured to execute the instructions to perform operations. The operations may include receiving a first user login from an electronic device, the first user login comprising login credentials; receiving a second user login from an electronic device, the second user login comprising login credentials; analyzing a plurality of device history characteristics, wherein at least one device history characteristic comprises a determination of a last financial transaction performed by an electronic device; determining, based on the analysis, that the first and second user logins originate from different electronic devices; identifying, based on the determination and the analysis, the second user login as a suspicious login; sending, to a user, a notification of the identified suspicious login; and modifying, based on the notification, security settings on an account of the user.

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Reston, VA, United States of America

Zainab Zaki