Capital One Services, LLC

David Septimus

Tyler Maiman

Samuel Rapowitz

Viraj Chaudhary

Joshua S Edwards

Jenny Melendez

Daniel Miller

Vyjayanthi Vadrevu

Daniel E Miller

Methods, systems, and apparatuses are described herein for improving computer authentication processes through excluding fraudulent transactions in transaction-based authentication. A computing device may receive a request for access to an account from a user. The computing device may provide transaction data to a machine learning model. The computing device may receive data indicating a confidence threshold associated with the user from the machine learning model. The computing device may generate a modified set of merchant choices for the user by excluding merchants related to potentially fraudulent transactions within a predetermined time period. An authentication question may be generated, and access to the account may be provided based on a response to the authentication question.

Excluding fraudulent transactions in transaction based authentication

Methods, systems, and apparatuses are described herein for improving the security of personal information by preventing attempts at gleaning personal information from authentication questions. A computing device may receive a request for access to an account associated with a user. The request may comprise candidate authentication information. Based on comparing the candidate authentication information with the account data, the computing device may generate a synthetic authentication question. The synthetic authentication question may be generated as if the candidate authentication information is valid. A response to the synthetic authentication question may be received, and the request for access to the account may be denied.

Preventing unauthorized access to personal data during authentication processes

Methods, systems, and apparatuses are described herein for improving computer authentication processes through the exclusion of certain merchants that may cause confusion. Indications of a plurality of different merchants, including merchant logos may be received. The indications may be processed to identify at least one similarity between a first merchant and a second merchant. A request for access to an account associated with a user and transaction data corresponding to the account may be received. Based on the similarity between the first merchant and the second merchant, at least one transaction corresponding to the first merchant may be removed to generate processed transaction record. An authentication question may be generated and a candidate response to the authentication question may be received. Based on the candidate response, access to the account may be provided.

Account authentication excluding merchants based on merchant logos

Methods, systems, and apparatuses are described herein for improving the accuracy of synthetic authentication questions by analyzing third party account data. A request for access to a first account associated with a user may be received. The first account may be managed by a first organization. A transactions database might be queried for first account data. Second account data corresponding to a second account associated with the user might be received. That second account may be managed by a second organization different from the first organization. One or more second transactions, unique to the second account, may be identified. A synthetic transaction, configured to be different from transactions in the first account and the one or more second transactions, may be generated. An authentication question may be generated based on the synthetic transaction. Access to the first account might be provided based on a response to the authentication question.

Third party data processing for improvement of authentication questions

Methods, systems, and apparatuses are described herein for improving computer authentication processes through presenting favorite merchants selection in transaction-based authentication. A computing device may receive a request for access to an account associated with a user. The computing device may train a machine learning model to determine predicted favorite merchant information, and provide one or more transactions conducted by the user as input to the trained first machine learning model. Data indicating favorite merchant information may be received from the trained first machine learning model. An authentication question may be generated and a candidate response to the authentication question may be received. Based on the candidate response, access to the account may be provided.

Favorite merchants selection in transaction based authentication

Aspects described herein may allow for authenticating a user by generating a customized set of authentication questions based on patterns that are automatically detected and extracted from user data. The user data may include transaction data collected over a period of time. By automatically detecting user patterns that correspond to user behavior over a period of time, an authentication system may be able to generate information that is recognizable to an authentic user but difficult to guess or circumvent for any other user.

Authenticating based on behavioral transactional patterns

Methods, systems, and apparatuses are described herein for improving computer authentication processes by using physical environment information. A computing device may receive video depicting a physical environment and process that video to identify one or more first objects. The computing device may receive a request for access to an account. The computing device may send a prompt for a physical environment description and receive, in response, an indication of one or more second objects. The indication of the one or more second objects may comprise video of the physical environment, a verbal description of the physical environment, and/or text describing the physical environment. The computing device may grant the user device access to the account based on comparing the one or more first objects to the one or more second objects.

Physical environment based account authentication

Aspects described herein may relate to techniques for detecting login activity to a financial account during a knowledge-based authentication process. The login activity may be related to access to an online interface for the financial account. The detection of login activity during the authentication process my indicate that the integrity of the authentication process is compromised as login access may provide an individual with transaction data that may be used to answer transaction-based authentication questions. As a result of detecting login activity, an alternative authentication process may be initiated or an authentication request related to the financial account may be denied.

Detecting separate login during action based knowledge-based authentication and disqualifying validity

Methods, systems, and apparatuses are described herein for improving computer authentication processes by generating authentication questions based on the location of a user. Transaction data indicating a plurality of transactions associated with a user account may be received. Location data indicating a plurality of locations of a user device might be received. At least a subset of the plurality of transactions may be tagged, based on the location data, with an indication that a user was present for a respective transaction. For example, a location of a merchant might be compared to a user device location indicated by the location data. A plurality of authentication questions might be generated based on the subset of the plurality of transactions. Access to the user account might be provided based on responses to the plurality of authentication questions.

User presence detection for authentication question generation

Methods, systems, and apparatuses are described herein for improving the accuracy of authentication questions using transaction limitations provided by users. A request for access to an account associated with a user may be received from a user device. An account restrictions database may be queried to determine one or more transaction rules associated with the account. The one or more transaction rules may have been created by the user and indicate limitations on financial transactions that may be performed via the account. An authentication question may be generated that is associated with a violation of the one or more transaction rules. The authentication question may be provided to the user device, and a response to the authentication question may be received. Access to the account may be provided to the user device based on the response.

Generation of authentication questions based on user-created transaction limitations

Disclosed embodiments may include a method for authenticating calls for a call center where the user can authenticate before or after starting a call to a call center using an application on a user device. The system determines what level of authentication is necessary depending on the task the user wants to accomplish during the call. The system may direct the user to provide credentials, such as a username and password, for some tasks. Other tasks may require the user to verify their financial card by taking a photo of their financial card or tapping their financial card to their device and allow the device to interact with the financial card using NFC technology. Once appropriately authenticated, the system then allows the user to make a call to the call center and also sends a signal to the call center that the user has been previously authenticated.

Systems and methods for authenticating calls for a call center

Methods, systems, and apparatuses are described herein for improving computer authentication processes using vocal confidence processing. A request for access to an account may be received. An authentication question may be provided to a user. Voice data indicating one or more vocal utterances by the user in response to the authentication question may be received. The voice data may be processed, and a first confidence score that indicates a degree of confidence of the user when answering the authentication question may be determined. An overall confidence score may be modified based on the first confidence score. Based on determining that the overall confidence score satisfies a threshold, data preventing the authentication question from being used in future authentication processes may be stored. The data may be removed when a time period expires.

Authentication question improvement based on vocal confidence processing

Methods, systems, and apparatuses are described herein for improving the accuracy of authentication questions using e-mail processing. A request for access to an account may be received from a user device. A plurality of organizations may be identified. One or more e-mail associated with the account may be identified. The e-mails may be processed to identify one or more organizations that correspond to transactions conducted by a user. A modified plurality of organizations may be generated by removing, from the plurality of organizations, the one or more organizations. An authentication question may be generated and provided to the user device. A response to the authentication question may be received, and the user device may be provided access based on the response.

Email processing for improved authentication question accuracy

Methods, systems, and apparatuses are described herein for authenticating access to an account using questions which allow users to provide textual information about transactions conducted by an account. A machine learning model may be trained to predict the guessability of merchants. A request for access to an account may be received, and transaction data for that account may be received. An authentication question may be presented. For example, a user may be prompted to list places they have shopped recently. A user response comprising text data may be received. The text data may be processed to identify one or more merchants. If the one or more merchant(s) are represented in the transaction data, the machine learning model may be used to determine the guessability of the one or more merchants. An authentication score may be determined based on the guessability, and access to the account may be provided.

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New York, NY, United States of America

David Septimus