Capital One Services, LLC

Jenny Melendez

Samuel Rapowitz

Joshua S Edwards

Tyler Maiman

Viraj Chaudhary

David Septimus

Daniel Miller

Vyjayanthi Vadrevu

Daniel E Miller

Bryant Alan Yee

Alexander Lin

Joshua Peters

Zachary Sweeney

Steven Black

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

Disclosed embodiments may include a method for automatically providing customized financial card incentives where the system can identify if a transaction is eligible for additional bonus rewards by determining if the user is a student using a clustering algorithm with user similarity data to generate a probability. Then, if the probability is above a predetermined threshold, a machine learning model with comprehensive user data classifies the user as a student or non-student. Once the user is determined to be a student, the transaction is verified as being an educational purchase using the transaction data or by having the user provide an image of a receipt. Once the transaction is verified, the transaction or items within the transaction qualify for additional rewards that are applied to the user's account. Users who are not identified as students may qualify for a standard amount of rewards.

Systems and methods for automatically providing customized financial card incentives

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.

User authentication based on account transaction information in text field

Aspects discussed herein may relate to techniques for authenticating a user using transaction-based authentication questions. The transaction-based authentication questions may be provided to the user. The user may provide audible responses to the transaction-based authentication questions that may be captured by a smart device, such as an always on listening device. Authentication of the user may be based on voice analysis of the audible responses to the transaction-based authentication questions and/or based on the answers to the transaction-based authentication questions.

Using an always on listening device skill to relay answers to transaction-based knowledge-based authentications

Methods, systems, and apparatuses are described herein for improving computer authentication processes by analyzing user response times to authentication questions. A request for access to an account may be received. Transaction data associated with a user of that account may be retrieved, and a list of merchants may be generated based on the transaction data. A blocklist may be retrieved, and the list of merchants may be filtered based on the blocklist. An authentication question may be presented. The authentication question may relate to the list of merchants. User responses may be received, and response times for the user responses may be measured. Based on the response times and the response times for other users, an average response time for the merchants may be determined. Based on the average response time for a particular merchant exceeding a threshold, the particular merchant may be added to the blocklist.

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Falls Church, VA, United States of America

Jenny Melendez