Abstract: 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.

Abstract: An exemplary method includes receiving, by a processor, flight information from a computer device associated with an airline identifying a flight and passengers on the flight that purchased flight tickets by utilizing payment cards associated with a card authorizing entity. Passenger food preferences for the flight passengers based at least in part on historical payment card transaction data of the flight passengers is received from a computing device associated with the card authorizing entity based on the flight information. A food inventory available to be serviced on the flight is received. Automatically determining a flight-specific food inventory, to minimize an overall weight of the flight-specific food inventory to be carried by the flight based on the passenger food preferences. Automatically transmitting food packing instructions to a packing-related machine based on the flight-specific food inventory so as to assemble a portion of the flight-specific food inventory.

Abstract: Methods and systems are described for improvements to authentication processes. For example, conventional systems may rely on password authentication or contact-based alternative authentication techniques that are impractical or infeasible in sensitive medical environments, during pandemics, or fail meet the health and safety needs of an increasingly health-conscious public. In contrast, the described solutions provide an improvement over conventional authentication systems and methods by enabling user authentication via contactless physiological biometric authentication processes, behavioral authentication processes, and passive data authentication processes, that do not require a user to remove personal protective equipment.

Abstract: Aspects described herein may allow for authenticating a user by generating a customized set of authentication questions based on spending patterns that are automatically detected and extracted from user data. The user data may include transaction data collected over a period of time that may indicate the types of merchants that a user frequently transacts with. By automatically detecting user patterns that correspond to user behavior over a period of time, an authentication system may be able to generate authentication questions about those spending patterns that are easily answerable to an authentic user but difficult to guess or circumvent for any other user.

Abstract: Aspects described herein may use a machine learning model to identify transactions likely to be remembered by a user and that may be used to generate challenge questions to authenticate the user. An individual may request an action related to a financial account. In response to the request, the machine learning model may determine a likelihood an authorized user of the financial account will remember one or more recent transactions. The likelihood of each candidate transaction may be compared to a predetermined threshold to determine a subset of recent transactions. Information relating to the subset of recent transactions may be used to generate one or more challenge questions to pose to the user. The user's responses to the challenge questions may be used to evaluate whether the user is the authorized user of the financial account or is a fraudster or imposter.

Abstract: Provided are compact transaction cards. In some approaches, a transaction card may include a body having first and second main sides, a first and second end perimeter perimeters, and first and second side perimeters, wherein a first axis extending between the first and second side perimeters defines a first section of the body and a second section of the body. The card may further include an identification chip along the first main side and a magnetic stripe along the second main side, wherein the magnetic stripe extends between the first and second side perimeters, and wherein the magnetic stripe and the identification chip are located on the first section of the body. The card may further include a textured grip along at least one of the first main side and the second main side, wherein the textured grip is positioned only on the second section of the body.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Various embodiments are directed to a system or platform with machine learning capabilities configured to accurately predict in real-time a depreciation factor of a vehicle associated with a customer and further accurately predict a present value of the vehicle based at least in part on card transaction data associated with the customer. Based on one or more factors, such as a determination that the present value of the vehicle falls below a predefined threshold value, one or more auto financing products may be generated and provided to the customer by the system.

Abstract: 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.

Abstract: Systems, methods, and computer program products disclosed herein relate to knowledge-based authentication leveraging mobile-device photos and assets. In one embodiment, the system can identify, by employing a machine learning model, a plurality of authentication resources associated with a user, wherein the machine learning model is trained using historical information efficacy of authentication challenges. In another embodiment, the system can select a mobile-device photo and a mobile-device asset associated with the user from the plurality of authentication resources. In another embodiment, the system can select a synthetic photo consistent with the mobile-device photo. In another embodiment, the system can generate a challenge that includes the mobile-device photo, the mobile-device asset and the synthetic photo. In another embodiment, the system can authenticate with knowledge-based authentication based upon accuracy of a reply received in response to the challenge.

Abstract: 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.

Abstract: A method and system include identifying, by a processor, departing flight information that designates departure airports and departure times in payment card transaction data of a plurality of users. Airport-specific data for a departure airport before a departure time of a departing flight of a user from the plurality of users is received. The airport-specific data is inputted into a machine learning model that outputs a user-specific airport processing time for the user to reach a departure gate upon arriving to the departure airport. A travel time from a geographical location of the computing device of the user to the departure airport is received from a navigation system. The computing device displays a time for the user to start travel to the departure airport based on the user-specific airport processing time and the travel time to the departure airport for the user to reach the departure gate by the departure time.

Abstract: Provided are compact transaction cards. In some approaches, a body of the card includes first and second sections rotatably coupled together, wherein the first and second sections define a first main side opposite a second main side, an insertion end perimeter opposite a second end perimeter, and a first side perimeter opposite a second side perimeter. The body may include a chip along the first main side, the chip being located on the first section of the body. A magnetic stripe may be provided along the second main side of the body, wherein the magnetic stripe is located on the second section, wherein the magnetic stripe extends parallel to the insertion end perimeter when the first and second sections are in a first configuration, and wherein the magnetic stripe extends perpendicular to the insertion end perimeter when the first and second sections are in a second configuration.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for secure prediction can include a memory and a processor that is configured to retrieve location information associated with a user account and calculate an average spend over one or more route options for a first period of time based on the location information. The processor can be configured to apply a predictive model to the average spend to determine a predicted utilization amount, identify one or more users that have a user utilization amount exceeding the predicted utilization amount, transmit one or more notifications to adjust the user utilization amount from a first value to a second value for a second period of time, and receive one or more responses that are responsive to the one or more notifications. The processor can be configured to adjust the user utilization amount to the second value and revert the user utilization amount back to the first value.