Abstract: A device may process a message associated with an account to identify a first identifier that identifies a third party. The device may identify a match between the first identifier and transaction information related to an individual associated with the account. The transaction information may include a set of first identifiers that identify a respective set of third parties. The transaction information may facilitate identification of one or more messages that is likely to include account information. The device may tag the message with a second identifier based on the match. The device may receive, from another device, the message based on the message being tagged with the second identifier. The device may process the message to identify the account information related to another account. The device may extract identified account information from the message. The device may perform an action related to extracted account information or the message.

Abstract: Disclosed embodiments pertain to protecting sensitive information. A browser extension associated with a web browser can detect a user entering information associated with the user into an electronic form. The browser extension can monitor the user entering sensitive information into the electronic form and detect that the user has entered sensitive information incorrectly. In response, the browser extension can provide a warning to the user that sensitive information has been incorrectly entered. Instructions can be displayed to a user on how incorrectly entered sensitive information is to be corrected. The incorrectly entered sensitive information is corrected based on a response from the user before the sensitive information propagates beyond the electronic form.

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: A device may receive meeting data associated with a first meeting, the first meeting having previously occurred. The device may obtain, based on the meeting data, data identifying at least one individual associated with the first meeting and data identifying at least one topic associated with the first meeting. In addition, the device may identify a second meeting based on the at least one individual or the at least one topic, the second meeting having not yet occurred. The device may provide, to a user device associated with the second meeting and based on identifying the second meeting, at least a portion of the meeting data associated with the first meeting.

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: The disclosed embodiments provide systems, methods, and techniques for managing merchandising cards. A merchandising card may be, for example, a gift card, loyalty card, or the like. Consistent disclosed embodiments, a system for managing merchandising cards may include one or more memory devices storing instructions and one or more processors configured to acquire, from a device over a network, a plurality of locations associated with the device, the device locations being acquired at different instances in time within a predetermined period of time. Additionally, the processor may be configured to calculate a merchant confidence rating for a merchant using the device locations. Further, the one or more processors may be configured to, based on the merchant confidence rating, determine that the merchant matches a merchant that is associated with merchandising card, and send a reminder a user of the device.

Abstract: A device may receive transaction information that identifies a set of third parties. The device may process messages in an electronic messaging account using a set of processing techniques after receiving the transaction information. The device may identify, in the electronic messaging account, a first set of messages associated with the set of third parties. The device may determine a manner in which to configure a user interface of an application associated with a user device to display the first set of messages based on the transaction information. The device may provide a set of instructions to the user device to configure the user interface of the application to display the first set of messages.

Abstract: In some implementations, a front-end device may receive, from a brain-machine interface (BMI) associated with a user, a request to authenticate the user with secret information associated with the user. Accordingly, the front-end device may transmit, to the BMI, a request for an identifier associated with one or more hardware components of the BMI. The front-end device may receive, from the BMI, an indication of the identifier associated with the one or more hardware components. Accordingly, the front-end device may authenticate the user based on the secret information associated with the user and the identifier associated with the one or more hardware components. Additionally, or alternatively, the front-end device may authenticate the user based on a location of an external device associated with the user and/or an indication of a biometric property associated with the user.

Abstract: Multi-factor authentication systems and methods are provided that include receiving a request to authenticate a user of a mobile device. The request for authentication may include credential information associated with the user and vehicle data. A determination may be made regarding whether the vehicle data was obtained from a vehicle via the mobile device. The received vehicle data and received credential information may be compared to stored data. When there is a match between the received vehicle data and received credential information and corresponding stored data, a notification may be provided to the user device indicating that the user has been authenticated.

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: A device may process a message associated with an account to identify a first identifier that identifies a third party. The device may identify a match between the first identifier and transaction information related to an individual associated with the account. The transaction information may include a set of first identifiers that identify a respective set of third parties. The transaction information may facilitate identification of one or more messages that is likely to include account information. The device may tag the message with a second identifier based on the match. The device may receive, from another device, the message based on the message being tagged with the second identifier. The device may process the message to identify the account information related to another account. The device may extract identified account information from the message. The device may perform an action related to extracted account information or the message.

Abstract: Multi-factor authentication systems and methods are provided that include receiving a request to authenticate a user of a mobile device. The request for authentication may include credential information associated with the user and vehicle data. A determination may be made regarding whether the vehicle data was obtained from a vehicle via the mobile device. The received vehicle data and received credential information may be compared to stored data. When there is a match between the received vehicle data and received credential information and corresponding stored data, a notification may be provided to the user device, indicating that the user has been authenticated.

Abstract: A device may receive transaction information that identifies a set of third parties. The device may process messages in an electronic messaging account using a set of processing techniques after receiving the transaction information. The device may identify, in the electronic messaging account, a first set of messages associated with the set of third parties. The device may determine a manner in which to configure a user interface of an application associated with a user device to display the first set of messages based on the transaction information. The device may provide a set of instructions to the user device to configure the user interface of the application to display the first set of messages.

Abstract: A gifter initiates a digital gift reward for a giftee at a location. The digital gift reward is generated and associated with the location. The giftee is notified either when the digital gift reward is generated and/or when the giftee is near the location of the digital gift reward. The giftee receives instructions for claiming the digital gift reward, the instructions include conditions for transferring the digital gift reward to the giftee. The giftee can receive directions for navigating to the digital gift reward. The directions can be augmented reality based directions. When the conditions are satisfied, the digital gift reward can be transferred from the gifter's account to the giftee's account. The gifter may earn a gifting reward upon transfer of the digital gift reward. The gifting reward can be transferred to the gifter's account. The accounts are updated to reflect all transfers.

Abstract: A device may receive meeting data associated with a first meeting, the first meeting having previously occurred. The device may obtain, based on the meeting data, data identifying at least one individual associated with the first meeting and data identifying at least one topic associated with the first meeting. In addition, the device may identify a second meeting based on the at least one individual or the at least one topic, the second meeting having not yet occurred. The device may provide, to a user device associated with the second meeting and based on identifying the second meeting, at least a portion of the meeting data associated with the first meeting.

Abstract: A device may receive access information that permits access to messages associated with an electronic messaging account. The device may process the messages associated with the electronic messaging account to identify one or more of the messages that include at least one coupon after receiving the access information. The one or more of the messages may be identified using at least two of an image processing technique, a text processing technique, or a code processing technique. The device may store information identifying the one or more of the messages that include the at least one coupon after processing the messages. The device may perform one or more actions to facilitate use of the at least one coupon in association with completing a transaction.

Abstract: A device may process a message associated with an account to identify a first identifier that identifies a third party. The device may identify a match between the first identifier and transaction information related to an individual associated with the account. The transaction information may include a set of first identifiers that identify a respective set of third parties. The transaction information may facilitate identification of one or more messages that is likely to include account information. The device may tag the message with a second identifier based on the match. The device may receive, from another device, the message based on the message being tagged with the second identifier. The device may process the message to identify the account information related to another account. The device may extract identified account information from the message. The device may perform an action related to extracted account information or the message.

Abstract: A device may receive meeting data associated with a first meeting, the first meeting having previously occurred. The device may obtain, based on the meeting data, data identifying at least one individual associated with the first meeting and data identifying at least one topic associated with the first meeting. In addition, the device may identify a second meeting based on the at least one individual or the at least one topic, the second meeting having not yet occurred. The device may provide, to a user device associated with the second meeting and based on identifying the second meeting, at least a portion of the meeting data associated with the first meeting.

Abstract: Aspects described herein may use behavioral biometric data to authenticate an individual that requests performance of an action related to a financial account. In response to the request, challenge questions relating to recent transactions conducted with the financial account may be generated. The challenge questions may be provided to the individual and may prompt the individual for audile response and/or touch input responses. Behavioral biometric data may be extracted from the responses and may be used to determine a likelihood the individual is an authorized user of the account.

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.