Abstract: A method of receiving, by an access controlling server, a danger-related communication for an inclement danger condition. The access controlling server is in operational communication with a locking mechanism, controlling an opening and closing condition of a door of the enclosed structure. The access controlling server enables, based on the danger-related communication, an entry allowance setting of the locking mechanism of the door of the enclosed structure. The access controlling server adjusts the security restriction associated with the security-protected machine or the enclosed structure. The access controlling server transmits a push notification, informing a user in the area that the entry allowance setting of the locking mechanism of the access controlling server of the door of the enclosed structure is enabled.

Abstract: In some embodiments, generating synthetic training datasets for training machine learning models in training data-sparse environments for non-homogenous predictions may be facilitated. In some embodiments, user-specific information associated with a user may be received. The system may generate synthetic training data representing one or more alternative actions corresponding to one or more characteristics by: labeling the user-specific information, determining (e.g., based on the labeled user-specific information) one or more characteristics of the labeled user-specific information, and determining (e.g., based on the one or more characteristics) one or more alternative actions corresponding to the one or more characteristics. The system may then train a machine learning model based on the synthetic training data to generate a prediction in response to providing an action of a first user to the machine learning model.

Abstract: In some implementations, a transaction card may include a card body having a first surface and a second surface opposite the first surface. The transaction card may include an integrated circuit (IC) chip embedded in the card body, and a plurality of first electrical contact sets electrically accessible from the first surface of the card body, where the plurality of first electrical contact sets have electrical connections to the IC chip. The transaction card may include a plurality of first magnetic stripe areas readable from the first surface of the card body. The transaction card may include a plurality of second electrical contact sets electrically accessible from the second surface of the card body, where the plurality of second electrical contact sets have electrical connections to the IC chip. The transaction card may include a plurality of second magnetic stripe areas readable from the second surface of the card body.

Abstract: A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

Abstract: Embodiments are directed to methods and techniques for providing power via piezoelectric components on contactless cards.

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).

Abstract: Disclosed is a contactless card, and a system in which the contactless card may be used. The contactless card may include a chip component, a communication interface, processing circuitry, and electromagnetic field sensing circuitry. The electromagnetic field sensing circuitry of the contactless card may be operable to provide orientation signals to the processing circuitry when the contactless card is in proximity to an oscillating electromagnetic field output by a card reader device. The processing circuitry of the contactless card may be operable to receive the orientation signals. An orientation of the contactless card with respect to the mobile device may be determined. An orientation indication signal may be generated based on the determined orientation, and a haptic indication, an audio indication, a visual indication or a combination directing movement of the contactless card in a particular direction may be output.

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: 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.

Abstract: Disclosed embodiments may include a system configured to perform digital image analysis. The system may receive transaction data and image data associated with a user. The system may identify, from the transaction data, first travel feature(s). The system may identify, from the image data via computer vision, second travel feature(s). The system may train a machine learning model (MLM) to generate trip recommendation(s) for the user based on the first travel feature(s) and the second travel feature(s). The system may determine, via the trained MLM, whether at least a first trip recommendation of the trip recommendation(s) exceeds a predetermined threshold indicating a likelihood the user will be interested in the first trip recommendation. Responsive to determining the first trip recommendation exceeds the predetermined threshold, the system may provide the first trip recommendation to the user.

Abstract: Aspects provided may allow for an entity to deactivate a virtual card number (VCN). In particular, aspects provide for deactivating VCNs based on a frequency or periodicity of an interaction. After deactivation, the entity may block future charges to the VCN. Further aspects provide for using incoming charge data sent to one or more previous users to train a machine learning model to determine the frequency or periodicity of the interaction. Additional aspects may also provide for including trial length when determining the deactivation date, providing VCN configuration options to a user, and notifying the user based on upcoming and current VCN status changes.

Abstract: Systems and methods for fraud monitoring are disclosed, including: receiving a transaction request associated with a first instrument of a user; extracting, characteristics of the transaction request; identifying, by the first processor, user data based on the transaction request; determining a fraud severity value and notification value based on inputting the characteristics and user data into a fraud machine learning model; performing a first fraud action based on the fraud severity value; wherein the first fraud action is at least one selected from the group of locking the first instrument for a period of time, deactivating the first instrument, and electronically transmitting a first query message to a user device associated with the first instrument; and transmitting a fraud notification based on the notification value, wherein the fraud notification includes severity information associated with the fraud severity value.

Abstract: In some implementations, a system may receive interaction data corresponding to an interaction associated with a particular user of a plurality of users. The system may determine, based on the interaction data, one or more interaction categories associated with the interaction. The system may store the interaction data as historical interaction data associated with historical interactions of the particular user. The system may determine, based on the historical interaction data, experience statuses associated with the particular user in at least a subset of the plurality of categories. The system may receive, from a user device of the particular user, review data indicating a review associated with a historical interaction of the particular user. The system may store the review as historical review data. The review may be associated with one or more experience statuses of the particular user in the one or more interaction categories associated with the historical interaction.

Abstract: In some implementations, a transaction card includes a card body having a first surface and a second surface opposite the first surface, and the card body may be in a configuration that defines a storage area for a physical key. The transaction card may include at least one of an integrated circuit chip or a magnetic stripe accessible from at least one of the first surface or the second surface of the card body. The transaction card may include a connecting element to connect the physical key to the card body. The connecting element may be configured to enable movement of the physical key between a storage position, in which at least a portion of the physical key occupies the storage area, and a use position, in which at least a portion of the physical key is outside of the storage area.

Abstract: 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.

Abstract: Various embodiments are directed to a method and system for provisioning a merchant-specific payment apparatus, such as a temporary payment card, via a computing device, such as an automated teller machine (ATM). A user, such as a customer, may specify a merchant. A record of known payment acceptance types at that merchant may be returned, which may be used to determine what type of temporary payment card gets issued to the user.

Abstract: Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.

Abstract: Provided are approaches for customizing transaction cards using a pressure sensitive modification area. In some approaches, a transaction card may include a body having a first main side opposite a second main side, wherein a chip is coupled to the first main side of the body, wherein a first outer layer extends along the first main side, and wherein a second outer layer extends along the second main side. The transaction card may further include an internal layer between the first and second outer layers, and a plurality of microspheres and a pigmented substance positioned between the internal layer and the first outer layer.

Abstract: Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.

Abstract: In some implementations, a transaction card may include a card body having a first surface and a second surface opposite the first surface. The transaction card may include an integrated circuit (IC) chip embedded in the card body, and a plurality of first electrical contact sets electrically accessible from the first surface of the card body, where the plurality of first electrical contact sets have electrical connections to the IC chip. The transaction card may include a plurality of first magnetic stripe areas readable from the first surface of the card body. The transaction card may include a plurality of second electrical contact sets electrically accessible from the second surface of the card body, where the plurality of second electrical contact sets have electrical connections to the IC chip. The transaction card may include a plurality of second magnetic stripe areas readable from the second surface of the card body.