A method for electronic transaction monitoring and reporting includes: determining whether the received transaction request is encrypted, upon determining that the received transaction request is not encrypted, sending a failure alert to the merchant, determining a receiving acquirer processor for the transaction request, and transmitting the transaction request to the determined acquirer processor.

Systems, apparatuses, methods, and computer program products are disclosed for facilitating contactless and socially distant payments. The methods further correspond to receiving, in real-time, image data representative of at least a portion of a user body, the user body tracked by a contactless human to device interface system. The methods further include causing to display, by the contactless human to device interface system, a graphical user interface that permits the user to generate a new control event to map to the image data based, at least in part on a determination that the image data is not associated with a control event, generating an updated user expression map that identifies a mapping of the image data to the new control event, and storing the updated user expression map for access by the contactless human to device interface system.

A goods sensing system includes: a sample collector that collects a plurality of sets of image samples, where each set of the image samples comprise sample images of a type of goods at multiple angles, where a set of the image samples of a same type of goods are provided with a same group identification, and the group identification is the type of the goods corresponding to the set of image samples; a model trainer that trains a convolutional neural network model according to each sample image and a group identification of the sample image to obtain a goods identification model; a real-time image collector that continuously acquires at least one real-time image of space in front of a shelf, each real-time image including part or all of images of goods; and a goods category deriver that obtains a type and quantity of the goods displayed in the real-time.

Embodiments of the present invention provide a skip-scanning identification method and apparatus, a self-service cash register terminal and system. The method includes: obtaining a video of a user scanning an item; determining posture data of the user based on the obtained video; determining, according to the posture data of the user, a time period in which a scanning action of the user takes place; receiving a scanning result of the item; and determining whether the user has skipped scanning the item based on the scanning result and the time period.

Image analysis is performed on a user at a transaction terminal. Based on behaviors, expressions, and activities of the user, fraud or potential fraud is flagged. When fraud is flagged, the transaction terminal stops processing an active transaction on behalf of the user and alerts are sent. When potential fraud is flagged, images/video associated with the active transaction are sent for review and the active transaction may be suspended or permitted to proceed at the transaction terminal. In an embodiment, a same user conducting multiple transactions with different accounts at a same transaction terminal or multiple different transaction terminals within a configured period of time is automatically identified as fraud based on a fraud rule.

An item position tracking system includes weight sensors each associated with a weight board. Each weight sensor transmits sensor data indicative of a weight of an item to its corresponding weight board. Each weight board is configured to assign a particular address number to its corresponding weight sensor. The weight boards transmit the sensor data and the address numbers to a circuit board that transmits the sensor data and the address numbers to a weight server. The weight server determines from which weight sensor data is originated based on the address numbers, and whether items were removed from the weight sensors.

Performing point of sale transactions are performed by configuring a point of service (POS) environment with a sensor pod including an active sensor and a passive sensor. A first signal is sent from the active sensor of the sensor pod to a user device that is initiating a sale for a service. A converted first signal is received at the active sensor, wherein the converted version of the first signal includes identification information for the user. It can be determined that the active sensor of the sensor pod of the point of service environment has not functioned. A user wake up call is received from the user device at the passive sensor of the sensor pod. Functionality of the active sensor is restored with a POS wake up signal sent from the passive sensor to the active sensor.

A machine-learning algorithm is trained with features relevant to basket data for items of transactions. The trained algorithm is trained to predict whether a given transaction is more or less likely to be associated with theft being engaged in by a transaction operator for the transaction. The trained algorithm is then provided basket data for a given transaction and produces as output a theft prediction value. When the theft prediction value exceeds a configured threshold value, the transaction is flagged for manual intervention or the transaction is flagged for subsequent manual verification.

Image analysis is performed on a user at a transaction terminal. Based on behaviors, expressions, and activities of the user, fraud or potential fraud is flagged. When fraud is flagged, the transaction terminal stops processing an active transaction on behalf of the user and alerts are sent. When potential fraud is flagged, images/video associated with the active transaction are sent for review and the active transaction may be suspended or permitted to proceed at the transaction terminal. In an embodiment, a same user conducting multiple transactions with different accounts at a same transaction terminal or multiple different transaction terminals within a configured period of time is automatically identified as fraud based on a fraud rule.

Provided is a computer-implemented method for authenticating a transaction. The method includes associating, in at least one database, a plurality of voice identifiers with a plurality of users, each voice identifier of the plurality of voice identifiers corresponding to a user having at least one account identifier, receiving audio data comprising a spoken voice identifier, determining a detected voice identifier from the plurality of voice identifiers in the at least one database, the detected voice identifier matching the spoken voice identifier, determining a user from the plurality of users based at least partially on the detected voice identifier, in response to determining the user of the plurality of users, communicating a notification to a device associated with the user, the notification comprising transaction data associated with the transaction, and in response to receiving an authentication signal from the device associated with the user, conducting the transaction.