A device may detect a first entry of a first user device of a first user into a merchant area. The device may monitor a movement of the first user device within the merchant area. The movement may include a transition from a shopping area of the merchant area to a checkout area of the merchant area. The device may detect a transaction between the first user and the merchant. The device may determine a shopping duration for the first user and a checkout duration for the first user. The device may detect a second entry of a second user device of a second user into the merchant area. The device may perform one or more actions based on detecting the second entry. The one or more actions may be performed selectively based on the shopping duration or the checkout duration of the first user.

Electronic transaction systems and methods are operable to conduct transaction verification and authorization process for an electronic transaction. An exemplary embodiment determines location of a personal electronic device of a consumer during an electronic transaction; determines location of an electronic transaction device that the consumer is using to conduct the electronic transaction; determines a distance between the personal electronic device of the consumer and the electronic transaction device; compares the determined distance with a predefined distance; and verifies and authorizes the electronic transaction when the determined distance less than or equal to the predefined distance.

According to one aspect of the present disclosure, a method for improved reliability in a bank computer network can include: identifying an ATM proximate to a user device; determining whether the user device is communicably coupled to a server device over a secure link, the server device hosted by a financial institution and capable processing ATM transactions; establishing a secure link with the server device in response to determining the user device is not communicably coupled with the server device; prompting a user for permission to tether with the ATM in response to determining the user device is communicably coupled with the server device or determining the secure link has been established; receiving an indication of a response to the prompt for permission to tether with ATM; and establishing a P2P connection between the ATM and the user device.

In certain embodiments, an online transaction is verified based on merchant-independent user geolocation. A geolocation of a user associated with the online transaction is obtained from a mobile device of the user by a transaction verifying entity separate and independent from a merchant entity with which the online transaction is initiated. A server associated with the transaction verifying entity determines a proximity of the geolocation of the user to a delivery address for verifying the online transaction. The server generates a verification message including an approval message based on the proximity of the geolocation to the delivery address being within a specified threshold. The server generates a verification message including a rejection message based on the proximity of the geolocation to a delivery address exceeding the specified threshold. The server sends the verification message to another server associated with the merchant entity to carry out the online transaction accordingly.

A mobile payment system and method enable anonymous and secure transfer of money between users. To anonymously transfer money, a first mobile device receives an input from a first user to transfer an amount of money to a second user. The first mobile device communicates directly with the second mobile device to send a unique tokenized transaction identifier to the second mobile device while preserving anonymity of the first user. Using the tokenized transaction identifier, the payment server is configured to cause the transfer of the indicated amount of money between a financial account associated with the first user and a financial account associated with the second user.

An in-application content transfer system that generates receiver IDs to categorize a given exchange between two users where one does not execute a local instance of the application. One user executes the application on their mobile device and makes use of near-field communication protocols with a neighboring mobile device that is not executing the subject application. The content transfer is linked to the receiver's phone number and a unique ID is assigned to the content transfer. The receiving user is made aware of the content transfer via a push notification received via near field communication or via an SMS text message. The received message or notification enables the user to retrieve the content from the transfer at a later time.

A method of facilitating the processing of payments in a location-based sales venue such as a restaurant, comprising a plurality of customer locations for service. Each customer location has an identity target device associated therewith and attached in proximity thereto, comprising a network-connected electronic device capable of display or broadcast of a one-time identifier associated with the corresponding customer location to proximate mobile customer devices. On reading of a target by a mobile customer device and a related transmission to the server, the server can associate mobile customer device with its related customer location in the venue-associated POS system, facilitate a payment transaction for the amount owing at the selected customer location through a transaction gateway, and provide a transaction completion indication to the POS system. Following the completion of the payment transaction, the display of the associated identity target device would be updated to remove the expended one-time identifier and a new one-time identifier would be generated and displayed in advance of a new payment transaction in respect of the location. Dynamically generated location one-time identifiers provide maximum flexibility in system configuration, as well as maximized security for users.

A method may include receiving, at a computing device, a plurality of past transactions associated with a user account of a user; extracting transaction characteristics of a transaction of the plurality of transactions, the transaction characteristics including a location characteristic and a time characteristic; retrieving, based on the transaction characteristics, characteristics of the computing device at a time according to the time characteristic; comparing the transaction characteristics with the retrieved computing device characteristics based on a set of rules to determine a likelihood that the transaction is anomalous; and based on the likelihood indicating that the transaction is anomalous, presenting a notification to the user.

A system for monitoring shopping carts uses cameras to generate images of the carts moving in a store. In some implementations, cameras may additionally or alternatively be mounted to the shopping carts and configured to image cart contents. The system may use the collected image data, and/or other types of sensor data (such as the store location at which an item was added to the basket), to classify items detected in the shopping carts. For example, a trained machine learning model may classify item in a cart as “non-merchandise,” “high theft risk merchandise,” “electronics merchandise,” etc. When a shopping cart approaches a store exit without any indication of an associated payment transaction, the system may use the associated item classification data, optionally in combination with other data such as cart path data, to determine whether to execute an anti-theft action, such as locking a cart wheel or activating a store alarm. The system may also compare the classifications of cart contents to payment transaction records (or summaries thereof) to, e.g., detect underpayment events.

A system for monitoring shopping carts uses cameras to generate images of the carts moving in a store. In some implementations, cameras may additionally or alternatively be mounted to the shopping carts and configured to image cart contents. The system may use the collected image data, and/or other types of sensor data (such as the store location at which an item was added to the basket), to classify items detected in the shopping carts. For example, a trained machine learning model may classify item in a cart as “non-merchandise,” “high theft risk merchandise,” “electronics merchandise,” etc. When a shopping cart approaches a store exit without any indication of an associated payment transaction, the system may use the associated item classification data, optionally in combination with other data such as cart path data, to determine whether to execute an anti-theft action, such as locking a cart wheel or activating a store alarm. The system may also compare the classifications of cart contents to payment transaction records (or summaries thereof) to, e.g., detect underpayment events.