Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: A mobile electronic device detects a first virtual Bluetooth low energy (vBLE) signal from a first vBLE transceiver associated with a first merchant transaction device and a second vBLE signal from a second vBLE transceiver associated with a second merchant transaction device. An overlap region is determined including a first region corresponding to the first vBLE signal and a second region corresponding to the second vBLE signal. A checkout area is selected corresponding to the first region in response to receiving, from the first merchant transaction device, an indication of an external device receiving the second vBLE signal but not receiving the first vBLE signal. The mobile electronic device is then paired to the first merchant transaction device and a payment transaction is initiated with the first merchant transaction device.

Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: Examples provide pairing of a mobile electronic device with a merchant transaction device associated with a checkout area at a merchant facility. A plurality of advertising packets are received by the mobile electronic device. Each advertising packet of the plurality of advertising packets is associated with an individual beacon transceiver of a plurality of beacon transceivers. A checkout area of a plurality of checkout areas is identified within a beacon zone defined by an analysis of at least one parameter associated with the received advertising packets. A merchant pairing screen is generated at the mobile electronic device to pair the mobile electronic device to a merchant transaction device at the checkout lane of the plurality of checkout lanes. A pairing process for customer check-in to pick up an item is also provided in some examples.