A technique for remote access via a computing device is discussed. In one embodiment, a computing device receives data indicative of a card number associated with a bank-issued debit card. The debit card is associated with a bank account at a bank that issued the bank-issued debit card. The computing device also receives data indicative of an amount of cash to be deposited into the account based on an amount of cash provided at the computing device location. Encrypted data indicative of the card number and the data indicative of the amount of cash is routed to a gateway vendor server and is further routed to a payment network server for deposit of an amount corresponding to the data indicative of the amount of cash into the bank account associated with the card number.

A method comprises receiving, by a computing device from a customer device, a request to provision a financial account for the customer, associating, by the computing device, a mobile device with the requested financial account, and sending, by the computing device to the mobile device, authentication information for the requested financial account. The method further includes receiving the authentication information by the computing device from a financial terminal that received the authentication information from the mobile device, receiving, by the computing device, first location information of the mobile device, and provisioning, by the computing device and based on the first location information of the mobile device being within a predetermined distance of a location of the financial terminal, the requested financial account for the customer.

A method may include receiving, via a processor, a request for payment of a payment amount from a first computing system. The method may also involve receiving a location of the first computing system, identifying one or more automatic teller machines (ATMs) based on the location of the first computing system, generating an image configured to cause the one or more ATMs to dispense funds that correspond to the payment amount, and sending the image to the first computing system.

A queue management ultra-wideband (UWB) automated teller machine (ATM) transaction pre-staging systems and methods for use with an ATM server, at least one UWB-enabled wireless portable electronic device, and a plurality of UWB-enabled ATMs are disclosed. Transactions are pre-staged by the UWB-enabled devices with the ATM servers. Queue wait times at each UWB-enabled ATM can be determined based on how many transactions have been scheduled for the ATM, how many customers are currently waiting at the ATM, and what currency resources are required by the pre-staged transactions. Geographical distances between the user and the ATMs can be determined. Based on historical customer ATM usage preferences, the distance to various ATMs, and queue wait times, the ATM server may make recommendations regarding which ATM would be optimal for the customer to use to complete the pre-staged transaction.

An automated transaction machine can include a user-interface device, a computing device, a battery, a head portion, a first socket-half, a first electrical rail, and a relay. The a user-interface device can be configured to receive an input from a user and transmit the input. The computing device can receive the input. The battery can selectively direct electric power to the user-interface device and the computing device, which can all be housed in the head portion. The first socket-half can be exposed on an outside surface of the head portion and direct power to the user-interface device, the computing device, and the battery. The relay can be arranged to connect the battery and the first electrical rail in a first configuration and be switched between the first configuration and the second configuration by an output signal of the first socket-half.

Examples described herein relate to apparatuses and methods of detecting fraudulent activity at an automated teller machine (ATM) using a machine learning model. A method includes receiving ATM activity data indicative of one or more withdrawal transactions at one or more ATMs using a transaction card, receiving transaction data and ATM data, ingesting the transaction data and the ATM data, analyzing the ingested transaction data and the ingested ATM data using a machine learning model, determining that the ingested transaction data and the ingested ATM data indicate fraudulent activity using the machine learning model, and performing one or more remedial actions based on the determination of fraudulent activity using the machine learning model.

A method for use with a smart card is provided. The smart card may include a communication interface, a housing, a microprocessor embedded in the housing, a battery for powering the communication interface and the microprocessor, and a nano GPS chip, located within the housing and in electronic communication with the communication interface and the microprocessor. The thickness of the smart card is preferably no greater than 0.8 millimeters (“mm”) and has surface area not greater than 86 mm×54 mm. The method may include determining the location of the smart card in response to a request for a smart card transaction authorization and determining the location of a mobile device associated with a specified user of the smart card. Based on the location of the smart card and the location of the mobile device, the smart card may calculate a location delta between the smart card and the mobile device. If the location delta is greater than or equal to a threshold distance, the smart card may deny the request for smart card transaction authorization.

A method includes receiving a transaction request at a transaction machine, the transaction request being associated with an account holder, determining a weather status for the transaction machine, determining screen content for the transaction machine based on the weather status, and displaying the screen content to the account holder via a display screen of the transaction machine.

The present disclosure relates to system and methods for predicting performance caused by software code changes. For this purpose, an augmented machine learning model predicts a latency of software module with updated code executed in a production environment. In some aspects, the latency is predicted based on a change of deviation that is determined by comparing the latency of the software module with updated code and the latency of the software module without updated code, whereas the software modules are executed in environments different from the production environment.

Systems and methods are provided to enable users to interact with user terminals having a touch screen interface without requiring the user to physically contact a surface of the touch screen interface.