A data interaction method, a verification terminal, a server, and a system are described. The method includes: receiving, by a verification terminal, identity verification information from a user terminal, the identity verification information being information sent to the user terminal by a server in advance; sending, by the verification terminal, a request instruction to the server, the request instruction including the identity verification information; executing, by the server, an operation corresponding to the request instruction; and sending, by the server, feedback information to the verification terminal.

Arrangements for automatically detecting bi-directional artificial intelligence (AI) communications and automatically negotiating (i.e., switching to alternative) direct digital communications.

Arrangements for automatically detecting bi-directional artificial intelligence (AI) communications and automatically negotiating (i.e., switching to alternative) direct digital communications.

An example method for delivery of push notifications includes receiving a push notification including a message and a destination, creating a send token, sending a push notification derived from the received push notification and the send token, and receiving push information concerning a processing of the sent push notification which is identified by the send token. An example system for delivering push notifications includes a server system having a processor, memory, and a network interface, where the memory stores program instructions including code segments for receiving a received push notification via the network interface. In this example, the program instructions further includes code segments for creating a send token, code segments for sending a sent push notification derived from the received push notification and the send token via the network interface, and code segments for receiving received push information concerning a processing of the sent push notification are provided.

An example method for delivery of push notifications includes receiving a push notification including a message and a destination, creating a send token, sending a push notification derived from the received push notification and the send token, and receiving push information concerning a processing of the sent push notification which is identified by the send token. An example system for delivering push notifications includes a server system having a processor, memory, and a network interface, where the memory stores program instructions including code segments for receiving a received push notification via the network interface. In this example, the program instructions further includes code segments for creating a send token, code segments for sending a sent push notification derived from the received push notification and the send token via the network interface, and code segments for receiving received push information concerning a processing of the sent push notification are provided.

Recommendation and approval of a header for a message includes generating a proposed header based on the name and/or brand of the entity and product and/or content of the message, classifying the proposed header using a machine learning model trained based on historical complaints on previously used headers related to the entity name and brand and product and/or content of the message and recommending the proposed header based on the classification. The training of the machine learning model may include learning a threshold wherein headers having a classification greater than the threshold are not recommended as having a high probability of being wrongly associated with the requesting entity and headers having a classification lower than the threshold are recommended as having a high probability of not being wrongly associated with the requesting entity.

Recommendation and approval of a header for a message includes generating a proposed header based on the name and/or brand of the entity and product and/or content of the message, classifying the proposed header using a machine learning model trained based on historical complaints on previously used headers related to the entity name and brand and product and/or content of the message and recommending the proposed header based on the classification. The training of the machine learning model may include learning a threshold wherein headers having a classification greater than the threshold are not recommended as having a high probability of being wrongly associated with the requesting entity and headers having a classification lower than the threshold are recommended as having a high probability of not being wrongly associated with the requesting entity.

A method for mobile credit acquisition is disclosed. The method deploys a shopping incentive offer, the shopping incentive offer for a shopping incentive. A device identifier associated with a user's mobile device is received in response to a user responding to the shopping incentive offer on the user's mobile device. In addition, a user identifier is received for the user. The device identifier and the user identifier are used to obtain user specific information useable for a credit prescreen. The shopping incentive is then provided to the user's mobile device. If the user does pass a credit prescreen, a pre-approved credit offer is also provided to the user via the user's mobile device in conjunction with the shopping incentive.

A method for mobile credit acquisition is disclosed. The method deploys a shopping incentive offer, the shopping incentive offer for a shopping incentive. A device identifier associated with a user's mobile device is received in response to a user responding to the shopping incentive offer on the user's mobile device. In addition, a user identifier is received for the user. The device identifier and the user identifier are used to obtain user specific information useable for a credit prescreen. The shopping incentive is then provided to the user's mobile device. If the user does pass a credit prescreen, a pre-approved credit offer is also provided to the user via the user's mobile device in conjunction with the shopping incentive.

One or more computing devices, systems, and/or methods for securely verifying devices such as protected are provided. A code may be generated for a first device. A short message service (SMS) message comprising the code may be transmitted to the first device at a mobile directory number of the first device. An entry may be created to associate the code with the mobile directory number. A determination may be made as to whether a first code within a message associated with the mobile directory number matches the code within the entry. In response to a match, the message may be processed and a status of the first device may be marked as valid, otherwise, the message may be rejected.