Abstract: Distributing machine learning models to electronic devices is provided. A number of machine learning models are built and stored on a server, wherein each model is customized to different device and user requirements. A request for a machine learning model is received from an electronic device, wherein the request includes a device profile comprising device attributes and user characteristics. The server determines which machine learning model from among the number of models is best suited for the device profile according to a set of model policies. The machine learning model best suited for the device profile is selected and downloaded to the device.

Abstract: Provided are techniques for synchronizing a device using push notifications. For each of a plurality of messages, a message record is created in a table of a database with a message create time for a message, a device identifier of a device to receive the message, an application identifier of an application on the device to receive the message, message content of the message, and an indicator to indicate whether the message has been picked up by the device. A message is selected from the plurality of messages. It is determined that a message retry count for the message has not been met and the message has not been picked up by the device. It is determined that it is time to retry sending a silent push notification based on a retry interval. The silent push notification is sent to the application on the device to wake up the application.

Abstract: Distributing machine learning models to electronic devices is provided. A number of machine learning models are built and stored on a server, wherein each model is customized to different device and user requirements. A request for a machine learning model is received from an electronic device, wherein the request includes a device profile comprising device attributes and user characteristics. The server determines which machine learning model from among the number of models is best suited for the device profile according to a set of model policies. The machine learning model best suited for the device profile is selected and downloaded to the device.

Abstract: Provided are techniques for synchronizing a device using push notifications. For each of a plurality of messages, a message record is created in a table of a database with a message create time for a message, a device identifier of a device to receive the message, an application identifier of an application on the device to receive the message, message content of the message, and an indicator to indicate whether the message has been picked up by the device. A message is selected from the plurality of messages. It is determined that a message retry count for the message has not been met and the message has not been picked up by the device. It is determined that it is time to retry sending a silent push notification based on a retry interval. The silent push notification is sent to the application on the device to wake up the application.

Abstract: Gathering and verifying customer information via SMS and in-person representatives. A hash is received with customer account information from a business correspondent application, wherein the account information is given in-person to a business correspondent, the business correspondent enters the account information into the business correspondent app, the account information is verified by the business correspondent after entering the account information in the business correspondent app, and the hash is generated by the business correspondent app after the business correspondent app receives confirmation of verification of the account information from the business correspondent. An account number is generated for the customer. The account number is sent to the business correspondent app and to the customer. An enrollment PIN is sent to the customer, wherein the enrollment PIN is linked to the account number.

Abstract: Gathering and verifying customer information via SMS and in-person representatives. Customer account information is received, wherein the customer account information is given to a business correspondent by a customer. A verification of the customer account information is received, wherein the verification is performed by the business correspondent. A hash is generated with the customer account information. The hash is sent to a data center. An account number is received from the data center. An enrollment PIN is received, wherein the enrollment PIN is sent from the data center to the customer, the customer gives the enrollment PIN to the business correspondent, and the business correspondent enters the enrollment PIN into the business correspondent app, and wherein the enrollment PIN is linked to the account number. The data center is synced after receiving the enrollment PIN.

Abstract: An approach is provided for tuning middleware. Performance-related settings are loaded. Performance data of the middleware of a MobileFirst Platform (MFP) running in a docker container infrastructure is received. The performance data is collected by agents installed in container groups. Based on the performance data, a performance issue in one of the container groups is identified and a server included in the one container group is identified as a source of the performance issue. Recommendations are generated for tuning the middleware by modifying one or more of the performance-related settings. While the middleware is running in the docker container infrastructure, one of the recommendations is applied to modify one of the performance-related settings which dynamically tunes the middleware, thereby resolving the performance issue.

Abstract: In an approach for managing updates of a mobile software application, a computer receives information regarding a mobile computing device and a mobile software application on the mobile computing device, wherein the information regarding the mobile computing device comprises at least one or more of a location of the mobile computing device, a network connection type, a security bit associated with the mobile computing device, and a checksum associated with the at least one mobile software application. The computer determines whether the mobile software application matches a stored mobile software application on a computing device. Responsive to determining that the mobile software application does not match the stored mobile software application on the computing device, the computer determines an update policy for scheduling an update to the mobile software application on the mobile computing device.

Abstract: Gathering and verifying customer information via SMS and in-person representatives. A hash is received with customer account information from a business correspondent application, wherein the account information is given in-person to a business correspondent, the business correspondent enters the account information into the business correspondent app, the account information is verified by the business correspondent after entering the account information in the business correspondent app, and the hash is generated by the business correspondent app after the business correspondent app receives confirmation of verification of the account information from the business correspondent. An account number is generated for the customer. The account number is sent to the business correspondent app and to the customer. An enrollment PIN is sent to the customer, wherein the enrollment PIN is linked to the account number.

Abstract: Gathering and verifying customer information via SMS and in-person representatives. Customer account information is received, wherein the customer account information is given to a business correspondent by a customer. A verification of the customer account information is received, wherein the verification is performed by the business correspondent. A hash is generated with the customer account information. The hash is sent to a data center. An account number is received from the data center. An enrollment PIN is received, wherein the enrollment PIN is sent from the data center to the customer, the customer gives the enrollment PIN to the business correspondent, and the business correspondent enters the enrollment PIN into the business correspondent app, and wherein the enrollment PIN is linked to the account number. The data center is synced after receiving the enrollment PIN.

Abstract: A method for gathering and verifying customer information via SMS and in-person representatives are provided. The method may include a business correspondent receiving and verifying customer information in-person; sending the customer information to a data center, via a business correspondent application (BC app), to create a customer account; sending the account information to the customer and the BC app; verifying the account information with the customer; and syncing the BC app with the data center.

Abstract: In an approach for managing updates of a mobile software application, a computer receives information regarding a mobile computing device and a mobile software application on the mobile computing device, wherein the information regarding the mobile computing device comprises at least one or more of a location of the mobile computing device, a network connection type, a security bit associated with the mobile computing device, and a checksum associated with the at least one mobile software application. The computer determines whether the mobile software application matches a stored mobile software application on a computing device. Responsive to determining that the mobile software application does not match the stored mobile software application on the computing device, the computer determines an update policy for scheduling an update to the mobile software application on the mobile computing device.

Abstract: An approach is provided for tuning middleware. Performance-related settings are loaded. Performance data of the middleware of a MobileFirst Platform (MFP) running in a docker container infrastructure is received. The performance data is collected by agents installed in container groups. Based on the performance data, a performance issue in one of the container groups is identified and a server included in the one container group is identified as a source of the performance issue. Recommendations are generated for tuning the middleware by modifying one or more of the performance-related settings. While the middleware is running in the docker container infrastructure, one of the recommendations is applied to modify one of the performance-related settings which dynamically tunes the middleware, thereby resolving the performance issue.

Abstract: A method, system, and/or computer program product downloads a blob of data onto a mobile device. A blob of data that is to be downloaded from a server to a mobile device via a particular network is identified. A first maximum chunk size for the first network for the particular network is identified, as is a timeout value for the mobile device when downloading a chunk of data. An optimal chunk size for chunking the blob of data is determined based on the timeout value of the mobile device and the maximum chunk size for the first network. The blob of data is partitioned into chunks of data based on the first optimal chunk size, and the chunks of data are transmitted from the server to the mobile device.

Abstract: A method for gathering and verifying customer information via SMS and in-person representatives are provided. The method may include a business correspondent receiving and verifying customer information in-person; sending the customer information to a data center, via a business correspondent application (BC app), to create a customer account; sending the account information to the customer and the BC app; verifying the account information with the customer; and syncing the BC app with the data center.

Abstract: A method, system, and/or computer program product downloads a blob of data onto a mobile device. A blob of data that is to be downloaded from a server to a mobile device via a particular network is identified. A first maximum chunk size for the first network for the particular network is identified, as is a timeout value for the mobile device when downloading a chunk of data. An optimal chunk size for chunking the blob of data is determined based on the timeout value of the mobile device and the maximum chunk size for the first network. The blob of data is partitioned into chunks of data based on the first optimal chunk size, and the chunks of data are transmitted from the server to the mobile device.

Abstract: In an approach for managing updates of a mobile software application, a computer receives information regarding a mobile computing device and a mobile software application on the mobile computing device, wherein the information regarding the mobile computing device comprises at least one or more of a location of the mobile computing device, a network connection type, a security bit associated with the mobile computing device, and a checksum associated with the at least one mobile software application. The computer determines whether the mobile software application matches a stored mobile software application on a computing device. Responsive to determining that the mobile software application does not match the stored mobile software application on the computing device, the computer determines an update policy for scheduling an update to the mobile software application on the mobile computing device.

Abstract: In an approach for managing updates of a mobile software application, a computer receives information regarding a mobile computing device and a mobile software application on the mobile computing device, wherein the information regarding the mobile computing device comprises at least one or more of a location of the mobile computing device, a network connection type, a security bit associated with the mobile computing device, and a checksum associated with the at least one mobile software application. The computer determines whether the mobile software application matches a stored mobile software application on a computing device. Responsive to determining that the mobile software application does not match the stored mobile software application on the computing device, the computer determines an update policy for scheduling an update to the mobile software application on the mobile computing device.

Abstract: A method, system, and/or computer program product downloads a blob of data onto a mobile device. A blob of data that is to be downloaded from a server to a mobile device via a particular network is identified. A first maximum chunk size for the first network for the particular network is identified, as is a timeout value for the mobile device when downloading a chunk of data. An optimal chunk size for chunking the blob of data is determined based on the timeout value of the mobile device and the maximum chunk size for the first network. The blob of data is partitioned into chunks of data based on the first optimal chunk size, and the chunks of data are transmitted from the server to the mobile device.

Abstract: A method, system, and/or computer program product downloads a blob of data onto a mobile device. A blob of data that is to be downloaded from a server to a mobile device via a particular network is identified. A first maximum chunk size for the first network for the particular network is identified, as is a timeout value for the mobile device when downloading a chunk of data. An optimal chunk size for chunking the blob of data is determined based on the timeout value of the mobile device and the maximum chunk size for the first network. The blob of data is partitioned into chunks of data based on the first optimal chunk size, and the chunks of data are transmitted from the server to the mobile device.