Abstract: Systems for predicting features to be accessed by a user and generating a customized user interface are provided. In some examples, a computing platform may receive a request to access a system. In some examples, a content data stream may be received including data associated with the identity of the user, current date and time information, and the like. Data may be extracted from the content stream and analyzed, based on one or more machine learning datasets (generated internally or received from an external source), to predict a likely function or feature the user may access. In some examples, access to other features may be disabled. Responsive to identifying the likely feature, the system may enable access to the predicted feature and may generate a customized user interface including the predicted feature. The customized and dynamic user interface may include and place the predicted feature in a predetermined location on the user interface, in a size and/or format other than standard.

Abstract: Aspects of the disclosure relate to implementing and using a data processing system with a machine learning engine to provide automated message management functions. In some embodiments, a computing platform having at least one processor, a memory, and a communication interface that may receive, via the communication interface, a plurality of messages corresponding to a messaging account. Next, the computing platform may monitor, via the communication interface, one or more user interactions with the plurality of messages. Subsequently, the computing platform may receive, via the communication interface, a new message. Responsive to receiving the new message, the computing platform may determine, based at least in part on the one or more user interactions with the plurality of messages, an opportunity to perform an automated message management action associated with the new message. Subsequently, the computing platform may perform the automated message management action.

Abstract: Systems for predicting features to be accessed by a user and generating a customized user interface are provided. In some examples, a computing platform may receive a request to access a system. In some examples, a content data stream may be received including data associated with the identity of the user, current date and time information, and the like. Data may be extracted from the content stream and analyzed, based on one or more machine learning datasets (generated internally or received from an external source), to predict a likely function or feature the user may access. In some examples, access to other features may be disabled. Responsive to identifying the likely feature, the system may enable access to the predicted feature and may generate a customized user interface including the predicted feature. The customized and dynamic user interface may include and place the predicted feature in a predetermined location on the user interface, in a size and/or format other than standard.

Abstract: A system and method of determining a recommended location for performing a work function is provided. In some examples, the system and method may include receiving a plurality of process criteria, such as hours of operation associated with the work function, type of work performed, skill set desired, language skills desired, and the like. The system and method may further include receiving a plurality of provider attributes, such as languages spoken, knowledge base/skill set, flexibility of staffing, and the like. One or more recommended locations for performing the work function may be determined based on the business and provider attributes.

Abstract: A system and method of determining a recommended location for performing a work function is provided. In some examples, the system and method may include receiving a plurality of process criteria, such as hours of operation associated with the work function, type of work performed, skill set desired, language skills desired, etc. The system and method may further include receiving a plurality of provider attributes, such as languages spoken, knowledge base/skill set, flexibility of staffing, etc. One or more recommended locations for performing the work function may be determined based on the business and provider attributes.

Abstract: A system and method of identifying one or more locations eligible to perform a work function based on one or more filtering parameters is provided. The system and method may include receiving a first plurality of locations and filtering the first plurality of locations to identify a second plurality of locations eligible to perform an identified work function based on a first filtering parameter. The second plurality of locations may be further filtered to determine a third plurality of eligible locations, the third plurality of eligible locations being identified based on a second filtering parameter. In some examples, the third plurality of locations may be further filtered to identify a fourth plurality of eligible locations, the fourth plurality of eligible locations being identified based on a third filtering parameter.

Abstract: A system and method of calculating a concentration risk score for a work process at an organization center are provided. The concentration risk score may be based on a redundancy score and a criticality score. In some examples, the redundancy score may be determined based on a percentage of total resources associated with the work process at a first center. The redundancy score and criticality score may then be combined to determine a concentration risk score for the work process at that center.

Abstract: Systems and methods are disclosed for assessing a country as a relocation option. More specifically, a line of business may assess a country by generating a geographic model that may be based on data about a country. The system and method of generating a geographic model may include the generation of a reward score based on reward drivers may also generate a risk score that is based on risk factors. The reward score the risk score may be compared. The line of business may also conduct an in-country/due diligence visit based at least in part on the geographic model and may assemble a risk mitigation framework.