Machine learning techniques to predict task event

A computing system comprising:

• a processor;
• and
• a memory coupled to the one or more processors, the memory storing a plurality of instructions executable by the one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform processing comprising:
• obtaining initial data for a service request, wherein the initial data comprises basic information captured as part of a service initiation including profile data for a user originating the service request, the service request, one or more products pertaining to the service request, and characteristics associated with the one or more products;
• enriching the initial data with data from one or more repositories of an enterprise executing the service request, wherein the enriching comprises augmenting the initial data with the data from the one or more repositories to generate enriched data, and wherein the data comprises a workflow for each of the one or more products, stages of each workflow, and data segments defined for the stages;
• generating a data structure comprising independent variables extracted from the enriched data, wherein the independent variables include a just-in-time position of the service request within each workflow;
• receiving a request for a prediction of a completion time for the service request at a first time during processing of the service request in accordance with each workflow;
• in response to receiving the request for the prediction, inputting the data structure into a machine-learning regression model;
• predicting, using the machine-learning regression model, a completion time for the service request, wherein the predicting comprises determining a relationship between the independent values and the completion time of the service request;
• and
• providing the completion time for the service request.

A non-transitory computer-readable memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform processing comprising:

• obtaining initial data for a service request, wherein the initial data comprises basic information captured as part of a service initiation including profile data for a user originating the service request, the service request, one or more products pertaining to the service request, and characteristics associated with the one or more products;
• enriching the initial data with data from one or more repositories of an enterprise executing the service request, wherein the enriching comprises augmenting the initial data with the data from the one or more repositories to generate enriched data, and wherein the data comprises a workflow for each of the one or more products, stages of each workflow, and data segments defined for the stages;
• generating a data structure comprising independent variables extracted from the enriched data, wherein the independent variables include a just-in-time position of the service request within each workflow;
• receiving a request for a prediction of a completion time for the service request at a first time during processing of the service request in accordance with each workflow;
• in response to receiving the request for the prediction, inputting the data structure into a machine-learning regression model;
• predicting, using the machine-learning regression model, a completion time for the service request, wherein the predicting comprises determining a relationship between the independent values and the completion time of the service request;
• and
• providing the completion time for the service request.