Utilizing a machine learning model for predicting issues associated with a closing process of an entity

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• identify tasks relevant to closing processes for an entity during a period of time;
• generate a request for historical closing data associated with the tasks relevant to the closing processes;
• provide the request to a server device associated with the entity;
• receive the historical closing data based on providing the request to the server device,
• wherein the historical closing data comprises information regarding historical issues that caused one or more historical tasks to be delayed including descriptions of historical reasons for historical delays and information identifying a measure of severity of the historical issues;
• identify a trend with respect to the one or more historical tasks based on sorting the one or more historical tasks based on amounts of delays associated with the historical delays;
• train a machine learning model, based on the historical closing data, to generate a trained machine learning model,
• wherein the machine learning model is trained to identify structural or organizational changes associated with reasons for the historic delays from the historical closing data, and
• wherein the machine learning model is trained to identify particular tasks that are associated with delays and particular periods of time associated with lengths of the delays;
• receive current closing data associated with tasks relevant to a current closing process;
• process the current closing data, with the trained machine learning model, to predict issue data identifying at least one potential issue associated with the current closing process;
• generate a recommendation based on the issue data;
• provide the recommendation to a user device associated with the at least one potential issue;
• receive, from the user device, feedback associated with the recommendation;
• generate a modification to the current closing process based on the issue data;
• and
• cause the server device to implement the modification to the current closing process;
• and
• retrain the machine learning model based on the feedback.

A device, comprising:

• one or more memories;
• and
• one or more processors, coupled to the one or more memories, configured to:
• identify tasks relevant to closing processes for an entity during a period of time;
• generate a request for historical closing data associated with the tasks relevant to the closing processes;
• provide the request to a server device associated with the entity;
• receive the historical closing data based on providing the request to the server device,
• wherein the historical closing data comprises information regarding historical issues that caused one or more historical tasks to be delayed including descriptions of historical reasons for historical delays and information identifying a measure of severity of the historical issues;
• identify a trend with respect to the one or more historical tasks based on sorting the one or more historical tasks based on amounts of delays associated with the historical delays;
• train a machine learning model, based on the historical closing data, to generate a trained machine learning model,
• wherein the machine learning model is trained to identify structural or organizational changes associated with reasons for the historic delays from the historical closing data, and
• wherein the machine learning model is trained to identify particular tasks that are associated with delays and particular periods of time associated with lengths of the delays;
• receive current closing data associated with tasks relevant to a current closing process;
• process the current closing data, with the trained machine learning model, to predict issue data identifying at least one potential issue associated with the current closing process;
• provide the issue data to a user device associated with the at least one potential issue;
• generate a modification to the current closing process based on the issue data;
• and
• cause the server device to implement the modification to the current closing process.