Generating dynamic base limit value user interface elements determined from a base limit value model

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computing device to:

• determine a base limit value for a user account indicating an excess utilization buffer for the user account by:
• determining that the user account corresponds to a first user account grouping from a set of different groupings of user accounts;
• selecting, based on the user account corresponding to the first user account grouping, an activity machine learning model from a set of activity machine learning models, wherein each of the set of activity machine learning models comprise learned parameters specific to different sets of user activity training data from the set of different groupings of user accounts;
• generating an activity score by utilizing the activity machine learning model to analyze user activity data of the user account;
• and
• utilizing the activity score with a base limit value model to determine the base limit value;
• determine a subsequent base limit value indicating an additional excess utilization buffer achievable for the user account and one or more user activity conditions to achieve the subsequent base limit value from the base limit value model utilizing the base limit value and the activity score;
• provide, for display within a graphical user interface of a computing device corresponding to the user account, a first base limit progress element that indicates progress of one or more user activities toward fulfilling the one or more user activity conditions;
• and
• upon determining partial fulfillment of the one or more user activity conditions to achieve the subsequent base limit value from the base limit value model, modify the first base limit progress element to reflect the partial fulfillment.

A system comprising:

• at least one processor;
• and
• at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
• determine a base limit value for a user account indicating an excess utilization buffer for the user account by:
• determining that the user account corresponds to a first user account grouping from a set of different groupings of user accounts;
• selecting, based on the user account corresponding to the first user account grouping, an activity machine learning model from a set of activity machine learning models, wherein each of the set of activity machine learning models comprise learned parameters specific to different sets of user activity training data from the set of different groupings of user accounts;
• generating an activity score by utilizing the activity machine learning model to analyze user activity data of the user account;
• and
• utilizing the activity score with a base limit value model to determine the base limit value;
• determine a subsequent base limit value indicating an additional excess utilization buffer achievable for the user account and one or more user activity conditions to achieve the subsequent base limit value from the base limit value model utilizing the base limit value and the activity score;
• provide, for display within a graphical user interface of a computing device corresponding to the user account, a first base limit progress element that indicates progress of one or more user activities toward fulfilling the one or more user activity conditions;
• and
• upon determining partial fulfillment of the one or more user activity conditions to achieve the subsequent base limit value from the base limit value model, modify the first base limit progress element to reflect the partial fulfillment.