Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a machine learning model and a credit value model to generate user interface elements that present credit values and credit value conditions in real time for user accounts. For instance, the disclosed systems can generate an activity score using an activity machine learning model with internal user activity data of a user account. Then, utilizing a credit value model with the activity score and a user activity condition, the disclosed systems can determine a dynamic credit value range for the user account. Indeed, the disclosed systems can display user interface elements with selectable credit values from the dynamic credit value range. Additionally, the disclosed systems can utilize the credit value model to determine and display one or more dynamic credit value conditions for a selected credit value received from the selectable credit values.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that increase a base limit value for a contact based on a user interaction with a graphical user interface. Generally, the disclosed system provides a graphical user interface for display to a user that includes base limit increase elements to increase base limit values for contacts of the user. Based on user selection of a base limit increase element, the disclosed system can provide an increased base limit value to an account associated with the contact.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that increase a base limit value for a contact based on a user interaction with a graphical user interface. Generally, the disclosed system provides a graphical user interface for display to a user that includes base limit increase elements to increase base limit values for contacts of the user. Based on user selection of a base limit increase element, the disclosed system can provide an increased base limit value to an account associated with the contact.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a machine learning model and a credit value model to generate user interface elements that present credit values and credit value conditions in real time for user accounts. For instance, the disclosed systems can generate an activity score using an activity machine learning model with internal user activity data of a user account. Then, utilizing a credit value model with the activity score and a user activity condition, the disclosed systems can determine a dynamic credit value range for the user account. Indeed, the disclosed systems can display user interface elements with selectable credit values from the dynamic credit value range. Additionally, the disclosed systems can utilize the credit value model to determine and display one or more dynamic credit value conditions for a selected credit value received from the selectable credit values.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a machine learning model and a credit value model to generate user interface elements that present credit values and credit value conditions in real time for user accounts. For instance, the disclosed systems can generate an activity score using an activity machine learning model with internal user activity data of a user account. Then, utilizing a credit value model with the activity score and a user activity condition, the disclosed systems can determine a dynamic credit value range for the user account. Indeed, the disclosed systems can display user interface elements with selectable credit values from the dynamic credit value range. Additionally, the disclosed systems can utilize the credit value model to determine and display one or more dynamic credit value conditions for a selected credit value received from the selectable credit values.