Chime Financial, Inc.

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that dynamically surface multiple user-specific application function GUIs for setting up (or accessing) application functions for a user account. For instance, the disclosed systems can select and display a GUI for a first application function from a set of application functions. Moreover, upon receiving user interactions with the GUI for the first application function (and identifying whether or not the first application system is activated for the user account), the disclosed systems can select a second application function from the set of application functions and displays a GUI (e.g., a setup interface) for the second application function from the set of application functions. In some implementations, the disclosed systems can utilize computer-based models to determine user account specific application function metrics and intelligently select and surface application functions using the application function metrics.

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media dynamically track, utilize, and display one or more base limit value modifiers to modify base limit values for user accounts, determined utilizing a variety of machine learning models and a base limit value model. For instance, the disclosed systems can utilize a variety of machine learning models and a base limit value model to generate a base limit value for a user account. Moreover, the disclosed systems can detect digital user account actions and generate base limit value modifiers for the digital user account actions. Furthermore, the disclosed systems can dynamically determine and display, utilizing a total base limit value modifier threshold, user interface elements indicating active base limit value modifiers that modify a displayed base limit value and/or inactive base limit value modifiers that do not affect the base limit value.

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that dynamically surface multiple user-specific application function GUIs for setting up (or accessing) application functions for a user account. For instance, the disclosed systems can select and display a GUI for a first application function from a set of application functions. Moreover, upon receiving user interactions with the GUI for the first application function (and identifying whether or not the first application system is activated for the user account), the disclosed systems can select a second application function from the set of application functions and displays a GUI (e.g., a setup interface) for the second application function from the set of application functions. In some implementations, the disclosed systems can utilize computer-based models to determine user account specific application function metrics and intelligently select and surface application functions using the application function metrics.

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a variety of machine learning models and a base limit value model to generate user interface elements that transparently and efficiently present current and future base limit values for user accounts. For example, the disclosed systems can utilize a machine learning model to determine a base limit value, subsequent base limit value, and user activity conditions to achieve the subsequent base limit value for a user account. Then, the disclosed systems can display a base limit progress element that indicates progress towards fulfilling the user activity conditions to achieve the subsequent base limit value. For example, the disclosed systems can display, within a graphical user interface, multiple base limit progress elements that indicate progress towards fulfilling the user activity conditions in separate time-based segments (e.g., to represent time elements within the user activity conditions).

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media dynamically track, utilize, and display one or more base limit value modifiers to modify base limit values for user accounts, determined utilizing a variety of machine learning models and a base limit value model. For instance, the disclosed systems can utilize a variety of machine learning models and a base limit value model to generate a base limit value for a user account. Moreover, the disclosed systems can detect digital user account actions and generate base limit value modifiers for the digital user account actions. Furthermore, the disclosed systems can dynamically determine and display, utilizing a total base limit value modifier threshold, user interface elements indicating active base limit value modifiers that modify a displayed base limit value and/or inactive base limit value modifiers that do not affect the base limit value.

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a variety of machine learning models and a base limit value model to generate user interface elements that transparently and efficiently present current and future base limit values for user accounts. For example, the disclosed systems can utilize a machine learning model to determine a base limit value, subsequent base limit value, and user activity conditions to achieve the subsequent base limit value for a user account. Then, the disclosed systems can display a base limit progress element that indicates progress towards fulfilling the user activity conditions to achieve the subsequent base limit value. For example, the disclosed systems can display, within a graphical user interface, multiple base limit progress elements that indicate progress towards fulfilling the user activity conditions in separate time-based segments (e.g., to represent time elements within the user activity conditions).

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that enable network transactions between user accounts belonging to different network transaction platforms and unify user account modifications across user accounts on different network transaction platforms. For instance, the disclosed systems can cause two or more network transaction platforms to transmit and/or receive transactional values from one or more intermediary holding accounts upon receiving network transaction requests between user accounts belonging to the two or more network transaction platforms. In addition, the disclosed system can unify user account data on different network transaction platforms by modifying or updating the user account data (e.g., user attributes) by propagating schematized data messages to the one or more other network transaction platforms with instructions to modify or update the user attributes on other user accounts related to the user account.

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a variety of machine learning models and a base limit value model to generate user interface elements that transparently and efficiently present current and future base limit values for user accounts. For example, the disclosed systems can utilize a machine learning model to determine a base limit value, subsequent base limit value, and user activity conditions to achieve the subsequent base limit value for a user account. Then, the disclosed systems can display a base limit progress element that indicates progress towards fulfilling the user activity conditions to achieve the subsequent base limit value. For example, the disclosed systems can display, within a graphical user interface, multiple base limit progress elements that indicate progress towards fulfilling the user activity conditions in separate time-based segments (e.g., to represent time elements within the user activity conditions).

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that dynamically surface multiple user-specific application function GUIs for setting up (or accessing) application functions for a user account. For instance, the disclosed systems can select and display a GUI for a first application function from a set of application functions. Moreover, upon receiving user interactions with the GUI for the first application function (and identifying whether or not the first application system is activated for the user account), the disclosed systems can select a second application function from the set of application functions and displays a GUI (e.g., a setup interface) for the second application function from the set of application functions. In some implementations, the disclosed systems can utilize computer-based models to determine user account specific application function metrics and intelligently select and surface application functions using the application function metrics.

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that enable network transactions between user accounts belonging to different network transaction platforms and unify user account modifications across user accounts on different network transaction platforms. For instance, the disclosed systems can cause two or more network transaction platforms to transmit and/or receive transactional values from one or more intermediary holding accounts upon receiving network transaction requests between user accounts belonging to the two or more network transaction platforms. In addition, the disclosed system can unify user account data on different network transaction platforms by modifying or updating the user account data (e.g., user attributes) by propagating schematized data messages to the one or more other network transaction platforms with instructions to modify or update the user attributes on other user accounts related to the user account.