Abstract: 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.

Abstract: 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.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a deposit transaction predictor model to facilitate downstream access to deposit transaction prediction data through a data pipeline. For instance, the disclosed systems can enable universal access to deposit transaction prediction data to various downstream services by utilizing a data pipeline that identifies data for a user account from various data sources, transforming the data into deposit transaction prediction data utilizing a deposit transaction predictor model, and updating a deposit transaction prediction data source with the deposit transaction prediction data. For instance, the disclosed systems can utilize the deposit transaction predictor model to analyze various user account data to determine patterns that indicate deposit transaction prediction data.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize deposit transaction prediction data from a deposit transaction predictor model to generate a graphical user interface (GUI) that indicates an available deposit balance and options for the available deposit balance. Indeed, the disclosed systems can enable access to an available deposit balance on a user account prior to an occurrence of a predicted deposit transaction as indicated by the deposit transaction predictor model. For example, the disclosed systems can receive deposit transaction prediction data from a data pipeline that includes a deposit transaction predictor model. Moreover, the disclosed systems can determine an available deposit balance from the deposit transaction prediction data.

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 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.

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.