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 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.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to determine predicted client intent classifications and/or client-agent escalation classes to generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client-agent escalation classes and corresponding probabilities. The disclosed systems utilize the predicted client-agent escalation classifications and the escalation class probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.

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: A real-time activity recommendation system receives an input from a user device regarding a targeted financial goal, such as a target credit score. Using machine learning models to evaluate patterns of user activity that contribute positively towards the goal, and to evaluate the limitations and opportunities of the user's financial circumstances and profile, the recommendation system makes an assessment in real time to determine user actions that can be taken to improve credit health based on a user's profile and activity data. A user-specific recommendation regarding an activity that should be performed to reach the goal is generated and transmitted to the user. User and third party activity is later monitored as the user's financial status changes over time, and the recommendations are updated accordingly.

Abstract: A transaction approval system makes an assessment in real time whether to approve a purchase transaction and to select an account of the customer to fund the purchase transaction. The transaction approval system can use a machine learning model to select the appropriate account of the customer to fund a purchase transaction that satisfies one or more preferences or goals established by the customer or the system. The machine learning model can receive, as inputs, information on one or more of the customer's accounts, user preference information, information regarding the transaction and/or information regarding prior transactions by the customer and use the inputs and machine learning model to make a selection of a customer account that satisfies the preferences or goals established by the customer or system.

Abstract: The present disclosure relates to method, systems, and non-transitory computer-readable media for building accounts of a digital system using one or more improved graphical user interfaces. For example, in one or more embodiments, the disclosed systems provide a consolidated graphical user interface for applying for multiple user accounts simultaneously utilizing application information received via an enrollment workflow. In some cases, upon application to the multiple accounts, the disclosed system creates at least one account and puts a hold on at least one other account until one or more additional requirements are met. Further, the disclosed systems can provide a graphical user interface that facilitates funding of an account by, for example, supplying options corresponding to suggested transfer amounts determined from user account characteristics.

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: A payer initiates a payment transaction of a certain dollar amount through a user interface provided by their bank. The bank generates a hyperlink associated with the payment transaction. By managing a state of the transaction, the hyperlink can be given an expiration time and/or a counter of visits. The hyperlink is sent to a payment recipient in a text message or email, either by the payer or by the bank. Upon following the hyperlink, the recipient can enter their own debit card information to receive the money transfer to their debit account, without the need to set up an account login or account. A notification mechanism, such as a broadcast stream of payments, can be employed to inform different entities (such as those recording property payments or utilities payments) that a payment was made to the payment recipient.

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.