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 present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine learning model to determine predicted client intent classifications and 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 intent classifications and corresponding intent classification probabilities. The disclosed systems utilize the predicted client disposition classifications and the disposition classification 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: The present disclosure relates to systems, methods, and non-transitory computer-readable media that detect synthetic user accounts of a digital system via machine learning. For instance, the disclosed systems can utilize a machine learning model to analyze account features that are related to a user account and generate an indication that the user account is synthetic based on the analysis. The disclosed systems can further disable (e.g., suspend or close) the user account based on determining that the user account is synthetic. In some cases, the machine learning model provides a precision score that indicates a likelihood that the user account is synthetic, and the disclosed systems disable the user account if the precision score satisfies a threshold. In some implementations, the disclosed systems generate the machine learning model using synthetic user accounts detected via one or more rules and other user accounts that are associated with those synthetic user accounts.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for processing access requests on a service-to-service basis using a third-party identification token. In particular, the disclosed systems can identify that a user is authenticated for a first computer service based on detecting a third-party identification token. Further, the disclosed systems can generate, by the first computer service, an access request comprising a requested action and the third-party identification token to a second computer service. Additionally, the disclosed systems can determine, by the second computer service, whether the access request is authorized based on determining that an authorization policy defined at the second computer service authorizes the requested action by the first computer service and that the third-party identification token is valid.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine-learning models to determine take-over scores and intelligently provide or limit access to account features. In particular, in one or more embodiments, the disclosed systems can train and utilize digital security machine-learning models to generate a take-over score indicating a likelihood that the request to access the secure digital account is unauthorized activity. Based on the determining that the take-over score satisfies a take-over threshold, the disclosed systems can allow access to the secure digital account by providing secure account information but prohibit access to a subset of account features.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing asynchronous double-writes to migrate data from a primary database to a secondary database. In particular, in one or more embodiments, the disclosed systems, upon receiving a work unit, write data to a primary database and generate a trigger to asynchronously write, by a sync record service, the data to a secondary database. In some embodiments, the disclosed systems reverse the direction of the asynchronous double-writes so that, upon receiving additional work units, the disclosed systems write, by the sync record service, additional data to the secondary database and generate a trigger to asynchronously write additional data to the primary database.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transforming data metrics to conform to a production schema for cross-domain uniformity. In particular, in one or more embodiments, the disclosed systems receive a metric having an initial schema, determine that the initial schema is inconsistent with a production schema, transform the metric to conform with the production schema, and store the metric in a standardized-schema database.

Abstract: This disclosure describes a vector configurator system that, as part of an inter-network facilitation system, can generate and utilize transaction vectors for facilitating network transactions among digital accounts of an inter-network facilitation system. For example, the disclosed systems can generate a transaction vector that includes a unique set of key attributes defining parameters for a network transaction and that further includes a ledger specification indicating computer code for how to execute the network transaction. Upon receiving a transaction request, the disclosed systems can access a repository of transaction vectors to determine whether a corresponding vector exists for the request. The disclosed systems can either validate or reject the transaction request based on the existence or nonexistence of a transaction vector for the request.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for relaying payloads with digital policy criteria for dispute requests of network transactions from a first digital system to a second digital system, for the second digital system to automatically implement the digital policy criteria. In particular, in one or more embodiments, the disclosed system receives payloads comprising digital policy criteria and policy identifier(s) and determines whether to approve or deny dispute requests based on incoming dispute requests.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a multi-transaction dispute package. For example, in one or more embodiments, the disclosed system receives a dispute request with an initial disputed transaction and identifies other potential transactions based on dispute classifications. In one or more embodiments, the disclosed system provides the identified potential transactions that are potentially related to the initial disputed transaction for selection. In response to receiving a selection from the user, the disclosed system generates and processes a multi-transaction dispute package.

Abstract: This disclosure describes an event bus system that, as part of an inter-network facilitation system, can generate and distribute network events for self-service event requests utilizing a network event data streaming platform. For example, the disclosed systems can utilize a network event data streaming platform that includes specialized network components, such as a batch distribution data lake and an event fanning platform. The disclosed systems can utilize the batch distribution data lake to distribute long-retention network events for high-latency event requests. Additionally, the disclosed systems can utilize the event fanning platform to generate low-latency fanned data streams from short-retention network events for low-latency event requests.

Abstract: This disclosure describes an intelligent fraud detections system that, as part of an inter-network facilitation system, can intelligently generate fraud predictions for digital claims to improve accuracy and efficiency of network-transaction security systems. For example, the disclosed systems can utilize a fraud detection machine-learning model to generate a fraud prediction for a digital claim disputing a digital transaction. Indeed, the disclosed systems can identify features of a digital claim and, based on those features, utilize a fraud detection machine-learning model to generate a fraud prediction for the digital claim. Additionally, based on the fraud prediction, the disclosed systems can perform authorizing, remedial, or other actions with regard to the digital claim.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine learning model to determine predicted client intent classifications and 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 intent classifications and corresponding intent classification probabilities. The disclosed systems utilize the predicted client disposition classifications and the disposition classification 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: This present application describes one or more embodiments of methods, non-transitory computer-readable media, and systems provide developer tools for generating and providing visualizations of data densities for various portions of a computer application. For example, the systems can determine a data density that reflects a ratio or an amount of data presented within a (portion of a) display window relative to the entire (portion of the) display window. The systems can further provide a visual representation of a data density for display on a client device, along with indications or suggestions for how to improve (e.g., reduce or increase) the data density for better comprehensibility (e.g., upon distribution of the application). In certain embodiments, the systems can generate suggestions based on an application type of a computer application in development and/or based on a function associated with a particular (portion of a) display window.

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 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 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.