Abstract: Systems and methods receive an input that triggers a request for a random numeric output associated with an outcome that indicates whether resource(s) are distributed to a player account of a player of a gaming application, and transmit the request for generation of the random numeric output to a third party server, the request indicating parameter(s) configured to influence the random numeric output and including data indicating weighted variables used to influence the outcome. A digitally signed electronic output file including the random numeric output is received, the digitally signed electronic output file being encrypted and including a stamp of authentication thereby cryptographically binding an electronic identity to the random numeric output. Key(s) stored to a key index are accessed and the digitally signed electronic output file is verified using the key(s). The random numeric output is authenticated and applied to a promotional draw to determine the outcome.

Abstract: Systems and methods train, using training data, a prediction model by iteratively predicting a target variable value of an engagement event associated with an online video game application, identifying an error between a prediction and the target variable value, and modifying weights of the prediction model for multiple iterations. The training data includes information obtained from the online video game application and a partner computer application that stores resource data of real-world resources to a real-world location and also includes specific data such as a duration of gameplay, a quantity of instances of gameplay, and a quantity of resource transactions of the virtual resource. The trained prediction model is deployed and applied to user data of users to predict attributes of the engagement event that are most likely to cause users to engage with the online video game application within a predefined number of days.

Abstract: Systems and methods extract raw user data of users of an online video game application (OVGA) and append raw user data to a dataset. Data object(s) of the dataset are segmented into, and attribute(s) are derived from field(s) of the dataset, and the attribute(s) include a time between initiation of user engagement with the OVGA and user disengagement with the OVGA by the users. Missing data is imputed with substitute values, and entity extraction is performed using natural language processing to extract specific data that is then classified and stored to a relational database. The relational database is versioned to generate versioned data, and a target variable value is inserted into an iterative training and testing loop to train a prediction model using the versioned data, where the training is based on adjusting weights and iterative calculations to predict the a date of a churn-based event.

Abstract: Systems receive a request from a player device for a random numeric output associated with an outcome that indicates whether resource(s) are to be distributed to a player account of a player of a computer game. The request is authenticated, which includes determining a geographic location of the player from an identifier of the player device, and a current amount of player points already attributed to the player account are determined from player data associated with the player account. An API request associated with the random numeric output to be generated by the outside server is generated and transmitted to an outside server that includes the current amount of player points. A randomly generated numeric input is received from the outside server and based on the current amount of player points, and the outcome is determined based on the randomly generated numeric input and the resource(s) are distributed.

Abstract: Systems and methods train, using training data, a prediction model by iteratively predicting a target variable value of a churn-based event associated with an online video game application by identifying an error between a prediction and the target variable value and modifying weights of the prediction model for multiple iterations, the training data includes information obtained from the online video game application and a partner computer application. The partner computer application stores resource data of real-world resources that are stored to a real-world location, and the information includes specific data from a duration of gameplay via the online video game application, a quantity of instances of gameplay, and a quantity of resource transactions of the virtual resource. The prediction model is deployed and applied to user data of users to predict the churn-based event, where user data is restricted by history date for a predefined number of days.