Training and using multi-task predictive risk models in software applications

A method implemented by a computing system comprising one or more processors and at least one memory, the method comprising:

• extracting, from a transaction history data set for a plurality of users of a software application, a plurality of features for each user of the plurality of users having records in the transaction history data set;
• generating a training data set based on the extracted plurality of features for each user of the plurality of users;
• and
• training a multi-output machine learning model to generate a plurality of predictive classifications for an input of the plurality of features based on the training data set, wherein each respective predictive classification of the plurality of predictive classifications is associated with a respective probability of whether a specified event will occur for a user of a computing system, and wherein the multi-output machine learning model includes a set of layers common to the plurality of predictive classifications and a plurality of classification task-specific layers, each respective layer of the plurality of classification task-specific layers being associated with a respective classification task.

A processing system, comprising:

• at least one memory having executable instructions stored thereon;
• and
• one or more processors configured to execute the executable instructions to cause the processing system to:
• receive, from a user of a computing system, a request to execution of a task in the computing system;
• generate an input feature vector based on historical data associated with the user,
• request, from a multi-output machine learning model, a plurality of predictive classifications, wherein each respective predictive classification is associated with a respective probability of whether a specified event will occur for the user, and wherein the multi-output machine learning model includes a set of layers common to the plurality of predictive classifications and a plurality of classification task-specific layers, each respective layer of the plurality of classification task-specific layers being associated with a respective classification task;
• and
• execute the task based on the plurality of predictive classifications.