Online user behavior analysis service backed by deep learning models trained on shared digital information

A system comprising:

• one or more hardware processors;
• a data processing module for receiving application-level event data for a plurality of accounts from one or more online service providers and for generating common digital information from the event data, comprising:
• extracting anonymized common digital information from the training data and using the common digital information to generate respective feature vectors for one or more of each user or each application-level event, wherein the common digital information comprises attributes of a digital communication that do not identify a particular user account and do not identify information about the event itself;
• a training module for training a deep neural network using the feature vectors as input, wherein the deep neural network is trained to determine model parameters such that applying the trained deep neural network model to an input comprising common digital information associated with a particular user account generates a prediction of whether the particular user account is fraudulent, wherein training the deep neural network model includes tailoring the model to domain specific information of the particular online service provider using a set of specific data fields available to a particular online service provider;
• and
• a computing module including the deep neural network model and a server module, wherein the deep neural network module, the deep neural network module configured to use the first common digital information to generate predictions of whether a the first user account of an online service provider is fraudulent and wherein the server module manages queries to the deep learning model from online service providers.