Probabilistic anomaly detection in streaming device data

Abstract

There are provided systems and methods for a machine learning model for probabilistic anomaly detection in streaming device data. A server obtains a plurality of features associated with a user interaction between a user device and the server. The server selects a combination of features from the plurality of features, where the combination of features comprises features having a variance of expected values that exceeds a threshold variance. The server selects a prediction engine to process the combination of features with a corresponding non-parametric statistical model of a plurality of non-parametric statistical models and to generate a prediction indicating a likelihood that the combination of features represents an anomaly corresponding to fraudulent activity. The server issues a remedial action to the user device through an application programming interface with a remedial action engine based on the prediction for applying the remedial action on the user interaction.