Systems and methods for anomaly detection in automated workflow event decisions in a machine learning-based digital threat mitigation platform

Abstract

A system and method for automated anomaly detection in automated disposal decisions of an automated decisioning workflow includes collecting a time-series of automated disposal decision data for a current period from an automated decisioning workflow, wherein the automated decisioning workflow computes one of a plurality of distinct disposal decisions for each distinct input comprising subject online event data and a machine learning-based threat score computed for the subject online event data; selecting an anomaly detection algorithm from a plurality of distinct anomaly detection algorithms based on a type of online abuse or online fraud that the automated decisioning workflow is configured to evaluate; evaluating, using the selected anomaly detection algorithm, the time-series of automated decision data for the current period; computing whether anomalies exist in the time-series of automated disposal decision data for the current period based on the evaluation; and generating an anomaly alert based on the computation.