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

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

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

Abstract: Systems and methods include implementing a remote machine learning service that collects digital event data; collecting incumbent digital threat scores generated by an incumbent machine learning model and successor digital threat scores generated by a successor digital threat machine learning (ML) model; implementing anomalous-shift-detection that detects whether the successor digital threat scores of the successor digital threat ML model produces an anomalous shift; if the anomalous shift is detected by the machine learning model validation system, blocking a deployment of the successor digital threat model to a live ensemble of digital threat scoring models; or if the anomalous shift is not detected by the machine learning model validation system, deploying the successor digital threat ML model by replacing the incumbent digital threat ML model in a live ensemble of digital threat scoring models with the successor digital threat ML model.

Abstract: Systems and methods include implementing a remote machine learning service that collects digital event data; collecting incumbent digital threat scores generated by an incumbent machine learning model and successor digital threat scores generated by a successor digital threat machine learning (ML) model; implementing anomalous-shift-detection that detects whether the successor digital threat scores of the successor digital threat ML model produces an anomalous shift; if the anomalous shift is detected by the machine learning model validation system, blocking a deployment of the successor digital threat model to a live ensemble of digital threat scoring models; or if the anomalous shift is not detected by the machine learning model validation system, deploying the successor digital threat ML model by replacing the incumbent digital threat ML model in a live ensemble of digital threat scoring models with the successor digital threat ML model.