Systems and methods for risk analysis and mitigation with nested machine learning models for exam registration and delivery processes

Abstract

Systems and methods may involve processing of entity data by nested machine learning models to produce one or more aggregate risk scores, which may be compared to one or more thresholds to determine when one or more predefined actions should be taken. The entity data may be collected for various entities related to an exam registration and delivery process, which may include a candidate, an exam, a test center, an exam registration event, a proctor, and an exam delivery event. Entity data for each entity may be separately processed by entity-specific machine learning models to generate intermediate entity risk scores. The intermediate entity risk scores may be input to an aggregate machine learning model, which may output an aggregate risk score. A resource management server may cause the predefined actions to be taken after comparing the aggregate risk score to the one or more thresholds.