Abstract: Techniques are provided for anomaly detection using processor-based machine learning (ML) models trained using cross-validation training datasets. One method comprises obtaining data characterizing a plurality of item groupings comprised of one or more items; in response to obtaining additional data corresponding to a given item grouping and associated with a given entity: identifying an ML model selected from a plurality of ML models trained using a cross-validation training process that processes data associated with entities having positive instances of a designated anomalous event, wherein training data for a given entity comprises data associated with positive instances of the designated anomalous event for one or more different entities than the given entity; applying feature values associated with the additional data to the selected ML model to obtain data characterizing an anomaly result; and initiating one or more processing steps based at least in part on the anomaly result.

Abstract: Methods, apparatus, and processor-readable storage media for security-related risk detection using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data related to at least one user associated with at least one interaction; identifying information pertaining to the at least one user by processing at least a portion of the obtained data using one or more artificial intelligence techniques; determining, based at least in part on at least a portion of the identified information, one or more security risks associated with the at least one user within a context of the at least one interaction; and performing one or more automated actions based at least in part on the one or more determined security risks.