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BEHAVIORAL MISALIGNMENT DETECTION WITHIN ENTITY HARD SEGMENTATION UTILIZING ARCHETYPE-CLUSTERING
BEHAVIORAL MISALIGNMENT DETECTION WITHIN ENTITY HARD SEGMENTATION UTILIZING ARCHETYPE-CLUSTERING
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机译:利用Archtype-Clustering进行实体硬性分割的行为失误检测
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摘要
An automated way of learning archetypes which capture many aspects of entity behavior, and assigning entities to a mixture of archetypes, such that each entity is represented as a distribution across multiple archetypes. Given those representations in archetypes, anomalous behavior can be detected by finding misalignment with a plurality of entities archetype clustering within a hard segmentation. Extensions to sequence modeling are also discussed. Applications of this method include anti-money laundering (where the entities can be customers and accounts, as described extensively below), retail banking fraud detection, network security, and general anomaly detection.
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