BEHAVIORAL MISALIGNMENT DETECTION WITHIN ENTITY HARD SEGMENTATION UTILIZING ARCHETYPE-CLUSTERING
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 b...
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Main Authors | , |
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Format | Patent |
Language | English French German |
Published |
20.09.2017
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Subjects | |
Online Access | Get full text |
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Summary: | 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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Bibliography: | Application Number: EP20170000439 |