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Fraud Type Decomposition and the Observation-Mechanism Taxonomy:Class-Specific Detection Limits in Payment Networks

Paper on fraud detection in payment networks using an observation-mechanism taxonomy.

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Fraud Type Decomposition and the Observation-Mechanism Taxonomy:Class-Specific Detection Limits in Payment Networks

By Gaurav DhamaarXiv
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The paper introduces a taxonomy that partitions fraud into five classes, each with distinct censorship and labeling pipelines. It proves that estimating fraud rates separately by class is more efficient than pooled estimation.

The authors derive theoretical constraints on detection for each class, including label corruption and structural non-observability.

Abstract

The paper introduces a taxonomy that partitions fraud into five classes, each with distinct censorship and labeling pipelines. It proves that estimating fraud rates separately by class is more efficient than pooled estimation. The authors derive theoretical constraints on detection for each class, including label corruption and structural non-observability.

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fraud detectionpayment networksobservation-mechanism taxonomyestimation efficiencylabel corruptionstructural non-observabilityStructured ContentContent EngineeringAI AgentsOntology & Taxonomy
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