SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection

• URL: http://arxiv.org/abs/2605.20157v1
• Date: Tue, 19 May 2026 17:46:31 GMT
• Title: SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection
• Abstract summary: Music streaming fraud poses a significant threat to streaming services and legitimate content creators.<n>Traditional fraud detection approaches struggle with a critical challenge: many legitimate edge cases, including super-fans and sleep-music sessions, exhibit activity patterns that closely mimic those of coordinated fraud.<n>We present SAGE, a novel counterfactual-aware negative harvesting approach that combines SimHash-based stratified sampling with a modular gating ensemble for confident negative identification from unlabeled data.
• Score: 0.30693357740321775
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Music streaming fraud, where bad actors artificially inflate stream counts to manipulate chart rankings and royalty payments, poses a significant threat to streaming services and legitimate content creators. Traditional fraud detection approaches struggle with a critical challenge: many legitimate edge cases, including super-fans and sleep-music sessions, exhibit activity patterns that closely mimic those of coordinated fraud. We present SAGE, a novel counterfactual-aware negative harvesting approach that combines SimHash-based stratified sampling with a modular gating ensemble for confident negative identification from unlabeled data. Our ensemble architecture employs pluggable statistical gates (currently instantiated with Mahalanobis distance and k-NN density) with configurable voting thresholds enabling adaptive precision-recall trade-offs. This addresses the representation bias problem in Positive-Unlabeled learning by ensuring comprehensive coverage of rare behavioral cohorts through floor-constrained sampling. Evaluation demonstrates strong precision and recall on held-out data. The approach generalizes across fraud detection domains, achieving strong performance on both customer-level and artist-level fraud without modification to the core methodology.

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