SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection
Researchers have developed SAGE, a new method for detecting music streaming fraud by identifying suspicious activity patterns. SAGE uses a combination of SimHash-based sampling and a modular gating ensemble to confidently distinguish fraudulent streams from legitimate edge cases like super-fans or sleep-music sessions. The system's adaptive nature allows for flexible precision-recall trade-offs, and it has demonstrated strong performance on held-out data, generalizing effectively across different fraud detection scenarios. AI
IMPACT Introduces a novel counterfactual-aware approach to negative harvesting for improved fraud detection in streaming services.