🤖 AI Summary
This work addresses the challenge of constructing real-time review queues for risk-scoring streams in financial crime investigations by proposing a label-free, adaptive thresholding mechanism. The approach leverages online adaptive kernel density estimation (KDE), dynamically satisfying queue capacity constraints through tail-mass curves and identifying stable thresholds via persistent density minima “snapshots” detected across multiple bandwidths. Integrated with sliding windows, exponential forgetting, and priority-based queue management, the system supports multi-queue routing and real-time processing. Experimental results demonstrate that the method strictly adheres to capacity limits across synthetic, concept-drifting, and multimodal data streams, significantly reduces threshold jitter, and achieves per-event update complexity of O(G) with constant memory usage.
📝 Abstract
We study the problem of converting a continuous stream of risk scores into stable decision thresholds under non-stationary score distributions. This problem arises in a wide range of detection systems where scores must be partitioned into prioritized processing regions while preserving semantic consistency over time.