Adaptive KDE for Real-Time Thresholding: Prioritized Queues for Financial Crime Investigation

We study the problem of converting a stream of risk scores into one or more review queues under explicit intake constraints[cite: 6]. Instead of top-$K$ or manually tuned cutoffs, we fit an online adaptive kernel density to the score stream, transform the density into a tail-mass curve to meet capacity, and ``snap'' the resulting cut to a persistent density valley detected across bandwidths[cite: 7]. The procedure is label-free, supports multi-queue routing, and operates in real time with sliding windows or exponential forgetting[cite: 8]. On synthetic, drifting, multimodal streams, the method achieves competitive capacity adherence while reducing threshold jitter[cite: 9]. Updates cost $O(G)$ per event with constant memory per activity