Danny Butvinik

Danny Butvinik, Yonit Marcus, Nitzan Tal et al.

We introduce the Temporal Contrastive Transformer (TCT), a representation learning framework designed to capture contextual temporal dynamics in sequences of financial transactions. The model is trained using a self-supervised contrastive objective to produce embeddings that encode behavioral patterns over time, with the goal of supporting downstream fraud detection tasks. We evaluate TCT in a realistic setting by using the learned embeddings as input features to a gradient boosting classifier. Experimental results show that embeddings alone achieve meaningful predictive performance (AUC 0.8644), indicating that the model captures non-trivial temporal structure. However, when combined with domain-engineered features, no measurable improvement is observed over the baseline (AUC 0.9205 vs. 0.9245), suggesting that the learned representations largely overlap with existing feature abstractions. These findings position TCT as a promising representation learning approach that captures relevant behavioral signal, while highlighting the challenges of achieving additive value over strong domain features. The results reflect an intermediate stage in the development of temporal representation learning for financial crime detection and motivate further research on model architecture, training objectives, and integration strategies. At this early stage, achieving performance comparable to a strong feature-engineered baseline is itself a meaningful outcome, indicating that learned representations approximate domain-specific features without manual engineering. While not yet production-ready, these results point to a promising direction for reducing reliance on feature engineering in financial crime detection.

Danny Butvinik, Nana Boateng, Achi Hackmon

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

Danny Butvinik, Ofir Yakobi, Michal Einhorn Cohen et al.

Conventional anti-money laundering (AML) systems predominantly focus on identifying anomalous entities or transactions, flagging them for manual investigation based on statistical deviation or suspicious behavior. This paradigm, however, misconstrues the true nature of money laundering, which is rarely anomalous but often deliberate, repeated, and concealed within consistent behavioral routines. In this paper, we challenge the entity-centric approach and propose a network-theoretic perspective that emphasizes detecting predefined laundering patterns across directed transaction networks. We introduce the notion of behavioral consistency as the core trait of laundering activity, and argue that such patterns are better captured through subgraph structures expressing semantic and functional roles - not solely geometry. Crucially, we explore the concept of pattern fragility: the sensitivity of laundering patterns to small attribute changes and, conversely, their semantic robustness even under drastic topological transformations. We claim that laundering detection should not hinge on statistical outliers, but on preservation of behavioral essence, and propose a reconceptualization of pattern similarity grounded in this insight. This philosophical and practical shift has implications for how AML systems model, scan, and interpret networks in the fight against financial crime.