TRACK: Multiple Instance Learning for Anti-Money Laundering in Transaction Networks
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
MINT is a graph neural network framework designed to detect illicit accounts in financial transaction networks. It addresses structural inconsistency by using an attentive message‑passing scheme that tracks deviations between nodes in bi‑directional flows and a transaction salience module that regularizes node interactions. To cope with limited labeled data, MINT employs Positive‑Unlabeled learning, leveraging a small set of known illicit nodes and many unlabeled ones, and demonstrates superior performance on three benchmark datasets.
The paper introduces Temporal-Context Edge Enrichment (TCEE), a framework that uses frozen time‑series foundation models (TSFMs) to summarise each account’s transaction history and enrich transaction representations with temporal context. These enriched edges can be directly scored for classification or used as features in a graph neural network (GNN). Across six AMLWorld datasets, TCEE improves PR‑AUC by an average of 10.8% over the best baseline and trains 6.0× faster; when combined with a GNN, it yields an additional 4.8% improvement on smaller datasets and a 14.5% gain on an Ethereum phishing dataset.
arXiv:2604. 17420v2 Announce Type: replace-cross Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring.
The paper investigates how to detect money‑laundering patterns across separate bank transaction graphs using federated learning. By leveraging transactions that both banks already record, the authors approximate remote account states locally and run multi‑layer Graph Neural Networks without exchanging sensitive account data. Experiments on AMLworld HI‑Small show that a simple baseline using locally available information yields the greatest performance, while adding limited context such as transaction counts improves results; however, sharing detailed remote states requires excessive noise that diminishes utility.
The paper introduces GARG-AML, a graph-based framework designed to detect smurfing—a money‑laundering technique that splits large transactions into smaller ones—to improve anti‑money laundering efforts. It quantifies smurfing risk with a single interpretable metric derived from the second‑order transaction network, offering a balance between computational efficiency and transparency. The authors demonstrate GARG‑AML’s scalability on large random networks and its effectiveness on two open‑source money‑laundering datasets, suggesting it can enhance smurfing detection and support investigative decision‑making.
arXiv:2607. 10131v1 Announce Type: cross Abstract: Money laundering threatens financial stability and exposes institutions to penalties, motivating automated detection.