AAMAS Conference 2026 Conference Paper
Network-based Active Learning for Identifying Illicit Actors in Financial Transaction Networks
- Amro Alabsi Aljundi
- Abhijin Adiga
- Christopher Barrett
- Margaret J. Foster
- Brian D. Klahn
- Dustin Machi
- Achla Marathe
- Philip B. K. Potter
Identifying illicit transactions within financial networks is an important area of research. Available datasets are highly imbalanced, makingthedesignofmachinelearningmethodschallenging. Active learning, which carefully chooses data points for annotation, has been shown to improve performance for such problems. Here, we design a new approach, C2AL, for detecting illicit nodes in financial networks, which incorporates network correlations more explicitly. Our approach builds on prior work on active learning on networks, specifically, collective classification (CC), which uses predicted labels of neighboring nodes to improve classification. We extend this approach by incorporating the information from both underlying models of collective classification, as well as their contrastive information, into the active learning sample selection procedure. We show that C2AL significantly improves sample efficiency, requiring up to 48% fewer labeled samples than prior methods to achieve comparable detection performance across six financial network datasets. This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), C. Amato, L. Dennis, V. Mascardi, J. Thangarajah (eds.), May 25 – 29, 2026, Paphos, Cyprus. © 2026 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). https: //doi. org/10. 65109/GOCK1684