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.
By Yin-Ju Wu, Bi-Cheng Yan, Berlin Chen
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.
By Danae Natalie Savvidi, H. \c{C}a\u{g}r{\i} Bilgi, Kubilay Atasu, Arie van Deursen
arXiv:2607. 19350v1 Announce Type: new Abstract: Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.
By Mariam Zakaria Moussa Ali
arXiv:2609.14234v1 Announce Type: cross
Abstract: Financial fraud in corporate transaction networks has grown more coordinated and harder to detect with rule-based engines and with classical learning...
By Sergei, Komarov
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.
By Keyang Chen, Mingxuan Jiang, Yongsheng Zhao, Zeping Li, Zaiyuan Chen, Weiqi Luo, Zhixin Li, Sen Liu, Yinan Jing, Guangnan Ye, Xihong Wu, Hongfeng Chai
Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately identifying credit fraud among billions of users is critical for minimizing financial losses and safeguarding the sustainability of inclusive financial services.
arXiv:2610.27107v1 Announce Type: new
Abstract: Credit card fraud is a significant problem for financial institutions, resulting in substantial monetary loss each year. Fraud patterns and methods are...
By Mythreyee Hari, Subin Sahayam, Vegesna S M Srinivasavarma
arXiv:2607. 07089v1 Announce Type: new Abstract: Relational Deep Learning (RDL) has become a standard methodology for machine learning on relational databases: the database is encoded as a heterogeneous temporal graph in which tuples become nodes and primary-key to foreign-key (PK-FK) dependencies become typed edges, over which a graph neural network is trained for downstream prediction.
By Alan Gany, Bogdan Cautis, Silviu Maniu
arXiv:2608. 02168v1 Announce Type: new Abstract: Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems.
By Xin Liu, Xiyuan Chen, Chenglong Wu, Xuan Zong, Jun Zhou, Dawei Cheng
arXiv:2311.16139v3 Announce Type: replace-cross
Abstract: Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such a...
By Zeyu Song, Ehsanul Kabir, Shagufta Mehnaz
arXiv:2609.09945v1 Announce Type: cross
Abstract: Machine learning-based credit scoring is increasingly central to Peer-to-Peer (P2P) lending, yet its resilience to adversarial manipulation, where ap...
By Gijs A. F. Niewzwaag, Marijn G. S. Veth, Manuele Massei, Marcos R. Machado
arXiv:2606. 28134v1 Announce Type: cross Abstract: Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to substantial financial losses and security risks.
By Liming Liu, Chao Hu, Mingfei Lu, Yiwei Ge, Xingle Li, Heyuan Shi