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: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.
arXiv:2606. 26189v1 Announce Type: new Abstract: Money Laundering Group Discovery (MLGD) aims to identify hidden criminal groups and recover their complete structures in large-scale financial networks.
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.
arXiv:2608. 15177v1 Announce Type: cross Abstract: The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational risk reasoning over interconnected financial entities.
arXiv:2607. 10131v1 Announce Type: cross Abstract: Money laundering threatens financial stability and exposes institutions to penalties, motivating automated detection.
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:2610.16449v1 Announce Type: cross Abstract: Pseudonymous accounts and complex smart-contract ecosystems substantially hinder the tracing of illicit cryptocurrency fund flows. Existing Graph Neu...
The paper examines how a breach of a single AI vendor—used by banks for fraud screening, credit decisions, AML triage, customer analytics, and internal support—can spread through operational, informational, and financial links, ultimately causing losses that resemble a traditional banking crisis. It introduces a four‑layer heterogeneous network linking AI vendors, banks, interbank exposures, and customer accounts, and presents CFC‑Prop, a stochastic epidemic‑and‑clearing model that reproduces heavy‑tailed loss distributions and sensitivity to patch latency on a synthetic dataset of 60 vendors, 220 banks, and 1,400 interbank exposures. Additionally, the authors develop CFC‑GNN, an early‑warning graph‑based model that predicts high‑cascade‑risk vendors with AUROC 0.82 and AUPRC 0.60, and they release all code, data, and scripts for reproducibility.
GraphFAS is a distributed system that automates graph feature generation and selection for industrial transaction networks. It uses a non‑parametric graph feature generator that creates explicit, interpretable structural features through multi‑hop subgraph extraction and multi‑scale aggregation, and an extended Boruta algorithm that aggregates feature importance across partitions to robustly identify informative features at scale. By decoupling feature aggregation from model training, GraphFAS can be directly integrated with tabular models and TreeSHAP‑based explanations, and it has been deployed in Alipay, achieving significant engineering efficiency gains and strong performance against expert‑driven and graph‑learning baselines on large‑scale graphs.
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:2501.18158v4 Announce Type: replace-cross Abstract: Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models m...
GraphFAS is a distributed system that automates graph feature generation and selection for industrial transaction networks. It uses a non‑parametric module to create explicit, interpretable structural features through multi‑hop subgraph extraction and multi‑scale aggregation, and extends the Boruta algorithm with median‑based aggregation across partitions for robust feature selection. The approach decouples feature aggregation from model training, allowing integration with tabular models and TreeSHAP explanations, and has been deployed in Alipay, yielding significant engineering efficiency gains and strong performance against expert‑driven and graph‑learning baselines.