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Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection

arXiv 2506.00654 ↗First posted 31 May 2025cs.CR cs.LGcited by 0

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Abstract (arXiv)

Money laundering is a financial crime that poses a serious threat to financial integrity and social security. The growing number of transactions makes it necessary to use automatic tools that help law enforcement agencies detect such criminal activity. In this work, we present Amatriciana, a novel approach based on Graph Neural Networks to detect money launderers inside a graph of transactions by considering temporal information. Amatriciana uses the whole graph of transactions without splitting it into several time-based subgraphs, exploiting all relational information in the dataset. Our experiments on a public dataset reveal that the model can learn from a limited amount of data. Furthermore, when more data is available, the model outperforms other State-of-the-art approaches; in particular, Amatriciana decreases the number of False Positives (FPs) while detecting many launderers. In summary, Amatriciana achieves an F1 score of 0.76. In addition, it lowers the FPs by 55% with respect to other State-of-the-art models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 33 references that are in the map. First posted on arXiv 31 May 2025.

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13 of 33 references.

References · 13

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  5. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 447
  6. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 164
  7. 2021Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depthcited by 55
  8. 2023Realistic Synthetic Financial Transactions for Anti-Money Laundering Modelscited by 50
  9. 2018Scalable Graph Learning for Anti-Money Laundering: A First Lookcited by 33
  10. 2022LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Launderingcited by 20
  11. 2016Detection of money laundering groups using supervised learning in networkscited by 9
  12. 2023Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Launderingcited by 2
1 more
  1. 2025Intelligent Anti-Money Laundering Solution Based upon Novel Community Detection in Massive Transaction Networks on Sparkcited by 2

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