# Using social network analysis to prevent money laundering

arXiv: https://arxiv.org/abs/2105.05793 · HTML: https://mlsanity.com/paper/2105.05793/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2021.

## Built on by (most cited first, 14 of 14)

- [The impact of virtual mirroring on customer satisfaction](https://mlsanity.com/paper/2105.09571/) (2021)
- [Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation](https://mlsanity.com/paper/2405.19383/) (2024)
- [Using four different online media sources to forecast the crude oil price](https://mlsanity.com/paper/2105.09154/) (2021)
- [Fighting Money Laundering with Statistics and Machine Learning](https://mlsanity.com/paper/2201.04207/) (2022)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Finding Money Launderers Using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2307.13499/) (2023)
- [Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach](https://mlsanity.com/paper/2308.01063/) (2023)
- [Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field](https://mlsanity.com/paper/2212.04329/) (2022)
- [Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review](https://mlsanity.com/paper/2503.24259/) (2025)
- [A Graph Machine Learning Approach for Detecting Topological Patterns in Transactional Graphs](https://mlsanity.com/paper/2509.12730/) (2025)
- [Unlocking Criminal Hierarchies: A Survey, Experimental, and Comparative Exploration of Techniques for Identifying Leaders within Criminal Networks](https://mlsanity.com/paper/2402.03355/) (2024)
- [GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering](https://mlsanity.com/paper/2506.04292/) (2025)
- [The Shape of Deceit: Behavioral Consistency and Fragility in Money Laundering Patterns](https://mlsanity.com/paper/2507.10608/) (2025)
- [ART: A Graph-based Framework for Investigating Illicit Activity in Monero via Address-Ring-Transaction Structures](https://mlsanity.com/paper/2511.16192/) (2025)

## Abstract (arXiv)

This research explores the opportunities for the application of network analytic techniques to prevent money laundering. We worked on real world data by analyzing the central database of a factoring company, mainly operating in Italy, over a period of 19 months. This database contained the financial operations linked to the factoring business, together with other useful information about the company clients. We propose a new approach to sort and map relational data and present predictive models, based on network metrics, to assess risk profiles of clients involved in the factoring business. We find that risk profiles can be predicted by using social network metrics. In our dataset, the most dangerous social actors deal with bigger or more frequent financial operations; they are more peripheral in the transactions network; they mediate transactions across different economic sectors and operate in riskier countries or Italian regions. Finally, to spot potential clusters of criminals, we propose a visual analysis of the tacit links existing among different companies who share the same owner or representative. Our findings show the importance of using a network-based approach when looking for suspicious financial operations and potential criminals.