# Intelligent Anti-Money Laundering Solution Based upon Novel Community Detection in Massive Transaction Networks on Spark

arXiv: https://arxiv.org/abs/2501.09026 · HTML: https://mlsanity.com/paper/2501.09026/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 17 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection](https://mlsanity.com/paper/2506.00654/) (2025)
- [Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation](https://mlsanity.com/paper/2604.02899/) (2026)

## Abstract (arXiv)

Criminals are using every means available to launder the profits from their illegal activities into ostensibly legitimate assets. Meanwhile, most commercial anti-money laundering systems are still rule-based, which cannot adapt to the ever-changing tricks. Although some machine learning methods have been proposed, they are mainly focused on the perspective of abnormal behavior for single accounts. Considering money laundering activities are often involved in gang criminals, these methods are still not intelligent enough to crack down on criminal gangs all-sidedly. In this paper, a systematic solution is presented to find suspicious money laundering gangs. A temporal-directed Louvain algorithm has been proposed to detect communities according to relevant anti-money laundering patterns. All processes are implemented and optimized on Spark platform. This solution can greatly improve the efficiency of anti-money laundering work for financial regulation agencies.