# Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions

arXiv: https://arxiv.org/abs/2309.13662 · HTML: https://mlsanity.com/paper/2309.13662/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [Detection of money laundering groups using supervised learning in networks](https://mlsanity.com/paper/1608.00708/) (2016)
- [Anomaly Detection in Networks with Application to Financial Transaction Networks](https://mlsanity.com/paper/1901.00402/) (2019)
- [Size Matters: A Comparative Analysis of Community Detection Algorithms](https://mlsanity.com/paper/1712.01690/) (2017)
- [TDB: Breaking All Hop-Constrained Cycles in Billion-Scale Directed Graphs](https://mlsanity.com/paper/2209.05909/) (2022)

## Built on by (most cited first, 10 of 10)

- [Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation](https://mlsanity.com/paper/2405.19383/) (2024)
- [Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes](https://mlsanity.com/paper/2312.16596/) (2023)
- [TeMP-TraG: Edge-based Temporal Message Passing in Transaction Graphs](https://mlsanity.com/paper/2503.16901/) (2025)
- [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)
- [Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling](https://mlsanity.com/paper/2604.01315/) (2026)
- [GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering](https://mlsanity.com/paper/2506.04292/) (2025)
- [ART: A Graph-based Framework for Investigating Illicit Activity in Monero via Address-Ring-Transaction Structures](https://mlsanity.com/paper/2511.16192/) (2025)
- [Tide: A Customisable Dataset Generator for Anti-Money Laundering Research](https://mlsanity.com/paper/2603.01863/) (2026)
- [Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation](https://mlsanity.com/paper/2604.02899/) (2026)

## Abstract (arXiv)

Money launderers exploit the weaknesses in detection systems by purposefully placing their ill-gotten money into multiple accounts, at different banks. That money is then layered and moved around among mule accounts to obscure the origin and the flow of transactions. Consequently, the money is integrated into the financial system without raising suspicion. Path finding algorithms that aim at tracking suspicious flows of money usually struggle with scale and complexity. Existing community detection techniques also fail to properly capture the time-dependent relationships. This is particularly evident when performing analytics over massive transaction graphs. We propose a framework (called FaSTMAN), adapted for domain-specific constraints, to efficiently construct a temporal graph of sequential transactions. The framework includes a weighting method, using 2nd order graph representation, to quantify the significance of the edges. This method enables us to distribute complex queries on smaller and densely connected networks of flows. Finally, based on those queries, we can effectively identify networks of suspicious flows. We extensively evaluate the scalability and the effectiveness of our framework against two state-of-the-art solutions for detecting suspicious flows of transactions. For a dataset of over 1 Billion transactions from multiple large European banks, the results show a clear superiority of our framework both in efficiency and usefulness.