# MonLAD: Money Laundering Agents Detection in Transaction Streams

arXiv: https://arxiv.org/abs/2201.10051 · HTML: https://mlsanity.com/paper/2201.10051/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics](https://mlsanity.com/paper/2306.06108/) (2023)
- [Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling](https://mlsanity.com/paper/2604.01315/) (2026)
- [Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies](https://mlsanity.com/paper/2603.01841/) (2026)

## Abstract (arXiv)

Given a stream of money transactions between accounts in a bank, how can we accurately detect money laundering agent accounts and suspected behaviors in real-time? Money laundering agents try to hide the origin of illegally obtained money by dispersive multiple small transactions and evade detection by smart strategies. Therefore, it is challenging to accurately catch such fraudsters in an unsupervised manner. Existing approaches do not consider the characteristics of those agent accounts and are not suitable to the streaming settings. Therefore, we propose MonLAD and MonLAD-W to detect money laundering agent accounts in a transaction stream by keeping track of their residuals and other features; we devise AnoScore algorithm to find anomalies based on the robust measure of statistical deviation. Experimental results show that MonLAD outperforms the state-of-the-art baselines on real-world data and finds various suspicious behavior patterns of money laundering. Additionally, several detected suspected accounts have been manually-verified as agents in real money laundering scenario.