MonLAD: Money Laundering Agents Detection in Transaction Streams

TL;DR

MonLAD introduces a real-time detection method for money laundering agents in transaction streams, utilizing residual tracking and anomaly scoring to outperform existing approaches and identify suspicious accounts effectively.

Contribution

The paper presents MonLAD and MonLAD-W, novel unsupervised algorithms tailored for streaming data to detect money laundering agents by analyzing residuals and features.

Findings

MonLAD outperforms state-of-the-art baselines on real data.

The approach detects diverse suspicious behaviors.

Some identified accounts are verified as laundering agents.

Abstract

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.