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

TL;DR

This paper presents a scalable community detection approach on Spark to identify criminal gangs involved in money laundering, improving the efficiency of anti-money laundering efforts beyond traditional rule-based and single-account methods.

Contribution

It introduces a novel temporal-directed Louvain algorithm for community detection tailored to anti-money laundering, optimized for large-scale transaction networks on Spark.

Findings

Enhanced detection of money laundering gangs.

Significant efficiency improvements on Spark platform.

Effective identification of suspicious criminal communities.

Abstract

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.