Detection of money laundering groups using supervised learning in networks

TL;DR

This paper presents a supervised learning-based system that analyzes group behavior in large networks to detect money laundering activities effectively, with low false positives suitable for real-world deployment.

Contribution

It introduces a novel detection system combining network analysis and supervised learning to identify money laundering groups, addressing the limitations of individual-focused methods.

Findings

Successfully detects suspicious activity in real-world data

Maintains a low false positive rate

Operates efficiently on large-scale networks

Abstract

Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals, and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates that suspicious activity is successfully detected. Importantly, the system exhibits a low rate of false positives, and is therefore suitable for use in a live intelligence environment.