Scalable Graph Learning for Anti-Money Laundering: A First Look

TL;DR

This paper explores the use of scalable graph convolutional neural networks for anti-money laundering, demonstrating preliminary results on large synthetic data and highlighting potential for improved detection of illicit financial activities.

Contribution

It introduces the application of scalable graph deep learning methods to AML, including a large synthetic dataset and initial experimental insights.

Findings

Preliminary results show promise of graph deep learning in AML detection.

Graph compression can improve computational efficiency.

Large synthetic datasets are useful for AML research.

Abstract

Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people. These nefarious industries rely on sophisticated money laundering schemes to operate. Despite tremendous resources dedicated to anti-money laundering (AML) only a tiny fraction of illicit activity is prevented. The research community can help. In this brief paper, we map the structural and behavioral dynamics driving the technical challenge. We review AML methods, current and emergent. We provide a first look at scalable graph convolutional neural networks for forensic analysis of financial data, which is massive, dense, and dynamic. We report preliminary experimental results using a large synthetic graph (1M nodes, 9M edges) generated by a data simulator we created called AMLSim. We consider opportunities for high performance efficiency, in terms of computation and memory, and we share results from a simple graph compression experiment. Our results support our working hypothesis that graph deep learning for AML bears great promise in the fight against criminal financial activity.