Fraud Detection in Cryptocurrency Markets with Spatio-Temporal Graph Neural Networks

TL;DR

This paper introduces a novel spatio-temporal graph neural network approach for detecting coordinated market manipulation in cryptocurrency markets, leveraging relational structures in transaction data.

Contribution

The paper proposes three graph construction methods and a unified GNN architecture that significantly improves fraud detection over traditional methods.

Findings

Graph-based models outperform standard machine learning baselines.

Learned market connectivity enhances detection of coordinated schemes.

Method effectively identifies pump-and-dump schemes over three years.

Abstract

Technological advancements in cryptocurrency markets have increased accessibility for investors, but concurrently exposed them to the risks of market manipulations. Existing fraud detection mechanisms typically rely on machine learning methods that treat each financial asset (i.e., token) and its related transactions independently. However, market manipulation strategies are rarely isolated events, but are rather characterized by coordination, repetition, and frequent transfers among related assets. This suggests that relational structure constitutes an integral component of the signal and can be effectively represented through graphical means. In this paper, we propose three graph construction methods that rely on aggregated hourly market data. The proposed graphs are processed by a unified spatio-temporal Graph Neural Network (GNN) architecture that combines attention-based spatial aggregation with temporal Transformer encoding. We evaluate our methodology on a real-world dataset comprised of pump-and-dump schemes in cryptocurrency markets, spanning a period of over three years. Our comparative results showcase that our graph-based models achieve significant improvements over standard machine learning baselines in detecting anomalous events. Our work highlights that learned market connectivity provides substantial gains for detecting coordinated market manipulation schemes.