Synthetic Pattern Generation and Detection of Financial Activities using Graph Autoencoders

TL;DR

This paper explores the use of graph autoencoders trained on synthetic data to identify topological patterns associated with illicit financial activities, addressing data scarcity and privacy issues.

Contribution

It introduces a synthetic data generation approach for illicit activity patterns and compares different GAE models for pattern detection without labeled data.

Findings

GAE-GCN shows the most consistent performance across patterns

Synthetic data effectively trains models for illicit pattern detection

GAE-SAGE and GAE-GAT perform well on specific patterns

Abstract

Illicit financial activities such as money laundering often manifest through recurrent topological patterns in transaction networks. Detecting these patterns automatically remains challenging due to the scarcity of labeled real-world data and strict privacy constraints. To address this, we investigate whether Graph Autoencoders (GAEs) can effectively learn and distinguish topological patterns that mimic money laundering operations when trained on synthetic data. The analysis consists of two phases: (i) data generation, where synthetic samples are created for seven well-known illicit activity patterns using parametrized generators that preserve structural consistency while introducing realistic variability; and (ii) model training and validation, where separate GAEs are trained on each pattern without explicit labels, relying solely on reconstruction error as an indicator of learned structure. We compare three GAE implementations based on three distinct convolutional layers: Graph Convolutional (GAE-GCN), GraphSAGE (GAE-SAGE), and Graph Attention Network (GAE-GAT). Experimental results show that GAE-GCN achieves the most consistent reconstruction performance across patterns, while GAE-SAGE and GAE-GAT exhibit competitive results only in few specific patterns. These findings suggest that graph-based representation learning on synthetic data provides a viable path toward developing AI-driven tools for detecting illicit behaviors, overcoming the limitations of financial datasets.