Tide: A Customisable Dataset Generator for Anti-Money Laundering Research

TL;DR

Tide is an open-source synthetic dataset generator for AML research that creates customizable, graph-based financial networks with structural and temporal laundering patterns, facilitating reproducible benchmarking of detection models.

Contribution

It introduces Tide, a novel generator that produces realistic, configurable AML datasets with temporal dynamics, addressing privacy concerns and enabling comprehensive model evaluation.

Findings

LightGBM achieves 78.05 PR-AUC in low illicit ratio scenarios.

XGBoost reaches 85.12 PR-AUC at higher fraud prevalence.

Datasets reveal condition-dependent model performance rankings.

Abstract

The lack of accessible transactional data significantly hinders machine learning research for Anti-Money Laundering (AML). Privacy and legal concerns prevent the sharing of real financial data, while existing synthetic generators focus on simplistic structural patterns and neglect the temporal dynamics (timing and frequency) that characterise sophisticated laundering schemes. We present Tide, an open-source synthetic dataset generator that produces graph-based financial networks incorporating money laundering patterns defined by both structural and temporal characteristics. Tide enables reproducible, customisable dataset generation tailored to specific research needs. We release two reference datasets with varying illicit ratios (LI: 0.10\%, HI: 0.19\%), alongside the implementation of state-of-the-art detection models. Evaluation across these datasets reveals condition-dependent model rankings: LightGBM achieves the highest PR-AUC (78.05) in the low illicit ratio condition, while XGBoost performs best (85.12) at higher fraud prevalence. These divergent rankings demonstrate that the reference datasets can meaningfully differentiate model capabilities across operational conditions. Tide provides the research community with a configurable benchmark that exposes meaningful performance variation across model architectures, advancing the development of robust AML detection methods.