# Realistic Synthetic Financial Transactions for Anti-Money Laundering Models

arXiv: https://arxiv.org/abs/2306.16424 · HTML: https://mlsanity.com/paper/2306.16424/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 60 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 28 of 28)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting](https://mlsanity.com/paper/2006.09252/) (2020)
- [Can Graph Neural Networks Count Substructures?](https://mlsanity.com/paper/2002.04025/) (2020)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks](https://mlsanity.com/paper/2103.08826/) (2021)
- [A Review on Graph Neural Network Methods in Financial Applications](https://mlsanity.com/paper/2111.15367/) (2021)
- [Scalable Graph Learning for Anti-Money Laundering: A First Look](https://mlsanity.com/paper/1812.00076/) (2018)
- [xFraud: Explainable Fraud Transaction Detection](https://mlsanity.com/paper/2011.12193/) (2020)
- [Provably Powerful Graph Neural Networks for Directed Multigraphs](https://mlsanity.com/paper/2306.11586/) (2023)
- [LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering](https://mlsanity.com/paper/2210.14360/) (2022)
- [Efficient Scaling of Dynamic Graph Neural Networks](https://mlsanity.com/paper/2109.07893/) (2021)
- [Synthesizing Credit Card Transactions](https://mlsanity.com/paper/1910.03033/) (2019)
- [Ethereum Fraud Detection with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2203.12363/) (2022)
- [Knowledge Discovery in Cryptocurrency Transactions: A Survey](https://mlsanity.com/paper/2010.01031/) (2020)
- [Scalable Fine-Grained Parallel Cycle Enumeration Algorithms](https://mlsanity.com/paper/2202.09685/) (2022)
- [Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles](https://mlsanity.com/paper/2301.01068/) (2023)
- [The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research](https://mlsanity.com/paper/1712.08394/) (2017)

## Built on by (most cited first, 30 of 50)

- [Graph Neural Networks for Financial Fraud Detection: A Review](https://mlsanity.com/paper/2411.05815/) (2024)
- [Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation](https://mlsanity.com/paper/2405.19383/) (2024)
- [When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark](https://mlsanity.com/paper/2407.10916/) (2024)
- [Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective](https://mlsanity.com/paper/2401.00965/) (2024)
- [Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection](https://mlsanity.com/paper/2402.08593/) (2024)
- [Temporal Graph Networks for Graph Anomaly Detection in Financial Networks](https://mlsanity.com/paper/2404.00060/) (2024)
- [Exploring the In-Context Learning Capabilities of LLMs for Money Laundering Detection in Financial Graphs](https://mlsanity.com/paper/2507.14785/) (2025)
- [Translating Expert Intuition into Quantifiable Features: Encode Investigator Domain Knowledge via LLM for Enhanced Predictive Analytics](https://mlsanity.com/paper/2405.08017/) (2024)
- [TeMP-TraG: Edge-based Temporal Message Passing in Transaction Graphs](https://mlsanity.com/paper/2503.16901/) (2025)
- [Pretext Training Algorithms for Event Sequence Data](https://mlsanity.com/paper/2402.10392/) (2024)
- [Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering](https://mlsanity.com/paper/2411.02926/) (2024)
- [MEGA: Message Passing Neural Networks for Multigraphs with EdGe Attributes](https://mlsanity.com/paper/2412.00241/) (2024)
- [Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions](https://mlsanity.com/paper/2503.10058/) (2025)
- [Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review](https://mlsanity.com/paper/2503.24259/) (2025)
- [A Graph Machine Learning Approach for Detecting Topological Patterns in Transactional Graphs](https://mlsanity.com/paper/2509.12730/) (2025)
- [PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback](https://mlsanity.com/paper/2601.03149/) (2026)
- [Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling](https://mlsanity.com/paper/2604.01315/) (2026)
- [A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation](https://mlsanity.com/paper/2404.14746/) (2024)
- [Behavioral Sequence Modeling with Ensemble Learning](https://mlsanity.com/paper/2411.02174/) (2024)
- [Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction](https://mlsanity.com/paper/2412.10895/) (2024)
- [Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection](https://mlsanity.com/paper/2506.00654/) (2025)
- [GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering](https://mlsanity.com/paper/2506.04292/) (2025)
- [FD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report](https://mlsanity.com/paper/2507.19402/) (2025)
- [AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions](https://mlsanity.com/paper/2509.11595/) (2025)
- [Hybrid Data can Enhance the Utility of Synthetic Data for Training Anti-Money Laundering Models](https://mlsanity.com/paper/2509.18499/) (2025)
- [Synthetic Pattern Generation and Detection of Financial Activities using Graph Autoencoders](https://mlsanity.com/paper/2601.21446/) (2026)
- [HybridFL: A Federated Learning Approach for Financial Crime Detection](https://mlsanity.com/paper/2602.19207/) (2026)
- [Tide: A Customisable Dataset Generator for Anti-Money Laundering Research](https://mlsanity.com/paper/2603.01863/) (2026)
- [DPxFin: Adaptive Differential Privacy for Anti-Money Laundering Detection via Reputation-Weighted Federated Learning](https://mlsanity.com/paper/2603.19314/) (2026)
- [Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation](https://mlsanity.com/paper/2604.02899/) (2026)

## Abstract (arXiv)

With the widespread digitization of finance and the increasing popularity of cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering -- the movement of illicit funds to conceal their origins -- can cross bank and national boundaries, producing complex transaction patterns. The UN estimates 2-5% of global GDP or 0.8 - 2.0 trillion dollars are laundered globally each year. Unfortunately, real data to train machine learning models to detect laundering is generally not available, and previous synthetic data generators have had significant shortcomings. A realistic, standardized, publicly-available benchmark is needed for comparing models and for the advancement of the area. To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets. We have calibrated this agent-based generator to match real transactions as closely as possible and made the datasets public. We describe the generator in detail and demonstrate how the datasets generated can help compare different machine learning models in terms of their AML abilities. In a key way, using synthetic data in these comparisons can be even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected.