# Hybrid Data can Enhance the Utility of Synthetic Data for Training Anti-Money Laundering Models

arXiv: https://arxiv.org/abs/2509.18499 · HTML: https://mlsanity.com/paper/2509.18499/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Realistic Synthetic Financial Transactions for Anti-Money Laundering Models](https://mlsanity.com/paper/2306.16424/) (2023)
- [Synthetic Data Applications in Finance](https://mlsanity.com/paper/2401.00081/) (2023)
- [LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering](https://mlsanity.com/paper/2210.14360/) (2022)
- [Ethereum Fraud Detection with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2203.12363/) (2022)
- [Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering](https://mlsanity.com/paper/2011.01826/) (2020)
- [Finding Money Launderers Using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2307.13499/) (2023)

## Abstract (arXiv)

Money laundering is a critical global issue for financial institutions. Automated Anti-money laundering (AML) models, like Graph Neural Networks (GNN), can be trained to identify illicit transactions in real time. A major issue for developing such models is the lack of access to training data due to privacy and confidentiality concerns. Synthetically generated data that mimics the statistical properties of real data but preserves privacy and confidentiality has been proposed as a solution. However, training AML models on purely synthetic datasets presents its own set of challenges. This article proposes the use of hybrid datasets to augment the utility of synthetic datasets by incorporating publicly available, easily accessible, and real-world features. These additions demonstrate that hybrid datasets not only preserve privacy but also improve model utility, offering a practical pathway for financial institutions to enhance AML systems.