# Towards Quantum-Ready Blockchain Fraud Detection via Ensemble Graph Neural Networks

arXiv: https://arxiv.org/abs/2509.23101 · HTML: https://mlsanity.com/paper/2509.23101/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models](https://mlsanity.com/paper/2308.05237/) (2023)
- [QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection](https://mlsanity.com/paper/2404.02595/) (2024)
- [From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance](https://mlsanity.com/paper/2307.01155/) (2023)
- [V-ZOR: Enabling Verifiable Cross-Blockchain Communication via Quantum-Driven ZKP Oracle Relays](https://mlsanity.com/paper/2509.10996/) (2025)
- [A Range-Based Sharding (RBS) Protocol for Scalable Enterprise Blockchain](https://mlsanity.com/paper/2509.11006/) (2025)

## Built on by (most cited first, 2 of 2)

- [Proof-of-Spiking-Neurons(PoSN): Neuromorphic Consensus for Next-Generation Blockchains](https://mlsanity.com/paper/2511.02868/) (2025)
- [Algorithmic Compliance and Regulatory Loss in Digital Assets](https://mlsanity.com/paper/2603.04328/) (2026)

## Abstract (arXiv)

Blockchain Business applications and cryptocurrencies such as enable secure, decentralized value transfer, yet their pseudonymous nature creates opportunities for illicit activity, challenging regulators and exchanges in anti money laundering (AML) enforcement. Detecting fraudulent transactions in blockchain networks requires models that can capture both structural and temporal dependencies while remaining resilient to noise, imbalance, and adversarial behavior. In this work, we propose an ensemble framework that integrates Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph Isomorphism Networks (GIN) to enhance blockchain fraud detection. Using the real-world Elliptic dataset, our tuned soft voting ensemble achieves high recall of illicit transactions while maintaining a false positive rate below 1%, beating individual GNN models and baseline methods. The modular architecture incorporates quantum-ready design hooks, allowing seamless future integration of quantum feature mappings and hybrid quantum classical graph neural networks. This ensures scalability, robustness, and long-term adaptability as quantum computing technologies mature. Our findings highlight ensemble GNNs as a practical and forward-looking solution for real-time cryptocurrency monitoring, providing both immediate AML utility and a pathway toward quantum-enhanced financial security analytics.