Intelligent Anti-Money Laundering on Cryptocurrency A CNN-GNN Fusion Approach

The decentralized structure of cryptocurrency has changed modern finance, but its anonymity also allows for many illegal acts such as money laundering, phishing, and other frauds. This paper presents a hybrid detection system. It combines Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) to find suspicious Ethereum transactions. We use data from May 2022, June 2022, and July 2024. CNNs pull out local transaction details. GNNs map how accounts connect. The node features are then classified with ensemble tools like Random Forest and XGBoost. This brings clear gains over older models-up to 5.79% higher precision and 18.1% higher recall. Tests on July 2024 data spotted new fraud types, including NFT scams and WazirX hack-linked transfers. This shows the model works well for protecting DeFi systems. CCS CONCEPTS Mathematics of computing ~Probability and statistics ~Probabilistic inference problems