Machine Learning in Money Laundering Detection over blockchain technology

Abstract

Money laundering has evolved significantly with the emergence of blockchain technology and cryptocurrencies, introducing new challenges in financial crime detection. Although blockchain provides transparency through distributed ledgers, the pseudo-anonymous nature of transactions enables illicit actors to obscure fund flows and conduct illegal activities. Traditional rule-based Anti-Money Laundering (AML) systems are often inadequate for handling the high volume, velocity, and complexity of blockchain transaction data. This paper presents a Machine Learning-based framework for detecting money laundering activities over blockchain networks. The proposed system utilizes transaction graph analysis, feature engineering techniques, and supervised learning algorithms to identify suspicious patterns and anomalous behaviors. Key features such as transaction frequency, wallet clustering, temporal patterns, and network centrality measures are extracted to enhance detection accuracy. Various classification models including Logistic Regression, Random Forest, and Gradient Boosting are evaluated to determine optimal performance. Experimental results demonstrate that the integration of machine learning techniques significantly improves detection accuracy, reduces false positives, and enhances real-time monitoring capabilities. The proposed approach provides a scalable and efficient solution for strengthening AML compliance in blockchain-based financial ecosystems.