ENHANCING BLOCKCHAIN-BASED MONEY LAUNDERING DETECTION USING ADVANCED MACHINE LEARNING TECHNIQUES

Abstract

The rapid adoption of blockchain technology and cryptocurrencies has transformed digital financial ecosystems, offering decentralization, transparency, and security. However, these same characteristics have also enabled sophisticated money laundering activities, where illicit funds are disguised through complex transaction patterns, layering strategies, and anonymization mechanisms. Traditional anti-money laundering (AML) approaches struggle to effectively detect such activities due to the pseudonymous nature of blockchain transactions and the high volume of data generated across distributed networks. This paper presents an advanced machine learning-based framework for enhancing blockchain-based money laundering detection by leveraging both supervised and ensemble learning techniques. The proposed system integrates data preprocessing, feature engineering, transaction graph analysis, and predictive modeling to identify suspicious patterns within blockchain transactions. Key features such as transaction value, frequency, wallet interactions, temporal behavior, and network topology are analyzed to distinguish between legitimate and illicit activities. Advanced models including Random Forest, Gradient Boosting, and deep learning architectures are employed to capture complex nonlinear relationships and improve detection accuracy. Additionally, the framework incorporates anomaly detection and risk scoring mechanisms to enhance interpretability and support real-time monitoring. Experimental evaluations demonstrate that the proposed approach achieves high accuracy, precision, recall, and F1-score, outperforming traditional detection methods while significantly reducing false positives and false negatives. The system also exhibits strong scalability and adaptability to evolving laundering techniques, making it suitable for large-scale blockchain environments. By combining advanced machine learning algorithms with blockchain analytics, this research contributes to the development of intelligent, automated, and reliable solutions for combating financial crimes in decentralized ecosystems, thereby strengthening trust, compliance, and security in digital financial systems.