IDENTIFYING MONEY LAUNDERING ACTIVITIES IN CRYPTOCURRENCY USING MACHINE LEARNING

Abstract

The quick rise of cryptocurrencies has made it easier to carry out complex money laundering schemes via decentralized networks and to create cutting-edge financial services. Conventional rule-based monitoring systems often fail to keep up with the latest techniques of money laundering on blockchain networks. Machine learning is a scalable approach for analyzing extensive transaction data and detecting anomalous, suspicious patterns. The suggested methodology utilizes both supervised and unsupervised models to discern behavioral signs of unlawful activities. To enable identification, it is imperative to gather vital information, encompassing network architecture, transaction frequency, wallet interactions, and temporal patterns. To improve the model's accuracy and robustness, we employ dimensionality reduction and feature engineering techniques. The system is trained on annotated datasets that include both lawful and unlawful transactions. Experimental assessment is more accurate and preserves information more efficiently compared to conventional detection approaches. This approach facilitates the swift identification of high-risk wallets and transaction patterns.