Volume : 10, Issue : 12, DEC 2024

A MULTI-LAYERED APPROACH FOR DETECTING CRYPTO-LINKED MONEY LAUNDERING USING ENSEMBLE MACHINE LEARNING MODELS

V. BACKIYALAKSHMI

Abstract

The growing adoption of crypto currencies has introduced new challenges in financial crime detection, particularly in identifying and preventing crypto-linked money laundering. Traditional anti-money laundering (AML) systems are often insufficient to handle the complexities of crypto currency transactions, including pseudonymity, decentralization, and high transaction volumes. This research proposes a multi-layered approach using ensemble machine learning models to improve the detection of suspicious activities and potential money laundering schemes in the crypto currency ecosystem. By combining multiple machine learning algorithms, such as decision trees, random forests, and support vector machines, this study aims to enhance the accuracy, robustness, and interpretability of detection systems. The proposed framework focuses on transaction pattern analysis, anomaly detection, and feature engineering to identify hidden illicit activities, minimizing false positives while ensuring real-time monitoring capabilities. The results show that ensemble models significantly outperform individual models, providing a scalable and effective solution for combating crypto-linked money laundering.

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