Artificial Intelligence Approaches for Cryptocurrency Money Laundering Detection on Blockchain Platforms

Abstract

The rapid growth of blockchain technology and cryptocurrencies has transformed digital financial transactions, offering enhanced security, transparency, and decentralization. However, these advantages have also created new opportunities for financial crimes such as money laundering, fraud, and illicit fund transfers. Detecting suspicious activities within blockchain networks is challenging due to the pseudonymous nature of cryptocurrency transactions and the large volume of transactional data. This study explores the application of machine learning techniques for identifying money laundering activities in blockchain-based financial systems. The proposed approach utilizes transaction-related attributes, including wallet addresses, transaction values, and transaction frequencies, to classify transactions as legal or illegal. Advanced machine learning models such as Random Forest, AdaBoost, and XGBoost are employed to improve detection accuracy and analyze complex transaction patterns. The framework incorporates data preprocessing, normalization, model training, and transaction analysis to enhance predictive performance. Experimental findings indicate that machine learning models can effectively identify suspicious cryptocurrency transactions with high accuracy, precision, and recall. The study demonstrates the potential of artificial intelligencedriven analytics in strengthening anti-money laundering mechanisms, improving regulatory compliance, and supporting financial investigators in tracking illicit activities across blockchain networks. The proposed framework contributes to the development of secure and transparent cryptocurrency ecosystems while addressing emerging challenges in digital financial crime detection.