Machine Learning-Based Evaluation of Financial Risks in Cryptocurrency

Cryptocurrencies, as decentralized digital currencies relying on cryptography, present unique challenges in assessing financial risks. One prominent risk is money laundering, which has become a significant concern in the cryptocurrency industry. This research proposes an innovative machine learning-based approach utilizing Hierarchical Threat Equality and unsupervised machine learning techniques to analyze and evaluate the financial risks associated with cryptocurrency transactions. The findings demonstrate the effectiveness of machine learning algorithms in capturing intricate interconnections between variables and providing accurate risk assessments. This study highlights the potential of machine learning-driven analysis to enhance financial risk management in the ever-changing world of cryptocurrencies.