Machine Learning Integration in Cryptocurrency Trading: A Systematic Review of Fintech Implications

This review synthesizes research on fintech implications of integrating machine learning algorithms into cryptocurrency trading strategies to address the fragmented understanding of their impact on trading efficacy, risk management, and financial innovation. The review aimed to evaluate current knowledge on machine learning applications, benchmark algorithmic trading performance, identify risk mitigation techniques, compare algorithm effectiveness, and examine regulatory and ethical considerations. A systematic analysis of diverse methodologies, including supervised, reinforcement, and hybrid learning models across global computational finance and AI literature, was conducted. Findings indicate that deep learning and ensemble methods significantly enhance predictive accuracy and trading profitability under volatile market conditions, while reinforcement learning frameworks improve dynamic portfolio optimization and risk-adjusted returns. Risk management benefits arise from integrating technical indicators and reward-based safety mechanisms, though universal frameworks remain lacking. Fintech integration advances through blockchain-enabled transparency and automation, yet practical deployment faces scalability and interoperability challenges. Ethical and regulatory discourse is nascent, underscoring the need for responsible AI frameworks to ensure market integrity and investor protection. These findings collectively demonstrate that machine learning substantially transforms cryptocurrency trading strategies, offering enhanced performance and risk control within evolving fintech infrastructures, while highlighting critical gaps in regulatory compliance and ethical governance that warrant focused future research.