This review synthesises 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 optimisation 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.
