The growing role of artificial intelligence (AI) across financial markets has reshaped how trading firms, banks, and asset managers tackle core operational challenges—from building predictive trading systems to managing risk and satisfying regulatory demands. This paper offers a structured review of AI-driven approaches applied to these domains, tracing the progression from classical statistical techniques to cutting-edge deep learning. Specific attention is given to Long ShortTerm Memory (LSTM) networks, Transformer-based language models, reinforcement learning agents, and hybrid ensemble architectures. An original comparative study—drawing on a curated dataset that combines price data, order-book features, and news sentiment—reveals that large language models fine-tuned alongside recurrent neural networks consistently surpass conventional baselines in forecasting both price direction and market volatility. Measured improvements in accuracy reach up to 19.9 percentage points versus support vector machines, while annualised portfolio returns exceed a passive benchmark by more than 4.1%. The paper also examines persistent systemic challenges: limited historical data in tail-risk scenarios, the opacity of black-box models, alignment with evolving regulations, algorithmic bias, and the threat of flash crashes. A forward-looking research agenda addresses explainable AI (XAI) tailored for finance, federated learning that enables privacy-preserving collaboration, and causal inference frameworks designed to separate genuine market signals from spurious correlations.

Artificial Intelligence,financial market,deep learning,LSTM model,algorithmic trading,risk management,Financial Fraud Detection,multimodal sentiment analysis,Reinforcement Learning,Large Language Model