Applications of Artificial Intelligence in Financial Data Forecasting and Risk Management

This paper presents a novel hybrid deep learning framework for financial data prediction and risk management, integrating advanced artificial intelligence technologies with traditional financial models. The proposed architecture combines Long Short-Term Memory (LSTM) networks and Transformer models through an adaptive fusion mechanism, achieving superior performance in processing multi-source market data. Experimental results on cryptocurrency market data from January 2023 to December 2023 demonstrate significant improvements in both prediction accuracy and computational efficiency. The model achieves a Mean Squared Error (MSE) of 0.00082 and directional accuracy of $76.3 \%$, outperforming standalone LSTM and Transformer architectures by $34.9 \%$ and $20.4 \%$ respectively. The dual-stream processing architecture reduces average inference time to 45 ms while maintaining $94 \%$ scaling efficiency. The risk management framework exhibits robust performance under stress conditions, with maximum drawdown contained within $\mathbf{4. 3 \%}$. The system’s adaptability is evidenced by consistent performance across varying volatility conditions, maintaining prediction accuracy within a $5 \%$ variance band. The attention mechanism effectively captures market regime shifts and microstructure dynamics, providing interpretable insights into prediction drivers. Comprehensive comparative analysis confirms the model’s superiority in handling both short-term fluctuations and long-term trends while maintaining stable performance during extreme market events.