AI-Driven Framework for Financial Risk Management: Enhancing Decision-Making with LSTM Networks and Probabilistic Models

Artificial intelligence (AI) technology’s fast development has changed corporate financial management by allowing more precise and efficient financial decisions. This paper offers an AI-driven computational framework meant to surpass the constraints of conventional financial risk analysis techniques in managing high-dimensional, non-linear, and dynamic financial data. The framework improves risk modeling and prediction skills by integrating sophisticated deep learning architectures, especially Long Short-Term Memory (LSTM) networks, with probabilistic models like Bayesian networks. Key elements of the study are data preprocessing and feature engineering; missing values, outliers, and high-dimensional time-series data are handled using Principal Component Analysis (PCA); deep learning model design using LSTM networks to capture long-term dependencies and temporal patterns in financial data; and AI techniques combined with conventional risk metrics, such Value-at-Risk (VaR), to enhance stability and interpretability. Experimental validation with publicly available financial information reveals that the suggested framework beats conventional approaches in prediction accuracy and computing efficiency by a large margin. While lowering prediction latency for real-time applications, the LSTM-based model outperforms baseline techniques in Area under the Curve (AUC) and F1-scores. This paper offers companies sophisticated tools for data-driven, adaptable, and dependable decision-making in dynamic financial contexts, hence underlining the transforming power of artificial intelligence in financial risk management.