Financial Market Crises Prediction using AI-Based Analytics for Risk Management Applications A Hybrid Bi-Directional LSTM and Transformer Framework with Multi-Modal Data Integration

Although quantitative research was done for decades, the existing early warning systems (EWS) for financial crises are basically reactive, that is, they identify systemic deterioration when material damage has already been done. This paper makes the case that this shortcoming is structural: existing econometric and machine learning paradigms work with assumptions that are not appropriate for non-linear, multi-causal, and network behaviors of financial instability. Five critical gaps are identified via systematic review of 847 peer reviewed papers from four streams of literature. To address this, a hybrid modelling approach to combine the advantages of Bi-Directional LSTM networks, Transformer-based temporal encoders, Graph Neural Networks, contagion modelling, regime switching mechanisms and Bayesian uncertainty quantification, is proposed and evaluated on a multi-modal dataset across India, the United States, the European Union, and China between 2000 and 2024. Explainability tools include SHAP and LIME that help in getting probabilistic crisis outputs that support regulatory transparency requirements.