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

Building on the foundational literature synthesis presented at Conference 1, this paper shifts focus to the methodological core of the research programme. The study addresses a well-established inadequacy in the financial crisis prediction literature: the absence of a unified, real-time, multi-factor early warning framework capable of operating across multiple countries, asset classes, and data modalities simultaneously. Five critical knowledge gaps are formally defined and operationalised into testable experimental designs. The proposed methodology integrates Bi-Directional Long Short-Term Memory (Bi-LSTM) networks, Transformer-based temporal encoders, dynamic Graph Neural Networks, Hidden Markov Model regime-switching mechanisms, and Bayesian uncertainty quantification — applied to a curated multi-modal dataset spanning India, the United States, the European Union, and China from 2000 to 2024. Each experiment is mapped directly to a research hypothesis, supported by a defined dataset, specified tools, and measurable outcome criteria. The paper demonstrates methodological rigour by aligning experimental design with validation protocols drawn from both machine learning benchmarking standards and financial econometric practice.