<![CDATA[Financial crises have a significant impact on economic stability, making early detection crucial for policymakers and financial institutions. This study aims to develop a Financial Crisis EarlyWarning System by integrating time-varying dependency structures with machine learning techniques for proactive systemic risk detection. This study uses monthly macro-financial indicators from January 2020 to May 2025. The methodology includes rolling copula estimation to capture dynamic tail dependencies, feature engineering to transform copula parameters into systemic risk indicators, and predictive modeling using Backpropagation Neural Networks and Long Short-Term Memory. To ensure robust validation, the dataset is split chronologically (70% training, 30% out-of-sample testing). The results show that Frank Copula optimally captures nonlinear tail dependencies, particularly between stock indices and exchange rates. The predictive model achieves high out-of-sample detection accuracy (Backpropagation Neural Network: 91.2%; Long Short-Term Memory: 92.5%). Backpropagation Neural Networks successfully identified acute short-term shocks during validated crisis episodes, while Long Short- Term Memory detected long-term structural vulnerabilities and projected severe systemic stress in forward-looking forecasts. Conclusion: Integrating copula-based feature engineering with a sequence learning architecture significantly improves early warning accuracy. This integrated framework provides policymakers, regulators, and market participants with a powerful and proactive tool for financial stability surveillance and crisis mitigation.]]>
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