In an increasingly interconnected global economy, financial systems and supply chains are becoming more interdependent, exposing organizations to compounded risks from market volatility, operational disruptions, and geopolitical uncertainties. This paper presents a deep learning framework for cross-domain risk prediction that integrates financial indicators and supply chain variables into a unified analytical model. The proposed framework leverages multi-layer neural networks and recurrent architectures to capture both temporal dependencies and nonlinear correlations between heterogeneous datasets. Experimental results demonstrate that the model effectively forecasts emerging risks, offering improved accuracy over conventional statistical and single-domain predictive methods. By employing adaptive learning strategies and automated feature extraction, the system enables early warning and data-driven decision support for risk mitigation. The study highlights how deep learning can serve as a convergence point for financial analytics and supply chain intelligence, providing actionable insights for policy makers, investors, and logistics managers. Future work will focus on enhancing explainability, integrating reinforcement learning for adaptive response, and extending the model for real-time deployment in large-scale enterprise environments.

Keywords: Deep Learning, Risk Prediction, Financial Analytics, Supply Chain Management, Neural Networks, Predictive Modeling, Cross-Domain Data Integration