Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The rapid evolution of financial markets and the increasing complexity of risk factors have necessitated the development of more sophisticated risk assessment methodologies beyond traditional econometric approaches. This article presents a comprehensive analysis of deep learning applications in financial risk prediction, focusing on Long Short-Term Memory networks and Transformer-based architectures for market volatility forecasting, credit default prediction, and fraud detection systems. The article shows the limitations of conventional econometric models in capturing non-linear relationships and processing high-dimensional financial data, while demonstrating the superior performance of neural network architectures in identifying complex temporal dependencies and cross-sectional interactions. Key findings reveal that deep learning models consistently outperform traditional approaches across multiple risk domains, offering enhanced adaptability during market stress periods and the ability to process heterogeneous data sources including unstructured text and alternative data feeds. However, the black-box nature of these models presents significant interpretability challenges for regulatory compliance, necessitating the implementation of explainable AI techniques such as SHAP values and attention mechanisms to provide transparent decision-making frameworks. The article explores hybrid econometric-deep learning models as a promising solution that combines the theoretical grounding of traditional methods with the predictive power of neural networks, while addressing regulatory requirements through graduated interpretability frameworks and standardized model validation procedures.