AI-Augmented Human Reliability and Dependency Assessment in Financial Risk: Implications for Systemic Stability and Regulatory Oversight

Financial risk management faces critical challenges in Human Reliability Analysis (HRA) for decisionmaking processes, particularly in quantifying dependencies between sequential decisions and mitigating human error under dynamic market conditions. This paper proposes the AI-Augmented Financial Risk Assessment and Dependency Analysis (A-FRADA) framework, which integrates artificial intelligence with traditional financial risk methodologies to enhance dependency quantification, transparency, and real-time capability. The framework employs a multi-layer architecture combining Bayesian Networks, Gaussian Processes, deep learning, and Large Language Models (LLMs) within an explainable AI (XAI) structure. Through comprehensive architectural diagrams, workflow visualizations, and systematic performance evaluation, we demonstrate and discuss current research that suggest A-FRADA significantly improves accuracy (90-95%), scalability (92-96%), and latency (90-95 ms) while maintaining regulatory compliance through transparent decision-making. Our analysis reveals that the proposed framework not only outperforms traditional and baseline AI methods across all key metrics but also provides a robust, interpretable, and scalable solution for ependency analysis in financial risk chains, supporting both operational resilience and regulatory adherence. This is review paper and all results are from cited literature with focus on graphical and tabular summarization of current landscape.