Deep Learning for Early Detection of Systemic Risk in Interconnected Financial Markets: A U.S. Regulatory Perspective

Abstract

Building refined artificial intelligence (AI) models on early warning systems (EWS) has revolutionary potential to forecast financial crises, identify unseen systemic risks, and enhance macroprudential supervision. In today's financial markets, where there are a lot of interconnections and volatility, minor signs of heading in the wrong financial direction can easily prove to be catastrophic unless quickly looked into. Conventional statistical instruments seem poorly bio-equipped to record such complex non-linear dynamics thus necessitating the dependency on intelligent data-driven solutions. The work utilises machine learning methods, Python based analytics and statistical modelling to aggregate macroeconomic indicators, market information and novel alternative datasets and turn them into a predictive framework capable of generating early warning signs of the beginning of systemic stress. The process implies intensive data cleaning, more sophisticated feature engineering, and supervised and unsupervised model training. Latent patterns of risk are revealed by utilizing correlation mapping and anomaly detection, which show increased predictive accuracy as compared to other traditional methods. The findings illustrate the abilities of the framework in increasing both timeliness and reliability of early warning, so the policymakers have more time to take preemptive actions. Tabular and graphical visualizations made in Python show a tendency towards risks over time, which is accurate and interpretable at the same time. This study focuses on ethical and practical aspects of the AI implementation in financial governance with the focus on the ways of the model transparency, bias elimination, and the explicability. In sum, the results show that AI-based EWS has the potential to transform macroprudential supervision with benefits of creating resilient financial systems in a new world of elevated uncertainty. The results of this study validated the reasoning that AI-enhanced early warning systems will revolutionize macroprudential oversight and will empower timely and evidence-based decision making. This study explores the process of using AI to ensure more resilient financial rule and serves as a scalable and flexible toolset that will predict and prevent crises in a readily volatile global economy.