Predicting systemic financial crises with AI: A macroprudential approach in the U.S. Context

The capacity to avert systemic financial crises remains a core determinant of financial stability and the attenuation of extensive macroeconomic distress. This paper evaluates routes for embedding artificial intelligence (AI) within the macroprudential framework of the United States to enhance the pre-emptive detection of systemic risk. The study uses a secondary-data methodology to synthesize peer-reviewed empirical evidence and authoritative policy documents, organizing the corpus around four interdependent pillars: AI modeling technologies, macroprudential policy instruments, systemic-risk signal metrics, and regulatory infrastructures. The analysis confirms that predictive architectures grounded in Recurrent Neural Networks, eXtreme Gradient Boosting, and Random Forest methodologies yield optimal predictive precision once supplemented by interpretative post-hoc frameworks, with Shapley Additive Explanations (SHAP) emerging as the most potent mechanism of explanatory power. The prevailing regulatory triad countercyclical capital buffers, judiciously calibrated loan-to-value thresholds, and progressively granular probabilistic stress-testing routines is the conduit that translates AI-generated risk signals into judiciously calibrated supervisory measures. Three recurrent structural anchors, the credit-to-GDP differential, the implied volatility gauge, and the configuration of interbank liabilities persistently surface across modeling coalitions, affirming their ongoing empirical significance. The proposed embedding draws additional support from the Dodd-Frank Act and the Basel III framework, which, when considered together, confer a resilient institutional foundation for the prudent incorporation of advanced machine-learning instruments within the supervisory apparatus. The argument posits that integrating advanced artificial intelligence, meticulously validated risk indicators, and a cohesive regulatory framework significantly enhances the robustness of early-warning mechanisms and macroprudential supervision across the entire financial sector.