Explainable Macro-Financial Fragility Detection Using Residual Stress Factors and Temporal Attention Models
Keywords:
macro-financial fragility, residual stress factors, temporal attention, explainable AI, systemic risk, financial stability, deep learning, socio-technical infrastructureAbstract
The increasing complexity and interconnectivity of global financial systems have rendered traditional risk metrics inadequate for anticipating systemic fragility. This paper proposes a novel framework that integrates residual stress factors—latent signals extracted from high-frequency market data—with temporal attention mechanisms drawn from deep learning architectures to detect macro-financial vulnerabilities in a more interpretable and forward-looking manner. The residual stress factor construct, grounded in statistical arbitrage and stress-testing theory, captures nonlinear, regime-dependent deviations from equilibrium that conventional volatility-based measures often miss. Temporal attention models, specifically transformer-style architectures, provide the capacity to learn long-range dependencies and identify the most influential time segments preceding crisis events, thereby offering a degree of explainability that is essential for regulatory acceptance and policy formulation. We examine the structural trade-offs inherent in deploying such a system at the systemic level, including the balance between model complexity and computational sustainability, the tension between predictive accuracy and interpretability, and the challenges of data governance across heterogeneous jurisdictions. The architecture is discussed as a socio-technical infrastructure requiring robust feedback loops between machine learning outputs and human judgment. We further consider the implications for financial stability monitoring, macroprudential policy, and equitable risk allocation across institutions and economies. The proposed framework is not intended as a standalone predictor but as a complementary layer within a broader surveillance ecosystem. By coupling residual stress signals with attention-based explanations, the system aims to support regulators in identifying nascent fragility before it cascades into systemic crises, while also addressing fairness concerns related to model bias and transparency. The paper concludes with a forward-looking perspective on the governance and institutional design necessary to operationalize such explainable fragility detection at scale.
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