Explainable Machine Learning for Systemic Financial Risk Identification

Abstract

Systemic financial risk identification is a distinct machine learning problem because the inferential target is not an isolated event but a destabilizing process arising from interconnected balance sheets, market liquidity, information asymmetries, and institutional behavior. Machine learning models can capture nonlinear interactions and high-dimensional dependencies that traditional econometric methods may miss, yet their opacity creates substantial challenges for supervisors, risk managers, and market participants who must justify interventions under uncertainty. This paper develops a systems-level analysis of explainable machine learning for systemic financial risk identification. It examines the structural trade-offs between predictive capacity and interpretability, the design of model architectures that support meaningful explanation, and the data governance arrangements required for reliable monitoring. The discussion emphasizes that explainability in this setting cannot be reduced to post hoc attribution scores; it must be embedded within regulatory workflows, institutional accountability structures, and operational safeguards. The paper further analyzes robustness, fairness, and deployment sustainability, arguing that explainable models for financial stability require sustained alignment between technical systems and governance institutions. The analysis connects model interpretability to macroprudential policy, model risk management, and cross-border coordination. Rather than proposing a single model class, the paper outlines architectural principles and institutional conditions under which explainable machine learning can contribute responsibly to systemic risk surveillance. The conclusion considers forward-looking research directions, including causal explanation, stress-test integration, and the evolution of regulatory technology.