Abstract
Financial crisis early warning systems (EWS) are being reshaped by artificial intelligence (AI), the growth of alternative data, and the increasing interconnection of financial and digital infrastructures. This article revisits AI-enabled EWS through a 2026 lens, moving beyond prediction accuracy to consider systemic risk transmission, cyber resilience, model governance, and the financial stability implications of AI adoption itself. An integrative literature review was conducted using foundational peer-reviewed research on machine learning and crisis prediction alongside recent institutional publications available in 2025–2026, including work from the Bank for International Settlements, the International Monetary Fund, and the Bank of England. The synthesis indicates that machine learning can improve the identification of nonlinear patterns and combine macrofinancial indicators with market-based and textual signals, but its value depends on rigorous validation, stable data pipelines, and decision processes that remain accountable to human authorities. The 2026 policy discussion also highlights a dual relationship: AI can strengthen surveillance and cyber defense while simultaneously amplifying correlated behaviour, concentration risk, third-party dependency, and the speed and scale of cyber incidents. Accordingly, an effective AI-based EWS should combine predictive models with interpretable outputs, scenario analysis, operational-resilience indicators, and clearly assigned intervention responsibilities. The article proposes a layered governance framework linking data and models, institution-level vulnerabilities, system-wide transmission channels, and policy response. It concludes that AI should be treated as decision support within a broader financial stability architecture, rather than as an autonomous substitute for supervisory judgment.
