FinTech

The accelerating digital transformation of finance is reshaping the institutional and analytical environment in which central banks address climate-related financial risks while maintaining price and financial stability. This paper examines the interplay between Artificial Intelligence (AI), Sustainable Development Goals (SDGs), and the green transition, with particular attention to their implications for monetary policy and climate-risk assessment in central banking. The study does not apply AI or machine-learning techniques empirically; rather, it adopts a qualitative and institutional perspective to examine the potential role of AI and broader digital capabilities in strengthening the identification, assessment, and monitoring of physical and transition risks within central banks’ mandates. The study adopts a conceptual and policy-oriented analytical approach based on evidence from Eurostat Sustainable Development Goal indicators, the Network for Greening the Financial System (NGFS) climate scenarios, European Central Bank (ECB) reports, and European Systemic Risk Board (ESRB) analyses. These institutional frameworks are comparatively examined to assess how climate scenarios, stress-testing methodologies, sustainability indicators, and digital analytical capabilities can support macro-financial risk assessment and climate-aware monetary policy. Particular attention is given to the institutional mechanisms through which these approaches can contribute to understanding the transmission of climate-related risks to inflation, financial stability, and banking-sector resilience. The findings suggest that the integration of SDG indicators, climate scenarios, climate-risk assessment frameworks, and emerging digital capabilities provides a more comprehensive basis for understanding the implications of climate-related risks for monetary and financial stability. The ECB’s Climate and Nature Plan 2024–2025 illustrates how digital technologies, enhanced data capabilities, and climate-risk modelling are increasingly incorporated into monetary-policy implementation, prudential supervision, and portfolio management. Within this broader digital transformation, AI represents an emerging analytical capability that may support the processing of complex datasets and the monitoring of climate-related risks. However, its effective use requires appropriate governance, data quality, transparency, institutional coordination, and regulatory safeguards. The analysis also highlights potential trade-offs between sustainability objectives and traditional central-bank mandates. This paper contributes to the emerging literature on sustainable finance, climate-aware monetary policy, and the digital transformation of central banking by developing an integrated institutional framework linking AI, SDG indicators, climate scenarios, and climate-risk assessment. The findings provide policy-relevant insights into how central banks can enhance their analytical and institutional capacities to address climate-related financial risks and support the green transition while maintaining price and financial stability. Full article