Integrating Artificial Intelligence into Climate Risk Management: An Intellectual Cartography of a Decade of Research on Financial Decision-Making

With climate change issues becoming more complex, companies, financial institutions, investors, and regulators are facing greater risks, while the growing volume of climate, financial, and sustainability data is creating new opportunities for Artificial Intelligence (AI). Tools such as machine learning, natural language processing, and predictive analytics are now being used in AI-based applications to identify, measure, disclose, and manage climate-related financial risks. Despite this, academic research at the intersection of AI, climate risk management, sustainable finance, and financial decision-making remains scattered across several disciplinary and methodological streams.

This research aims to build an intellectual map of academic research on the subject from 2015 to 2026. Using a systematic literature review approach, it maps the main themes, methodological choices, AI techniques, financial applications, and climate-risk dimensions addressed in the literature. Each selected study is analyzed based on its aim, data sources, analytical approach, use of AI, climate-risk classification, financial context, and key contributions.

Special attention is given to the application of AI to measure climate risks, analyze sustainability and ESG disclosures, identify greenwashing, assess credit risk, manage financial portfolios, supervise banks, and support financial decisions. The study brings together these scattered contributions to create a coherent research landscape, thereby revealing dominant areas of research, emerging links, and gaps. It helps deepen the understanding of the potential of AI to convert complex climate data into financially meaningful knowledge and to foster more transparent, credible, and forward-looking climate-risk management in sustainable finance.