AI-Driven Risk Assessment in Green and Blue Finance: A Comprehensive Analysis

Abstract

The growing urgency of climate change and ocean degradation has increased the need for effective financial mechanisms such as green finance and blue finance to support environmental sustainability. This study examines the role of Artificial Intelligence (AI) in improving risk assessment and decision-making within these financial frameworks. A qualitative and analytical approach is adopted, based on an extensive review of literature, institutional reports, and a comparative evaluation of traditional and AI-driven environmental risk assessment models. The analysis shows that green finance demonstrates strong performance in sectors like renewable energy, green buildings, and sustainable transportation, while remaining relatively weak in agriculture, biodiversity protection, and water conservation. Similarly, blue finance is well-developed in sustainable fisheries, marine transport, and eco-tourism, but faces challenges in marine pollution control, coral reef protection, and coastal community development. The findings further highlight that AI enhances environmental risk assessment through the use of satellite imagery, geospatial data, natural language processing, and predictive analytics. These technologies enable real-time monitoring, improve the accuracy of climate risk predictions, detect greenwashing and blue washing, and support the efficient allocation of financial resources. Overall, the integration of AI transforms traditional risk assessment into a dynamic, data-driven system, strengthening transparency, accuracy, and sustainability in green and blue finance and contributing to long-term environmental and economic resilience.