<![CDATA[This study aims to analyze the role of Artificial Intelligence (AI) in managing emerging risks within the green finance sector through a systematic literature review approach. The growing importance of green finance in supporting sustainable development has been accompanied by the emergence of new and complex risks, including greenwashing, regulatory uncertainty, renewable energy project volatility, and reputational risk faced by financial institutions. These risks are often difficult to predict and manage using traditional risk management frameworks due to their dynamic nature and limited historical data. To address this issue, this study adopts the PRISMA 2020 framework, complemented by the SPAR-4-SLR and TCCM models, to systematically review and synthesize recent academic literature published between 2020 and 2025. The findings indicate that AI plays a significant role in enhancing the effectiveness of risk management in green finance by enabling large-scale data analysis, identifying complex and non-linear risk patterns, and improving the accuracy and timeliness of risk prediction. AI applications, such as machine learning and predictive analytics, are particularly effective in detecting greenwashing practices, supporting regulatory compliance, and strengthening sustainable investment decision-making. However, the implementation of AI in managing emerging risks in green finance still faces several challenges. Key obstacles include limited data availability and quality, potential algorithmic bias, and the absence of clear regulatory frameworks governing the use of AI in sustainable finance. Despite these challenges, the study concludes that AI represents a strategic and promising solution for strengthening sustainable risk governance. This research contributes to the existing literature by providing a comprehensive overview of current trends, challenges, and opportunities related to AI-driven risk management in green finance, particularly in the context of developing economies.]]>
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