Opportunities and Practical Challenges of Risk Management for AI-Driven Financial Analysis Under the Background of Sustainable Finance Development
DOI:
https://doi.org/10.54097/mbn73n57Keywords:
Sustainable finance, Artificial intelligence, Financial analysis, Risk management, Institutional governanceAbstract
The institutional architecture of sustainable finance is moving from principles to hard limits. The artificial intelligence is entrenched in the financial analysis, and it is changing the reasoning of risk management. This article explains the application of conventional AI to integrating ESG data, quantifying climate transition risk, pricing carbon financing, and evaluating the effectiveness of AI in risk management. We also discuss some of the restrictions including data quality, model transparency, algorithm convergence and institutional latency. The effectiveness of AI-enabled sustainable finance risk management depends on the comprehensiveness of the data infrastructure, the level of model interpretability attained and the flexible adaption of the governance framework. The green development framework of the "15th Five-Year Plan" has been fully launched. The EU "Artificial Intelligence Act" has come into effect in August 2024, and the ISSB standards have been consecutively implemented in several nations. The institutional environment accelerates the reconfiguration. Based on this, this study proposes improvement suggestions from the three aspects of data governance, model regulation and regulatory coordination, and calls for seeking the dynamic balance between technological innovation incentives and systemic risk prevention.
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