AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments

Saeid Vaghefi, Aymane Hachcham, Veronica Grasso, Jiska Manicus, Nakiete Msemo, Chiara Colesanti Senni and Markus Leippold
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Saeid Vaghefi: University of Zurich amd WMO
Aymane Hachcham: University of Zurich
Veronica Grasso: WMO
Jiska Manicus: WMO
Nakiete Msemo: WMO
Chiara Colesanti Senni: University of Zurich - Department of Finance
Markus Leippold: University of Zurich; Swiss Finance Institute

No 25-46, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: Tracking financial investments in climate adaptation is a complex and expertise-intensive task, particularly for Early Warning Systems (EWS), which lack standardized financial reporting across multilateral development banks (MDBs) and funds. To address this challenge, we introduce an LLM-based agentic AI system that integrates contextual retrieval, fine-tuning, and multi-step reasoning to extract relevant financial data, classify investments, and ensure compliance with funding guidelines. Our study focuses on a real-world application: tracking EWS investments in the Climate Risk and Early Warning Systems (CREWS) Fund. We analyze 25 MDB project documents and evaluate multiple AI-driven classification methods, including zero-shot and few-shot learning, fine-tuned transformer-based classifiers, chain-of-thought (CoT) prompting, and an agent-based retrievalaugmented generation (RAG) approach. Our results show that the agent-based RAG approach significantly outperforms other methods, achieving 87% accuracy, 89% precision, and 83% recall. Additionally, we contribute a benchmark dataset and expert-annotated corpus, providing a valuable resource for future research in AI-driven financial tracking and climate finance transparency. 1 * Equal Contributions. 1 We will open-source all code, LLM generations, and human annotations.

Pages: 12 pages
Date: 2025-04
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