AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908382541119488 |
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| author | Vaghefi, Saeid Ario Hachcham, Aymane Grasso, Veronica Manicus, Jiska Msemo, Nakiete Senni, Chiara Colesanti Leippold, Markus |
| author_facet | Vaghefi, Saeid Ario Hachcham, Aymane Grasso, Veronica Manicus, Jiska Msemo, Nakiete Senni, Chiara Colesanti Leippold, Markus |
| contents | 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 retrieval-augmented 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05104 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments Vaghefi, Saeid Ario Hachcham, Aymane Grasso, Veronica Manicus, Jiska Msemo, Nakiete Senni, Chiara Colesanti Leippold, Markus Computation and Language 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 retrieval-augmented 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. |
| title | AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.05104 |