@inproceedings{vaghefi-etal-2026-ai,
    title = "{AI} for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments",
    author = "Vaghefi, Saeid  and
      Hachcham, Aymane  and
      Grasso, Veronica  and
      Msemo, Nakiete  and
      Senni, Chiara Colesanti  and
      Leippold, Markus",
    editor = "Croce, Danilo  and
      Leidner, Jochen  and
      Moosavi, Nafise Sadat",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-demo.34/",
    pages = "492--511",
    ISBN = "979-8-89176-382-1",
    abstract = "Tracking financial investments in climate adaptation is complex and expertise-intensive, particularly for Early Warning Systems (EWS), where multilateral development bank (MDB) and fund reports lack standardized financial reporting and appear as heterogeneous PDFs with complex tables and inconsistent layouts.We introduce an agent-based Retrieval-Augmented Generation (RAG) system that uses hybrid retrieval and internal chain-of-thought (CoT) reasoning to extract relevant financial data, classify EWS investments, and allocate budgets with grounding evidence spans. While these components are individually established, our contribution is their integration into a domain-specific workflow tailored to heterogeneous MDB reports and numerically grounded EWS budget allocation. On a manually annotated CREWS Fund corpus, our system outperforms four alternatives (zero-shot classifier, few-shot ``zero rule'' classifier, fine-tuned transformer-based classifier, and few-shot CoT+ICL classifier) on multi-label classification and budget allocation, achieving 87{\%} accuracy, 89{\%} precision, and 83{\%} recall. We further benchmark against the Gemini 2.5 Flash AI Assistant on an expert-annotated MDB evidence set co-curated with the World Meteorological Organization (WMO), enabling a comparative analysis of glass-box agents versus black-box assistants in transparency and performance. The system is publicly deployed and accessible at https://ews-front.vercel.app/ (see Appendix A for demonstration details and Appendix B for dataset statistics and splits). We will open-source all code, LLM generations, and human annotations to support further work on AI-assisted climate finance."
}