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chatReport: Democratizing Sustainability Disclosure Analysis through LLM-based Tools

Author

Listed:
  • Jingwei Ni

    (ETH Zurich)

  • Julia Bingler

    (University of Oxford)

  • Chiara Colesanti Senni

    (ETH Zürich; University of Zurich)

  • Mathias Kraus

    (University of Erlangen)

  • Glen Gostlow

    (University of Zurich)

  • Tobias Schimanski

    (University of Zurich)

  • Dominik Stammbach

    (ETH Zurich)

  • Saeid Vaghefi

    (University of Zurich)

  • Qian Wang

    (University of Zurich)

  • Nicolas Webersinke

    (Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Tobias Wekhof

    (ETH Zürich)

  • Tingyu Yu

    (University of Zurich)

  • Markus Leippold

    (University of Zurich; Swiss Finance Institute)

Abstract

This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (TCFD) recommendations. Corporate sustainability reports are crucial in assessing organizations' environmental and social risks and impacts. However, analyzing these reports' vast amounts of information makes human analysis often too costly. As a result, only a few entities worldwide have the resources to analyze these reports, which could lead to a lack of transparency. While AI-powered tools can automatically analyze the data, they are prone to inaccuracies as they lack domain-specific expertise. This paper introduces a novel approach to enhance LLMs with expert knowledge to automate the analysis of corporate sustainability reports. We christen our tool \textsc{chatReport}, and apply it in a first use case to assess corporate climate risk disclosures following the TCFD recommendations. ChatReport results from collaborating with experts in climate science, finance, economic policy, and computer science, demonstrating how domain experts can be involved in developing AI tools. We make our prompt templates, generated data, and scores available to the public to encourage transparency.

Suggested Citation

  • Jingwei Ni & Julia Bingler & Chiara Colesanti Senni & Mathias Kraus & Glen Gostlow & Tobias Schimanski & Dominik Stammbach & Saeid Vaghefi & Qian Wang & Nicolas Webersinke & Tobias Wekhof & Tingyu Yu , 2023. "chatReport: Democratizing Sustainability Disclosure Analysis through LLM-based Tools," Swiss Finance Institute Research Paper Series 23-111, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp23111
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    Cited by:

    1. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).
    2. Julian Junyan Wang & Victor Xiaoqi Wang, 2024. "Leveraging Large Language Models to Democratize Access to Costly Financial Datasets for Academic Research," Papers 2412.02065, arXiv.org.

    More about this item

    Keywords

    Task Force for Climate-Related Financial Disclosures; Sustainability Report; Large Language Model; ChatGPT;
    All these keywords.

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