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Smart Money, Greener Future: AI-Enhanced English Financial Text Processing for ESG Investment Decisions

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  • Junying Fan

    (School of Foreign Languages, Guangzhou College of Commerce, Guangzhou 511363, China)

  • Daojuan Wang

    (Business School, Aalborg University, DK-9220 Aalborg, Denmark)

  • Yuhua Zheng

    (Business School, Beijing Technology and Business University, Beijing 100048, China)

Abstract

Emerging markets face growing pressures to integrate sustainable English business practices while maintaining economic growth, particularly in addressing environmental challenges and achieving carbon neutrality goals. English Financial information extraction becomes crucial for supporting green finance initiatives, Environmental, Social, and Governance (ESG) compliance, and sustainable investment decisions in these markets. This paper presents FinATG, an AI-driven autoregressive framework for extracting sustainability-related English financial information from English texts, specifically designed to support emerging markets in their transition toward sustainable development. The framework addresses the complex challenges of processing ESG reports, green bond disclosures, carbon footprint assessments, and sustainable investment documentation prevalent in emerging economies. FinATG introduces a domain-adaptive span representation method fine-tuned on sustainability-focused English financial corpora, implements constrained decoding mechanisms based on green finance regulations, and integrates FinBERT with autoregressive generation for end-to-end extraction of environmental and governance information. While achieving competitive performance on standard benchmarks, FinATG’s primary contribution lies in its architecture, which prioritizes correctness and compliance for the high-stakes financial domain. Experimental validation demonstrates FinATG’s effectiveness with entity F1 scores of 88.5 and REL F1 scores of 80.2 on standard English datasets, while achieving superior performance (85.7–86.0 entity F1, 73.1–74.0 REL+ F1) on sustainability-focused financial datasets. The framework particularly excels in extracting carbon emission data, green investment relationships, and ESG compliance indicators, achieving average AUC and RGR scores of 0.93 and 0.89 respectively. By automating the extraction of sustainability metrics from complex English financial documents, FinATG supports emerging markets in meeting international ESG standards, facilitating green finance flows, and enhancing transparency in sustainable business practices, ultimately contributing to their sustainable development goals and climate action commitments.

Suggested Citation

  • Junying Fan & Daojuan Wang & Yuhua Zheng, 2025. "Smart Money, Greener Future: AI-Enhanced English Financial Text Processing for ESG Investment Decisions," Sustainability, MDPI, vol. 17(15), pages 1-33, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:6971-:d:1714627
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    References listed on IDEAS

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    1. Tsalis, Thomas A. & Nikolaou, Ioannis E. & Konstantakopoulou, Fotini & Zhang, Ying & Evangelinos, Konstantinos I., 2020. "Evaluating the corporate environmental profile by analyzing corporate social responsibility reports," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 63-75.
    2. Ming-Fu Hsu & Chingho Chang & Jhih‐Hong Zeng, 2022. "Automated text mining process for corporate risk analysis and management," Risk Management, Palgrave Macmillan, vol. 24(4), pages 386-419, December.
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