Leveraging artificial intelligence to improve Environmental, Social, and Governance (ESG) data and assess the quality of ESG reports

利用人工智能改进环境、社会和治理 (ESG) 数据并评估 ESG 报告的质量

• 批准号: 571240-2022
• 负责人: Morales, Manuel
• 金额: $ 1.46万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Idea to Innovation
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=729094
• 关键词: Leveraging; artificial; intelligence; improve; Environmental

Most of the investors incorporate ESG scores in their portfolio and rely on ESG rating providers. However, there is a lack of consistency in the ESG impact measurements as the rating companies develop their own methodologies and indicators which are often different from other providers. A study shows that there is only 0.54 of correlation between six major ESG rating providers. This brings challenges for investors to make an investment decision based on the ESG ratings as their decisions may vary based on the rating agency. The lack of methodological standard for measuring ESG performance, poses problems not only for investors but also for rating agencies to produce reliable, consistent, and transparent ESG scores.The information necessary to understand the ESG impact of a company is in an unstructured relational format and artificial intelligence (AI) can be used in these efforts. In the traditional manner, extracting insights from these unstructured and qualitative text data, which are social media, companies' reports, earning calls, and news, has been done manually. In our product, we employ AI to effectively automate this extraction and provide more efficient and transparent large-scale analysis. More specifically, we use natural language processing (NLP) to capture the sentiment, entities, information, and semantics from text data and transform the data to interpretable structured data and information. Moreover, we leverage AI to assess the large-scale analysis of climate-related financial disclosures automatically.

大多数投资者将ESG评分纳入其投资组合，并依赖ESG评级提供商。然而，由于评级公司制定了自己的方法和指标，这些方法和指标往往与其他提供者不同，因此在衡量环境、社会和治理影响方面缺乏一致性。一项研究显示，六家主要ESG评级机构之间的相关性仅为0.54。这为投资者根据ESG评级作出投资决策带来挑战，因为他们的决策可能因评级机构而异。缺乏衡量ESG绩效的方法标准，不仅给投资者带来了问题，也给评级机构带来了问题，无法产生可靠、一致和透明的ESG评分。了解公司ESG影响所需的信息是非结构化的关系格式，人工智能（AI）可以用于这些工作。在传统方式中，从这些非结构化和定性文本数据（社交媒体，公司报告，收入电话和新闻）中提取见解是手动完成的。在我们的产品中，我们采用人工智能来有效地自动化这种提取，并提供更有效和透明的大规模分析。更具体地说，我们使用自然语言处理（NLP）从文本数据中捕获情感，实体，信息和语义，并将数据转换为可解释的结构化数据和信息。此外，我们利用人工智能自动评估与气候相关的财务披露的大规模分析。