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

点击翻译按钮获取中文摘要

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.