Artificial Intelligence Can Solve the Issue of Sustainable Data Reporting
Hypothetically, you have the option to either invest in firm A or in firm B. Both these firms have an impressive growth trajectory and are witnessing consistent profitability at more or less similar rates. As an investor, you are likely to get confused about where to channel your investment. You start looking at each firm’s annual reports. You find that from a business perspective, both firms may be doing equally well but in addition, firm A is making conscious efforts towards putting in place a circular economy and firm B isn’t. This information might play a key role in your decision to invest. What seemed like a tough call gets resolved instantaneously. You choose firm A. In recent years, Environmental Social and Governance (ESG) investing has gained incredible momentum in the context of heightened concerns surrounding climate change and the pandemic. As an investment strategy, ESG investing entails a keen assessment of the environmental, social and governance factors by investors alongside several financial factors. While ESG reporting has been prevalent in India since 2009, in May 2021, SEBI introduced a new ESG reporting structure, Business Responsibility and Sustainability Report (BRSR). Under BRSR, the top 1000 listed entities are mandated to disclose their sustainability performance in great detail. An in-depth overview of their ESG risks and opportunities, approaches to mitigate the risks along with financial implications of the same are included in this report. However, a key concern that plagues ESG reporting is ‘greenwashing’. Enterprise executives often seem to boast about their ESG considerations more than the actual on-the-ground scenario. Oftentimes, the data they provide with respect to ESG parameters are highly biased or immaterial. The demotivation to invest in such ESG-centric aspects is likely driven by the understanding that although these initiatives may be sound environmentally, socially and governance-wise, they typically aren’t of any material use to the company’s business as a whole. Additionally, the lack of data makes it more challenging for the investors to accurately assess the risks and benefits associated with investing in an ESG-compliant venture. A 2021 joint study “Climate Leadership in the Eleventh Hour” by United Nations Global Compact and Accenture found that 63% of CEOs opine that the difficulty in measuring ESG across the value chain is a barrier to sustainability in their industry. But what is the solution? Should the lack of reliable data render ESG reporting susceptible to greenwashing? Well, definitely not. With its rapid developments, technology today can resolve the most pressing challenges faced by humanity. In this case as well, artificial intelligence might be the way out. Integrating AI in assessing ESG landscape Artificial Intelligence that simulates human intelligence to perform day to day human tasks can act as a catalyst for ESG investment by filtering out  essential data on ESG metrics that investors currently lack. AI can find out very fine-grain nuggets of information from massive unstructured databases swiftly to provide accurate details to prospective investors. A key challenge with regard to ESG metrics is that much of the data that is relevant lies across the entire supply chain. The data doesn’t relate solely to the organisation but also to its suppliers and the supplier of its suppliers and so on. Thus, consolidating them at one place to give a valuable insight for investors is decidedly a herculean task to undertake. In such scenarios, AI can be of immense help. It can help glean relevant data, manage it, draw appropriate insights from it and, eventually, operationalise it. It is notable that several ESG investors also consider AI due to its rapidly evolving potential with respect to sentiment analysis. In sentiment analysis, AI algorithms allow computers to analyse the tone, type, context, pattern and texture of a conversation. Using natural language processing, these algorithms  identify parts of a conversation where a CEO or any other top executive from a certain firm discusses ESG-related topics. Once identified, the algorithms draw inference about how committed a firm is to ESG criteria. This carefully curated sentiment analysis not only allows prospective investors to make effective decisions but also helps keep greenwashing in check. The rising relevance of ESG in investment decisions and the potential of AI,  machine learning, big data analysis and natural language processing in guiding such decisions has led to the emergence of several ESG analytics firms. Futurescape, Environment Social Governance Data and Solutions and ESG Risk and Assessments Insights are some of the organisations engaged in ranking companies with regard to ESG. In 2021, the rating agency CRISIL launched environmental, social and governance (ESG) scores for 225 companies  across 18 sectors in India.As data sources continue to evolve and complicate the processes of generating a standard ESG report, AI algorithms are expected to improve the quality, quantity and reliability of the collated data to consequently improve the overall accuracy of sustainable data reporting. Incorporating AI in investment decisions could help accelerate the pace towards realising sustainable development goals while doing away with the current risks and challenges associated with ESG investing.
Several ESG investors consider AI due to its rapidly evolving potential with respect to sentiment analysis.
Several ESG investors consider AI due to its rapidly evolving potential with respect to sentiment analysis.
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["big data", "Go", "API", "machine learning", "artificial intelligence", "sentiment analysis", "AI", "analytics", "GAN", "R"]
["AI", "artificial intelligence", "machine learning", "analytics", "sentiment analysis", "R", "Go", "API", "big data", "GAN"]
Zinnia loves writing and it is this love that has brought her to the field of tech journalism.