The world of Environmental, Social, and Governance (ESG) analysis has witnessed significant transformation over the years. From traditional methods reliant on company disclosures to modern AI-driven techniques, the landscape has evolved to offer deeper and more comprehensive insights. This article delves into the journey of ESG analysis, comparing traditional methods with the potential of AI-driven approaches.
Traditional ESG Analysis
Historically, ESG analysis primarily depended on company disclosures. These disclosures, often in the form of Corporate Sustainability Reports (CSR), were typically issued annually. While valuable, these reports had limitations:
Frequency: Because they were annual, they didn't provide real-time insights.
Structure: Unlike financial disclosures, CSRs weren't always in a structured or machine-readable format.
Depth: They often provided a surface-level view, missing out on many underlying ESG factors.
The AI Revolution in ESG Analysis
With the advent of AI, the approach to ESG analysis has been revolutionized:
- Real-time Insights: AI-driven platforms, like ESG Analytics, offer real-time ESG scores, leveraging sentiment analysis and other AI techniques.
- Depth of Analysis: AI can delve deep into vast amounts of unstructured data, uncovering insights that might be missed in traditional analysis.
- Alternative Data: AI can process and analyze alternative data sources, from social media sentiment to satellite imagery, offering a more holistic view of a company's ESG performance.
- Case in Point: The Power of Alternative Data
The COVID-19 pandemic underscored the value of alternative data. With traditional economic indicators lagging, real-time data sources, like OpenTable's daily restaurant booking data, provided fresh insights into consumer behavior during the crisis.
The Future of ESG Analysis
While traditional ESG data sources, like those from Moody's or Bloomberg, remain crucial, the integration of AI offers a more comprehensive view. By combining traditional data with AI-driven insights from alternative sources, investors can achieve a more nuanced understanding of a company's ESG performance.
Conclusion
The evolution of ESG analysis from traditional methods to AI-driven approaches signifies the industry's commitment to deeper, more comprehensive insights. As AI technology continues to advance, its role in shaping the future of ESG analysis will be undeniable.
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Why is ESG data expensive?
The costs of collecting, analyzing and storing data are not cheap. And unlike financial data, there is no standardized process for determining ESG scores.The complexity of ESG data and the lack of standardization in the process for assessing environmental, social and governance factors also makes it difficult to compare companies on these metrics. Regulators are trying to make ESG information more transparent by mandating that companies disclose them alongside their financials, but this is still materializing globally. Traditional providers such as MSCI or Refinitiv employ armies of analysts to get this data from corporate disclosures (if it exists) and then normalize that data and provide it back to you. This is a very expenive process, with lots of quality control, and importantly - because this data is not disclosed very frequently (companies typically disclose ESG related data annually), there is less incentive to have a continuous subscription to a ESG data feed, along with risk of information leakage. All of this results in very expensive, and limited annual contracts.
Artificial Intelligence is changing the way we create and consume ESG data, which address many of the issues above - but that is a topic for another day.
Why is ESG data expensive? 6
- The costs of collecting
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- The costs of collecting
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