Evaluating a company’s exposure to environmental, social and governance (ESG) risk is an investment essential. But accessing reliable and timely information can be a challenge, given corporate transparency is uneven, and consistent global regulatory standards are still a work in progress. Now, data science is helping to provide the information investors need.
A skilled artificial intelligence (AI) and data science team can provide new information and ways of flagging, early on, whether a company may have a brewing ESG controversy that could send its stock price plunging, or whether a company may, in contrast, be developing new technologies that could help solve environmental issues.
AI can dig beneath the surface to identify with greater accuracy fresh, high quality, unique and complex data signals – creating new metrics, and drawing on alternative information sources to allow customized modelling. As such, AI is adding a useful new layer of information that can be utilized by a variety of funds, not just those labelled as “sustainable.”
And while these data science signals are just one small addition to the larger investment decision-making process, they represent a big step forward, offering investors the enticing possibility of an early lead in bringing important corporate issues and opportunities to the surface before they have a large material impact. They can also be customized, offering a different approach to ESG rating providers (so called “third party solutions”) that tend to rely on imprecise industry averages.
Shortfalls of existing data: Filling the gaps
AI can assess ESG risk, identify leaders and help fill the gaps in ESG data self-reported by companies themselves. Gap-filling matters because disclosure varies widely. Emerging market companies, for example, tend to disclose less than those in developed markets, small caps disclose less than large caps (it can be expensive), while companies in Europe disclose more than in other regions.
Disclosure also varies by sector and within sectors, as some companies may withhold information intentionally. Even companies releasing social responsibility reports do so infrequently and may not include early-stage problems. The gaps extend to third-party data providers, which don’t comprehensively cover all that investors want to know.
AI can deduce the missing pieces by, for example, using available data points that are correlated with an unreported ESG factor, producing outputs that allow investors to make systematic comparisons.
Here are a few real-life success stories:
Issue 1: Uncovering ESG risks before they become news
Issue 2: Identifying tomorrow’s leaders in ESG innovation by patent filings and R&D
Issue 3: Using AI to assess workforce diversity below the surface
Conclusion: A deeper view of company fundamentals
Assessing financially material ESG risks and opportunities requires going beyond commercially available ESG ratings. Machine learning and AI give active asset managers the insights needed to identify likely sustainability leaders and ESG laggards before the market has priced in these insights.
The process enriches ESG understanding in a thoughtful, deliberate and differentiating way, allowing for more granular investment decisions that can bolster a wide range of ESG-integrated investment strategies.