Could the technology behind Siri help funds invest more sustainably?

Under the specter of climate change, investors are putting pressure on asset managers to invest more ethically.

A recent study by McKinsey & Company puts assets that consider environmental, social and governance (ESG) factors in portfolio selection and management at $88 trillion globally. And it’s not Millennials driving the trend. Investors across all age demographics are expressing a desire to invest ethically and reflect greener values.

In the past, sustainable investing might have looked like a sizable exposure to “green bonds” or nixing investments in oil pipelines, but now, the mandate is much broader.  In today’s digital world, asset managers are trying to be more tech savvy about how to invest in sustainable companies without sacrificing potential returns.


An array of new data is at asset manager’s fingertips from social media to satellite imagery to real time mapping of oil tanker routes to emissions data.  Meanwhile, great strides have been made in artificial intelligence, which can be trained to analyze data and identify exactly which companies are most unethical.

A growing number of quantitative hedge funds are attempting to analyze this Big Data with AI techniques, like machine learning algorithms which can be trained to spot patterns that a human might otherwise miss.

The applications of AI in this space are vast—which is part of the problem. Algos could be trained to scan maps and identify a company’s exposure to power stations, mines, farms, oil pipelines, and other practices that impact the environment. Or instead, to forecast how many new oil rigs are being built by assessing construction sites, or identify areas where pollution is high.

A subset of this technology is natural language processing (NLP), an algorithm that can be trained to read text—the powerhouse behind voice recognition programs like Alexa and Siri.  Funds are trying to use NLP to churn through company reports, earnings calls, research papers, news reports, tweets and blogs at a rate much faster than research analysts. The machine is trained to search for keywords and sentiment in text, analyzing what the company managers might be saying in interviews and on social media. The robots could even sniff out if there’s a discrepancy between an organization’s public information and a policy that isn’t being followed.

Another interesting area of research in this space is the use of machine learning to fill in the blanks in data as a means of risk management. This is a huge problem in sustainable investing, as often companies across the globe do not report their emissions data regularly, if at all. So algorithms are being trained to estimate a company’s carbon emission data, carbon emissions and violations data by comparing it with peers.

Finally, AI is being used to attempt to solve the biggest question with ethical investing, precisely what makes a company unethical? Sustainability could refer to a broad swathe of issues from child labor to consumption of fossil fuels to diversity on the company board. Currently asset managers buy ESG ratings from data providers to determine a company’s credentials, but AI could be used to get more a more accurate picture of what aspects of sustainability actually drive returns.

All in all, though the use of technology and data to supercharge sustainable investing is at an early stage, many asset managers believe the success in green investing will come to those who best harness technological innovation.