How Investors Are Using AI To Make Better Sustainable Finance Decisions

Republished By Plato

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Sustainable finance professionals rely heavily on data access and quality. After decades of ESG data gathering, they’re plagued not by a dearth of data but too much data that isn’t always useful.

Disclosure regulations are taking shape to help address this. Meanwhile, generative AI — artificial intelligence that goes beyond analyzing data to produce new information — is emerging as one way to effectively evaluate and invest in less-obvious climate solutions.

Identify potential investments

Strategies to finance assets that facilitate the energy transition clearly fall under the category of climate investments. Secular investing in climate solutions — investment-speak for a broad trend that will persist over a long time — requires a more nuanced assessment of industries, sectors and diverse types of businesses. That’s where generative AI comes in.

Public equities and climate-focused investor ScopeFour Capital, for example, uses its Climate Taxonomy to find and invest in companies that offer one or more of about 100 climate solutions inspired by Project Drawdown’s list of practices and technologies that can reduce greenhouse gas concentrations.

Determining whether a company’s revenue is materially tied to these climate solutions can’t be easily identified using commercial data sets, said Heather Beatty, CEO of ScopeFour Capital. So the firm “maps these climate solutions to financial statements, and uses AI to more quickly determine materiality and actually assign a revenue number to the solutions,” Beatty said.

Sift through public data

Generative AI can also scour publicly available information to find businesses that may be embedding climate solutions into their business but were missed through the initial tagging exercise, according to Beatty.

A company’s move to put capital behind climate commitments is the key measure of progress in a company’s net-zero journey. Sentiment analysis — using AI to discern the emotional tone of climate messaging — could also be a useful indicator.

For example, AI can analyze company earnings call transcripts to gauge the tone of leadership in discussing climate commitments, work to address various ESG topics, or parse remarks regarding products or services that may pertain to climate solutions. This may be especially useful analysis in a time when many corporate leaders are talking less explicitly about ESG.

Access to AI hasn’t revolutionized the day-to-work of sustainable finance practitioners yet, but with demand for generative AI enterprise licenses booming, it’s just a matter of time.