The backlash against environmental, social, and governance (ESG) investing can be quelled with an overhaul from artificial intelligence (AI), according to Nigel Green, chief executive of DeVere Group.
Green said ESG had become a "lightning rod for controversy", particularly in the US where Republican lawmakers, among others, had framed it as ‘woke capitalism’, at odds with traditional investment principles.
The resultant backlash, including blacklisting of major financial groups and legislative restrictions in certain states, underscored the urgent need for a re-evaluation of ESG frameworks, he added.
However, Green believes that there is a transformative potential in emerging technologies, particularly AI, which he claims will "redefine and revitalise responsible investing"
Additionally, while the rift between EU and US investors on ESG widens, according to Green, there remains a common ground: the recognition of individual investors’ growing interest in responsible investing.
And to harness this momentum effectively, a "fundamental shift" is required in how companies are evaluated through an ESG lens.
Green added: “This shift goes beyond mere rebranding. It necessitates a major change in assessment methodologies, moving away from rigid scores and checklists towards deeper, more nuanced analyses aligned with investor priorities.
“This is where we see AI poised to revolutionise ESG assessment by leveraging its unparalleled capabilities in data analysis, pattern recognition, and predictive modelling.”
Green points to natural language processing (NLP) algorithms, as an example of AI technology that can can sift through vast amounts of textual data from corporate reports, news articles, social media, and regulatory filings: "By discerning nuanced signals of ESG-related risks and opportunities, AI-driven platforms provide investors with holistic insights into a company’s sustainability performance.”
Additionally, AI-powered predictive analytics can enable investors to anticipate ESG-related market shifts and identify emerging risks and opportunities.
“For instance, machine learning algorithms can dynamically adapt to evolving ESG trends and investor priorities, ensuring that investment decisions remain aligned with the most pressing sustainability challenges of our time," Green said.
“By incorporating real-time data feeds and sentiment analysis, AI empowers investors to make informed decisions that drive positive societal and environmental outcomes while delivering competitive financial returns.”
Green added that AI-driven risk management tools offered unparalleled precision in aligning investment portfolios with investor-defined ESG objectives, while optimisation algorithms can construct diversified portfolios that not only maximise financial returns but also mitigate ESG-related risks.
By incorporating sophisticated risk modelling techniques, AI can also quantify and manage ESG-related risks, enhancing the resilience of investment portfolios in the face of environmental, social, and governance disruptions.
For example, AI-powered geospatial analytics and remote sensing technologies can enable investors to monitor and assess environmental metrics such as carbon emissions, deforestation rates, and water usage with unprecedented accuracy and granularity.
And by integrating satellite imagery, IoT sensors, and AI algorithms, investors gain real-time insights into supply chain sustainability, identify potential ESG-related risks, and drive targeted interventions to mitigate environmental degradation.
Similarly, with social impact investing, Green said AI-driven analytics could assess companies’ social license to operate and their contributions to societal well-being.
