Published by Jessica Weisman-Pitts
Posted on June 13, 2022
5 min readLast updated: February 6, 2026
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By Achin Bhati, Head of ESG Research, Acuity Knowledge Partners
While everyone in financial services talks about ESG, its investment analysts that must meet ever more changeable regulations and demands for data. ESG might now be a mainstream interest compared to where it was a decade ago but as investors try to comprehensively address all aspects of non-financial risk, how to achieve ESG integration is one of the key challenges.
A recent global survey of around 100 ESG investors and investment advisors conducted by Acuity Knowledge Partners flagged ESG integration as one of their most pressing concerns. Investment advisors consider access to reliable data the most material challenge to ESG integration and an increasing number of analysts are in need of more complete and nuanced ESG data to feed into their investment models. In most instances, these investment models have been crafted by investment analysts to suit their particular investment strategies and philosophies. In the absence of standardised ESG-integrated investment models, the widespread problem of ESG data being measured, reported and integrated in different ways is only perpetuated by an evolving global landscape of mandatory ESG disclosures.
Regulatory bodies, investors of all shapes and sizes and activists continue to work on ESG disclosure standards, with noticeable progress being made at various levels. Regulatory development is a positive sign for the ESG industry, but with so many mandates, proposals, definitions and standards, the confusion among investment analysts keeps increasing. This evolving landscape of ESG investing also makes identification of ESG benchmarks, a key factor in investment analysis, a difficult task for analysts. At the same time, ESG data historically being more qualitative in nature makes the process of closing data gaps extremely difficult. This has left investment analysts struggling to find the bandwidth to integrate ESG data into their investment models to make defensible decisions. That’s where technology can play a transformative role.
AI-based solutions could effectively bridge ESG data gaps, but not immediately
Investment firms often rely on external ESG data sources when it becomes unfeasible internally to bridge the data gap without cutting corners on deep-dive analysis. External ESG data sources that cater to the needs of investment firms can take the form of off-the-shelf ESG data providers and bespoke research firms. While each category of player has its own value proposition, off-the-shelf data providers usually develop their own proprietary methodologies to provide ESG data, whereas bespoke research players work as a logical extension of an investment firm’s core investment analysis team and provide a wide variety of research solutions customised to their client’s needs.
In terms of ESG data gaps, data provided by off-the-shelf data providers is often tied to the proprietary view of the data provider with low or no flexibility of customisation in terms of factoring in additional important sources, such as non-public information in the hands of issuers. This is especially important, as ESG awareness is lower among companies that operate in emerging markets, often due to cost concerns, and it is necessary to engage with these issuers to avoid any potential data gaps.
The challenges associated with collecting ESG data from company disclosures have driven all industry participants to adopt technology solutions to help collect large volumes of ESG data. Of the technology solutions currently available, data science and Artificial Intelligence (AI) are increasingly making their way into ESG research. Using machines to read and collate the required ESG data from lengthy company disclosures is a solution to cut information processing time, reduce costs and optimise portfolio returns. It also enables investment analysts to make better investment decisions by focusing on the core area of deep diving into ESG data to integrate it effectively into their models.
Investment analysts should still adopt a degree of scepticism from those who currently claim that AI platforms can solve all their data integration issues. As leading technology solutions providers know, pure AI solutions are still in their infancy. What existing technology can do is to provide interim solutions that still allow investors and investment analysts to prioritise and integrate huge amounts of often very qualitative information.
One example of this amongst investors and ESG data providers is the increasing use of Natural Language Processing (NLP). A branch of AI, NLP technology can read large chunks of unstructured data in company reports and transform them into structured data for storage in a database and for later use for different purposes. NLP solutions aim to replicate a human’s reading of reports and are proving to be a scalable alternative for gathering ESG data. As the understanding of a specific ESG data use case becomes clearer over time to a team implementing NLP, they would learn to constantly calibrate the NLP models to improve accuracy in mining this data.
What’s clear to all industry participants is that the implementation of technology solutions to deal with investor and regulatory demands continues apace, especially when tied to investment analysts’ need to integrate ESG data into investment models. The financial services industry has historically been a big spender when it comes to tech. The questions for investment analysts are, is that spend delivering the insights needed and the returns demanded? If not, it may be time to reconsider what technology you’re implementing.
This is a Sponsored Feature.
ESG stands for Environmental, Social, and Governance. It refers to the three central factors used to measure the sustainability and societal impact of an investment.
AI in finance refers to the use of artificial intelligence technologies to analyze data, automate processes, and improve decision-making in financial services.
Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and respond to human language in a valuable way.
Investment models are frameworks or strategies used by analysts to evaluate potential investments based on various financial metrics and risk factors.
ESG benchmarks are standards or points of reference used to measure the performance of investments based on environmental, social, and governance criteria.
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