In recent years, the concept of Environmental, Social, and corporate Governance (ESG) investing has gained tremendous traction. Not only does it offer opportunities to generate sustainable returns, but it also enables investors to make a positive impact on society and the environment. However, to truly understand the value of ESG, we need to shift our perspective and consider the 'new' stakeholders that are becoming increasingly crucial in this space. In this blog post, we’ll also delve into the challenges of the current ESG rating systems and discuss how AI is transforming the ESG landscape.

Broadening the ESG landscape: Emergence of new stakeholders

Historically, financial analysis has primarily focused on the impact of a company’s actions on its shareholders. Today, however, this view is expanding to include a more diverse array of stakeholders, thanks to ESG analysis - groups that are vital for a company's long-term prosperity. The environment, local communities, government authorities, regulators, NGOs, and journalists now take center stage as new stakeholders in the ESG dialogue.

The environment, for instance, is a stakeholder that companies can no longer afford to ignore. Overexploitation and neglect have led to climate change, thus, the depletion of vital resources and biodiversity, jeopardizing the long-term viability of many businesses. The recognition of the environment as a stakeholder underscores the necessity to balance economic growth with sustainable practices.

Similarly, local communities provide the workforce that companies rely on and need to respect their social environments and fundamental human rights. Governments, often viewed solely as tax collectors, are also stakeholders, providing key services like infrastructure, safety, and the rule of law. Finally, NGOs and journalists, tasked with safeguarding the general interest, ensure transparency and accountability, holding companies to their ESG commitments.

The problem with current ESG ratings

As companies grapple with these complex and interconnected issues, ESG ratings have emerged as a tool to gauge their sustainability efforts. However, these ratings aren't without their flaws.

Firstly, there is a notable divergence of opinion between rating providers, which can lead to confusion and inconsistency. Different providers may emphasize different aspects of ESG, leading to disparate ratings for the same company.

Secondly, most ESG ratings are based on self-reported data, creating an inherent risk of bias or selective reporting. It’s like allowing students to write and grade their own exams, which isn’t ideal for a system aiming to bring transparency and objectivity.

The power of AI in ESG risk assessment

To overcome these challenges, a new player is emerging in the field: Artificial Intelligence (AI). Through Natural Language Processing (NLP) algorithms, AI can analyze billions of documents from a wide range of sources to provide a more objective and comprehensive view of a company's ESG performance.

These AI-driven tools, like those developed by SESAMm, can scan a plethora of information, from press articles and social media posts to reports from NGOs, local press, and governmental bodies. They can detect ESG controversies, positive events, and sentiments linked to various ESG issues. This results in a more detailed and accurate picture of a company's ESG framework that surpasses what current ratings offer.

By bridging the gap between traditional ESG ratings and actual on-the-ground impact, AI provides a novel and powerful tool for investors and companies alike. It fosters a more holistic approach to sustainability, one that takes into account the increasingly complex web of direct and indirect stakeholders.

The future of ESG

In the grand scheme of things, the integration of AI into ESG analysis marks a significant leap forward. By acknowledging the role of new stakeholders and addressing the shortcomings of current ESG ratings, AI is reshaping our understanding of sustainable investing. The road ahead is exciting and promising, and there's no better time than now to harness the power of AI for a more sustainable and inclusive future.

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In a stunning development that signals a potential turning point for global climate finance, the Net Zero Banking Alliance (NZBA) has announced it is pausing all activities and proposing a fundamental restructuring that would effectively end its existence as a membership-based organization. This move represents the most significant retreat yet in coordinated climate finance initiatives and raises questions about the future of institutional ESG commitments.

The Dramatic Unraveling

The NZBA's announcement comes after a series of high-profle departures that began in late 2024. What started as a coalition of 43 banks at its 2021 launch had grown to over 140 institutions representing $74 trillion in assets by 2024. However, political pressure, particularly from Republican politicians in the US, warning of potential legal violations, triggered a mass exodus.

The departures followed a predictable pattern: Goldman Sachs led the way in December 2024, followed rapidly by all major Wall Street peers within weeks. Canadian banks soon followed, and the bleeding continued through 2025 with HSBC, UBS, and Barclays all exiting. Barclays' departure statement was particularly telling, noting that "with the departure of most of the global banks, the organisation no longer has the membership to support our transition."

Proposed Restructuring

The NZBA has now proposed transitioning from a membership-based alliance to what it calls a "framework initiative." This fundamental change would essentially transform the organization from an active coalition with binding commitments to a more passive guidance provider. The steering group believes this approach would be "the most appropriate model to continue supporting banks across the globe to remain resilient and accelerate the real economy transition in line with the Paris Agreement."

A member vote on this restructuring is currently underway, with results expected at the end of September. However, given the exodus of major institutions, the outcome seems predetermined.

Broader Climate Coalition Collapse

The NZBA's troubles reflect a wider crisis affecting climate-focused financial coalitions:

• The Net-Zero Insurance Alliance (NZIA) was discontinued in 2024
• The Net Zero Asset Managers initiative (NZAM) suspended primary activities after BlackRock's exit
• The Glasgow Financial Alliance for Net Zero (GFANZ) underwent significant restructuring

A member vote on this restructuring is currently underway, with results expected at the end of September. However, given the exodus of major institutions, the outcome seems predetermined.

Political and Legal Pressures

The pause stems largely from sustained political pressure, particularly in the United States. Republican attorneys general and politicians have warned financial institutions of potential antitrust violations and threatened to exclude companies from state business. This "anti-ESG" campaign has proven remarkably effective in fracturing what once seemed like unstoppable momentum toward coordinated climate action.

Recent developments include a 23-state coalition warning the Science Based Targets initiative (SBTi) about potential antitrust risks, demonstrating that the pressure extends beyond banking to other ESG frameworks.

Market Implications

The NZBA's effective dissolution has several implications:

Fragmented Approach

Without coordinated frameworks, banks will likely develop individual approaches to climate commitments, potentially leading to:

• Inconsistent standards and methodologies
• Reduced transparency and comparability
• Weakened collective bargaining power with policymakers
  

Regulatory Response

The vacuum left by voluntary coalitions may accelerate regulatory intervention:

• Mandatory climate disclosure requirements
• Government-imposed transition standards
• Regional divergence in approaches

Investment Impact

For investors, this development signals:

• Increased difficulty in assessing bank climate commitments
• Greater need for individual due diligence
• Potential opportunities in banks with strong standalone commitments

Looking Forward

The NZBA's pause represents more than just one organization's troubles; it symbolizes a broader retreat from coordinated climate finance at precisely the moment when such coordination is most needed. With climate risks accelerating and the urgent need for massive capital deployment, the financial sector's inability to maintain collective action represents a significant setback.

However, this may also create opportunities for more resilient, legally defensible approaches to climate finance. Banks that remain committed to transition goals may find competitive advantages in developing robust standalone frameworks, while regulatory bodies may step in to fill the coordination gap.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

Online, Japan Investor Forum, July 2022.

Sylvain Forté, SESAMm's co-founder and CEO, discusses ESG data and its challenges. Further, he describes how to generate insights and reports on millions of companies, including micro-companies, using artificial intelligence and natural language processing.

Below is an approximation of this video’s audio content. Watch the video for a better view of graphs, charts, graphics, images, and quotes the presenter might be referring to in context.

About SESAMm

To give you a bit of context, I’m CEO of SESAMm, a French company of around 100 people that has been in business for eight years and that specializes in artificial intelligence for finance, especially with a focus on ESG.

So we work with some of the largest insurance companies in Japan, such as Tokio Marine, Asset Management One, or Japan Post Insurance. And we have seen the rise of ESG investing in the past few years, especially in the past four years in Europe and in the U.S. And we see now this trend also in Asia and in Japan, more specifically.

Primary uses of ESG data

The primary uses of ESG that we see are first complying with regulation. That is the key priority for most asset managers, but also improving performance. Many quantitative teams are seeing ESG also as a way to have new factors integrated that could qualify to generate alpha in investment funds. ESG is also used a lot in order to better manage risk in portfolio and, finally, to better analyze sustainable investment opportunities.

ESG use cases

So a couple of the main use cases are detecting ESC controversies. So purely from the perspective of generating risk alerts, excluding assets that are not well rated in portfolios, or creating portfolios that contain best-in-class assets, meaning most sustainable assets.

And finally, I want to mention that this trend is really global. So it's across both public assets, equities, and bonds, and also across private equity. And we see private equity reacting very quickly to the ESG trend.

Traditional ESG data challenges

So now, let's discuss in more detail some of the key challenges of ESG data. Traditionally, ESG data is created by teams of analysts that are looking at individual companies that are gathering data from each of the companies, and that are then reading the press in order to complement that information. This approach is relevant, but it is hard to scale, and it presents some difficulty. Traditional ESG ratings agencies are, for example, MSCI or system analytics.

The problem with a lot of traditional ratings is that they don't cover small companies very well. And this is one of the key challenges currently in ESG is the lack of coverage. So it is very difficult to cover small caps, microcaps, and also private companies. In particular, in Asia, the coverage is very poor right now for ESG, and that means that many portfolio companies may not be covered by ESG rating. In Japan specifically, even large companies are sometimes not covered by traditional ESG providers. So that creates a lot of data inefficiency in the industry.

Another key challenge that we see in ESG right now is the frequency of ESG ratings. So oftentimes, ESG ratings are updated only one time per year or just a few times per year. And when ESG ratings are used for risk management, obviously, the market is moving much more quickly than one time or a few times per year.

In addition to that, we see that ESG ratings mostly takes into account information that is reported by management and does not take as much into account information that is from outside of the company. For example, in the case of government scandals, such as fraud scandals, it is actually better to have information that is not reported by the company but that also has an external point of view.

Lastly, the last key challenge I want to mention in ESG data specifically, and one challenge that I'm sure you are aware of in market data and fundamental data is that ESG data is oftentime, not point-in-time. So that means that you don't have a continuous dataset that has not been modified over time. ESG agencies tend to modify their ratings after the fact, and so that means that the rating that you will receive now for a data point in 2020 will not be the same that the rating that you would actually have received in 2020 point-in-time. That creates a lot of problems when you want to back-test data because you cannot reproduce actual historical results.

So these are all of the key challenges that we have identified in ESG data currently, and there are challenges in order to address the needs that we described. But there are actually some solutions that exist.

The solution to ESG data challenges

And one of the key solutions right now that is merging in ESG is the use of artificial intelligence, in particular, what is called natural language processing, meaning text analysis.

What we do at SESAMm and what some other providers do is detecting ESG risks and positive impact with regards to sustainability by analyzing automatically billions of articles and messages in real time. So as an example, we have 18 billion articles and messages from common news websites, from social media, from blogs and forums, and from company reports. And we automatically detect ESG themes and risk and perform sentiment analysis in order to understand whether a company may be exposed to an ESG controversy or whether a company may have positive impact with regards to sustainability.

Advantages of AI for ESG data challenges

And the advantage of AI in that context is that it solves a lot of the challenges that we discussed before. So it helps access higher frequency data, it helps cover small companies, private companies, it helps also find information that is independent, that is public, and that is not necessarily just reported by management, and it also is point-in-time information that can easily be backlisted.

How SESAMm tackles ESG data challenges

So I'll mention a couple of use cases to illustrate that in more detail. But basically, at SESAMm, we create an ESG datasets in order to track more than 90 different ESG risks and also the 17 sustainable development goals in order to precisely identify positive impact. And we do that on millions of companies, not just large public companies but also small caps and also private companies.

SESAMm ESG data use cases

Some of the use cases that I wanted to illustrate for that is using artificial intelligence in order to perform ESG monitoring using alerts. What that means is that we automatically generate ESG alerts on portfolios, for example, of equities or bonds on a daily basis, including portfolios of Japanese equities. And this data is then used by quantitative analysts and also fundamental managers to systematically exclude companies that are exposed to controversies in a portfolio. And this is a very efficient approach to systematically exclude companies that are not sustainable that are exposed to them.

Secondly, we have companies generate ESG signals by combining market data and ESG AI data to generate alpha. So basically, we create long-only and long-term portfolios, and we incorporate these ESG signals in order to improve the alpha of these portfolios.

The two last examples I wanted to mention, one is positive impact. So there is a specific framework called the UNSDGs for sustainable development goals, which is well suited to automatically detecting positive impact actions by a company, such as implementing, for example, a new net zero carbon policy. And we automatically track these announcements and these positive actions that companies perform in order, again, to share this information in the form of alerts to help fundamental managers track the sustainability actions of their portfolio companies and automatically report on them without having to do manual research.

The last use case I wanted to illustrate, and it's going to be my last point, is due diligence in private equity. So this is not only applicable to public assets but also to private assets. As an example, we have the Carlyle Group, a very large private equity company in particular with the Japanese team, and we have them generate various kinds of analytics at the stage when they evaluate the company. And in particular, we help them monitor and track potential ESG risk and sustainability factors which are very important to assess potential private assets opportunities. So this is the last use case that I want to mention. And as you can see, there are many opportunities in a growing field in ESG that started in Europe and came out to Asia. But there are also a lot of the challenges which artificial intelligence can help solve in some cases and which are illustrated with some examples.

Thank you very much.