A familiar debate has followed ESG data for years. One camp argues that the field generates too much information for any human team to handle, so the work should be left to machines. The other argues that ESG judgments are too consequential to automate, so humans should review everything. Both positions contain a real concern. Neither describes how a credible rating is actually produced.

With the publication of its full Controversy Exposure Score methodology, now public and free to access following the entry into force of the EU ESG Rating Regulation on 2 July 2026, SESAMm is making the answer explicit. A trustworthy rating is not a choice between artificial intelligence and human expertise. It is the disciplined combination of the two, with each doing the part of the work it does best.

The Scale Problem Is Real

Teams that monitor ESG controversies rarely suffer from too little information. They suffer from too much. A single incident can generate dozens of articles within days, in multiple languages, across outlets of very different quality. Multiply that by a global investment universe and the volume becomes impossible to track by hand.

This is the part of the problem that machines are built for. SESAMm's pipeline ingests more than 10 million documents a day, drawn from an input layer of over 30 billion documents that includes licensed global news, public web and media feeds, NGO publications, and public regulatory and judicial filings. It screens controversies for millions of public and private companies, alongside infrastructure projects, state-owned entities and sovereigns. No analyst team could read at that scale, and none should try. Asking people to do machine work is how important signals get missed.

So artificial intelligence carries the load. Natural language processing and machine learning models, including large language models, attribute documents to the right entity, filter for genuine ESG relevance, and group related articles into discrete events and events into continuous cases. This is what allows a controversy that unfolds over weeks to be tracked as one developing story rather than a hundred disconnected headlines.

Why Scale Alone Is Not Trust

A system that reads everything will also, inevitably, misread some of it. SESAMm is direct about this in its methodology, because pretending otherwise would be the opposite of transparency.

Probabilistic language models can misinterpret a historical or hypothetical reference as an active controversy. Automated clustering can occasionally merge two distinct incidents or split one prolonged crisis into fragments. Model accuracy varies across languages, and lower-resource languages or heavily idiomatic content raise the risk of misclassification. These are structural properties of statistical systems, not bugs to be wished away.

This is precisely where scale stops being enough and human expertise becomes indispensable. A number that informs how capital is allocated cannot rest on automation alone.

Where Human Judgment Enters

SESAMm operates a dual-layer human quality-assurance process, and it runs every day.

At the first layer, a dedicated Research and Analytics quality-assurance team reviews data accuracy, both reactively, when a question is raised about a case, a score or a classification, and proactively, by reviewing generated alerts. Where an issue is confirmed, the correction, whether a reattributed entity, a corrected sub-risk tag or the removal of an irrelevant event, is applied at the source, logged, and the affected scores are recomputed on the standard daily cycle.

At the second layer, complex cases and recurring structural issues are escalated to the Methodology Lead, who can update the underlying training corpus so that a category of error becomes less likely in future. This is the detail that matters most. Human review is not a final rubber stamp on top of the machine. It is a feedback loop that teaches the system, so that today's corrections improve tomorrow's automated output.

The same expertise sits at the front of the process, not only the end. Analysts define the 44 ESG sub-risks, design how severity is assessed, and fine-tune the models. The methodology is a human construction that machines then apply consistently at scale.

A Division of Labor, Not a Contest

Seen this way, the old debate dissolves. The question was never whether AI or people should produce ESG ratings. The question is which part of the work belongs to which.

Machines provide reach, consistency and speed. They apply the same rules to every entity, every day, without fatigue or favor.

People provide judgment, correction and improvement. They decide what the system should look for, they catch what it gets wrong, and they raise the standard of the model over time.

The result is a rating that is both broad enough to cover the real world and rigorous enough to be relied upon. Scale without rigor is noise. Rigor without scale never reaches most of the companies an investor actually holds. The value is in the combination.

What This Means Going Forward

The EU ESG Rating Regulation asks providers to disclose how their ratings are built. SESAMm has chosen to disclose the full pipeline, including the role of AI, the points where it can fail, and the human controls that contain it. The aim is not to claim the technology is flawless. It is to show, in detail, why the output can be trusted anyway.

As artificial intelligence becomes more capable, the temptation to remove the human layer will grow. SESAMm's position is the opposite. The more powerful the models become, the more valuable the people who direct them, check them and teach them become. That is the architecture of a rating worth trusting, and it is now open for anyone to read.

To explore the full methodology behind the Controversy Exposure Score, visit sesamm.com/methodology.

Sylvain Forté, CEO and co-founder of SESAMm, presented the following at Finovate 2022. In the presentation, Sylvain explains who SESAMm is, what SESAMm does, including examples, and how it benefits our financial clients.

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

Hi, everyone. Thank you very much for the opportunity to be with you today. I’m very glad to introduce you to SESAMm. I’m Sylvain, CEO and co-founder of SESAMm.

We’re an artificial intelligence company specializing in analytics for investment professionals and [corporations]. We basically extract billions of articles and messages from the web and transform them into actionable insights to make better decisions. We’re a team of close to 100 people now, and we generate insights from more than 20 billion articles and messages.

Immediate access to daily insights

Let me jump straight to the demo and give you a practical example of what we do. So imagine you’re, for example, a bank looking to compute environmental, social, and governance risks on your portfolio on your clients or on your suppliers. Right now, you may have access to ratings, which are updated once per quarter or once per year. We can give you access immediately to timely daily data on all of your companies in order for you to better assess risks and raise early warnings.

Wirecard use case

In this specific example (Figure 1), we look at Wirecard, a company that went bankrupt due to a 2 billion fraud scandal in Germany.

We extracted dozens of thousands of articles and messages on the company, and we can immediately see that there is a huge anomaly in terms of governance risk. The company is basically exposed to fraud accusations, to lawsuits, and the like, things that you don’t really want to see in your clients or your own portfolio.

Furthermore, we can see on this chart that we can get that type of indicator every single day. And we can see that six months prior to the company’s bankruptcy, there were already huge alerts actually here in January 2020, indicating that the company was in a pretty bad situation from the perspective of web content and web data from news to social platforms, blogs, and forums.

We really have the ability to compute live insights for ESG risk, sustainability monitoring, credit, and similar topics. The advantage of the platform is that we can go very deep. You can see here (Figure 2) some of the underlying governance topics associated with Wirecard, such as fraud, embezzlement, and crime—the main accusation—but also things related to anti-competitive practices or corruption.

And furthermore, the platform enables full transparency. This is AI at scale, but the underlying content is actually text articles and messages that you can read in order to understand the situation and see why the company is in that risk position. So with our platform, with our text analysis engine (TextReveal®), you can immediately extract content on your portfolio, your clients, your suppliers, and for example, generate ESG insights, competitive insights, sentiment insights, or credit warnings, for example.

Trusted, reliable, and abundant insights

We are today trusted by major financial institutions, such as Nomura [Holdings] or Raiffeisen Bank in the banking sector, for example, or large private equity firms worldwide. The reason why they trust us is that we can provide data more quickly—so waiting one day instead of waiting three months—to get an indicator. In addition to that, we have better coverage. We’re the only company in the world that can provide information on five million different public and private companies, meaning all of your banking clients, for example, are covered. And finally, we have access to a large variety of sources, from social content to news and blogs.

Insights beyond companies

Another example that is very common—sadly right now—is clients asking us to follow the Ukraine Russia War and to understand the current situation, including by getting access to local content in local languages in Ukrainian, in Polish, in Russian, to really understand the news and social media out there.

You can see here that beyond companies, we actually track sectors, infrastructure projects, and concepts.

Here (Figure 3), Nord Stream, for example, in the context of Ukraine specifically—so as to understand how these two topics are associated on the web—we can see an explosion in terms of volumes of data over time, the news associating this concept more and more, with more than 40,000 pieces of content. And we can see that sentiment over time, as displayed on this curve (Figure 4), decreases very rapidly, so we see the shock on e-reputation, and we can observe that immediately. And, for example, as a bank or as an asset manager, we can use that to assess the potential risk to clients or portfolio companies.

The interesting thing here is that, beyond the graphs and the raw contents, we can look at where the information comes from. Here (Figure 5), you see a lot of information in German, for example, which is not surprising. And you can even follow the Russian propaganda directly from the platform, looking at Russia Today or Sputnik straight from the engine, as these are also sources that we monitor.

And as you can see, these contents are highly customizable and can be used in very specific situations. So this is really a platform as a service (PaaS) that we offer. This is an engine that tracks four million different sources of information, and we can track millions of companies but also even fuzzy concepts, countries, or topics of interest.

Generate analytics from big data with API

One last thought. A lot of our clients integrate with our API; it’s a technical solution. We work a lot with data science teams, data engineering teams, risk teams, quantitative analysts, and heads of innovation. All of these teams are looking to generate analytics from big data and from web content at scale, with solutions that are currently used by dozens of clients worldwide and for which we provide very relevant analytics.

I’ll leave you with three final calls to action.

1. The first one is come see us at our booth. We would be very happy to present the solution in a bit more detail.
2. The second is, please request a demo. You understand that these indicators can be tailored to your needs in real time. So we’ll be very happy to show you a demo at SESAMm.com.
3. And finally, come see us for a free proof-of-concept (POC). We would be very happy to show you how we incorporate these solutions in actual banking tools and in risk management tools.

So the web is now readily available as a system that you can use and that you can rely on in order to generate valuable insights. We’re very happy to provide the solution to the market and to help inform better decisions and to help monitor risks.

Thank you very much.

In a landmark move for sustainable finance, the UK government has announced plans to regulate ESG (Environmental, Social, and Governance) ratings providers. The Financial Conduct Authority (FCA) will soon be tasked with overseeing these firms, marking a major shift from the current hands-off approach. This development comes amid growing concerns about the inconsistency, opacity, and influence of ESG ratings on investment decisions.

Why Regulate ESG Ratings Providers?

The regulatory gap in ESG ratings is clear when compared to traditional credit ratings. Credit rating agencies (like S&P, Moody’s, and Fitch) operate under strict regulatory oversight and well-defined methodologies, which is one reason their assessments tend to be closely aligned. In fact, one study found the top credit agencies’ ratings are 99% correlated, whereas ESG ratings from different providers showed only about 60% correlation. In practice, that means two ESG raters might disagree as wildly as “AAA” vs “BBB” for the same firm in the same period. By contrast, it’s rare to see such divergence in credit ratings because that industry has long been supervised and standardized.

Absent regulation, ESG ratings have been opaque and inconsistent. Regulators and market watchdogs have likened the ESG ratings arena to a “Wild West” in need of a sheriff. An environment “unregulated and opaque” where even companies with poor environmental track records can sometimes score surprisingly well. The lack of transparency in how ratings are determined makes it hard for investors to trust what an ESG score truly reflects. This opacity not only fuels skepticism but also raises the risk of greenwashing, where unsustainable companies might hide behind inflated ESG scores.

New oversight aims to bring transparency, consistency, and trust to ESG ratings. Authorities around the world are now stepping in. For instance, the UK government has introduced legislation to bring ESG rating providers under the Financial Conduct Authority’s remit. Similarly, European regulators (ESMA in the EU) and others in Japan and India are moving toward tighter standards. The consensus is that ESG ratings need basic guardrails, much like credit ratings, to ensure they are rigorous, reliable, and free of conflicts of interest. As one analysis noted, if a credit rating agency were to suddenly downgrade scores at the scale we’ve seen with ESG re-ratings, regulators would have intervened immediately. Treating ESG ratings “similarly” to credit ratings in terms of oversight is increasingly seen as necessary to prevent nasty surprises (read: unexpected discrepancies) and to maintain market stability.

Regulation can address several issues: it can mandate clearer methodological transparency, require disclosure of rating drivers, and enforce governance standards (for example, to manage conflicts of interest if a rater also offers paid consulting). All of these steps would help investors and companies finally peek behind an ESG rating. In other words, examine the underlying factors, rather than taking scores at face value. Ultimately, effective regulation should turn ESG ratings from a black box into a more consistent, credible tool for decision-making.

What the UK Plans to Do

Under the new legislation, any ESG ratings provider serving UK clients will be required to obtain authorization from the FCA. These firms will need to disclose their methodologies, manage conflicts of interest, and maintain proper governance controls. The regulation is designed to align with international recommendations, such as those from IOSCO, and mirrors similar efforts already underway in the EU.

The goal is to bring greater transparency, comparability, and accountability to a market expected to grow significantly in the years ahead. The FCA plans to consult on specific rules later this year, with implementation expected to phase in over time.

Why This Matters

Bringing ESG ratings under regulatory oversight could be a turning point for sustainable investing. With consistent standards and greater clarity on how scores are determined, investors can better understand the rationale behind ratings and compare them more effectively. It could also reduce the risk of greenwashing by forcing providers to show their work.

Of course, some concerns remain. Smaller ESG ratings firms may struggle with the cost of compliance. Others worry that regulation could stifle innovation or lead to market consolidation. But broadly, the move has been welcomed by investors and industry groups as a necessary step toward improving trust in ESG data.

As global regulators push for greater alignment, the UK's framework could help shape a more transparent and robust ESG ratings ecosystem - one that better serves both capital markets and long-term sustainability goals.

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.