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Intro to SESAMm

Thank you very much, Greg. Thank you, everyone, for listening to this presentation. I’m Sylvain. I’m CEO and co-founder of SESAMm. SESAMm is an AI company. We extract billions of articles and messages from the web in order to identify critical insights related to financial institutions and corporates. We’re a team of close to a hundred people. And what we aim to show you today is our new product that helps financial institutions and corporates identify ESG controversies in the form of alerts on all of their investments, on all of their clients, and all of their suppliers.

So there are more than 23 million companies in the world right now. These companies are your investments, your suppliers, your clients, and no one is actually tracking them. Most of these companies are never tracked day to day. SESAMm’s solution aims at automatically identifying controversies on these companies and finding the critical information that you’re missing.

See a dashboard example

So let’s take a quick example first. Here we have dashboards where we analyze a company called Wirecard. Wirecard is a fintech company—German—that went bankrupt a few years ago due to a two billion fraud scandal. That company was heavily embedded into the financial sector, working with a lot of banks, a lot of corporates worldwide.

On our dashboards, we can immediately identify all of the key controversies and all of the key risks on the companies. And we have a score called a virality score that helps assess the severity of each ESG event so as to understand whether that company should be excluded from your list of suppliers, for example, or even discussed as a client.

SESAMm solution benefits

There are key benefits to providing this information and to the way that this product is brought to the market. First, SESAMm covers more companies than anyone else. We cover close to five million firms, whereas most ESG providers have coverage limited to 50,000 firms in total. In addition to that, we’re able to detect controversies in real time and generate daily alerts where normally a bank, for example, would have to go through that process manually and update it just a few times a year instead of receiving that live information.

In addition to that, as you can see on the demo here, we have information for more than 14 years of data. So anytime you onboard a new supplier, anytime you check for information—ESG information, on a new client, or on an investment—you’ll automatically be able to go back in history and understand whether that company was exposed to issues in the past.

Trusted by major financial institutions

SESAMm solutions are already adopted by major banks such as Raiffeisen or Nomura, for example, in this industry, major private equity firms such as Carlyle. And what’s interesting in this solution is that we’re seeing specific interests from commercial banks that are missing the solution in order to track ESG risk on their suppliers and their clients. And it makes sense. Most of these suppliers and clients are small firms, local firms that no one else is going to track. And AI is enabling us to automate the process of monitoring these firms and making sense of that data in real time.

SESAMm's solution in action

So now, let’s go to the second part of the demo. We want to take an actual life example. So let’s take a company like Twilio, for example. So you may know Twilio communications, API, messaging services, phone services, and the like. This company is a typical provider of banks or of financial institutions or any other corporates in the world.

So you see on the left, we immediately identify all of the information related to Twilio. And we can rank this based on negative sentiment so as to understand what are the key critical topics that I should care about and that I should evaluate before actually working with Twilio or in the context of already working with Twilio. We go through that process by handling more than 20 billion articles and messages from more than four million sources worldwide. So that’s an insanely large amount of information.

And on Twilio—say Twilio is one of your suppliers or one of your clients—we immediately identify a large controversy related to a data breach and cybersecurity issue, and we identified both in news but also in some of the specialized cybersecurity websites. In addition to that, we can go in even more granularity and look transparently at the content themselves, read the contents from the platform, and not just rely on a numeric rate saying that “Hey! This company is problematic.” We can actually read the underlying content and understand how the controversy emerged.

SESAMm solution benefits

So the key benefits and the real advantages of that solution is getting information immediately. You don’t have to wait for a due diligence for someone to check for someone to send a questionnaire to the company. You just type in the name, get the information in a few seconds wherever the company is, and however local that company is. It could be the most obscure company. And as you can see our system also covers many different languages, including Asian languages that are monitored automatically.

The second part is that we have access to millions of sources, including very industry-specific sources. I was mentioning cyberthreats. We also have access to NGO websites that identify these types of ESG issues in real time.

So this is really the information that is aimed at helping you monitor controversies and ESG events in just one place on any number of companies, public and private, whether they are your suppliers, your clients, or your investments. You can make sense of that data in real time using AI.

Presentation summary

I’ll finish this presentation a bit early, and we’ll actually bring the point to three calls to action. The first one is, first, please come to our booth. We’re actually on the left of the exhibit hall right when you come in. The second one is, please visit our website. It’s spelled SESAMm, sesamm.com, and you can get a free trial from the website. And finally, come talk to our amazing team with Dave and the rest of our team at our booth. And please ask us for a free POC—whether you’re a bank, an asset manager, or a fintech company—and help us help you track all of the ESG controversies on millions of companies.

Thank you very much.

Reach out to SESAMm

TextReveal’s web data analysis of over five million public and private companies is essential for keeping tabs on ESG investment risks. For example, ESG Alerts provides a better way to research and monitor your investment portfolio. To learn more about how you can analyze web data or to request a demo, reach out to one of our representatives.

Environmental, Social, and Governance (ESG) factors have moved from the periphery to the core of investment and risk management discussions. While positive ESG actions are often linked to better financial stability, new research from the Norwegian University of Science and Technology reveals the flip side: ESG controversies significantly increase a company’s exposure to systemic risk.

Published in Finance Research Letters (2025), the study "Environmental, Social and Governance Controversies and Systematic Risk: A Machine Learning Approach" sheds new light on the complex relationship between ESG controversies and firm-level risk, using advanced machine learning (ML) techniques to provide unprecedented insights.

What the Study Examined

The researchers analyzed 463 non-financial companies listed in the STOXX Europe 600 index between 2016 and 2022. Their objective was to assess whether ESG controversies, such as environmental violations, social misconduct, or governance failures, impact a company’s systematic risk, measured by its beta coefficient (a key risk indicator in finance).

Importantly, the study leveraged a Random Forest machine learning model combined with Explainable AI (XAI) methods to predict and interpret firm-level risk.

Key Finding: ESG Controversies Increase Systematic Risk

The study's core conclusion is clear: ESG controversies significantly raise a firm’s systematic risk. In other words, when a company is embroiled in environmental scandals, social misconduct, or governance failures, investors perceive it as riskier, leading to greater stock volatility and sensitivity to market shocks.

Not All Controversies Are Equal

As Responsible Investor has reported, some investors have already voiced their concerns.

Sondre Myge, head of ESG at Skagen Funds, said that while it’s still early, his “first impression is that it complicates comparability. Investors are now drowning in a mix of voluntary and legal disclosures requiring them to make assessments through a kaleidoscope of standards and methodologies. Sifting critically through hundreds of pages of text just for one company is a huge undertaking. While first movers will provide glossy reports that convey a convincing impression, it is important to remember that disclosures are not necessarily representative.”

Jan Kaeraa Rasmussen, head of ESG and sustainability at PensionDanmark, agreed, stating that initial disclosures tend to be “more narrative than quantitative. This limits our ability to draw robust, forward-looking insights from the information provided.”

What’s Next: Simplification or More Complexity?

Interestingly, the study found that the relationship between ESG controversies and risk is non-linear:

• Firms experiencing their first ESG controversy ("first-timers") see a pronounced jump in risk.
• For firms regularly facing controversies ("regulars"), the effect on risk remains high but stabilizes.
• Small controversies matter most for firms with otherwise clean records. For already controversial
• firms, additional issues have less incremental impact.
  

This pattern aligns with investor behavior: markets tend to overreact to initial controversies while becoming desensitized to repeated issues.

Machine Learning: A Powerful Risk Prediction Tool

The researchers used Random Forest regression, a machine learning technique that captures complex, non-linear relationships in data, to predict systematic risk.

Compared to traditional models, the Random Forest approach reduced the prediction error by nearly 30%. The model achieved a mean absolute error of 0.25 for 2022 risk predictions, outperforming a naïve benchmark model that assigned every company the same average risk.

This reinforces the value of machine learning in financial risk management — particularly when assessing non-traditional factors like ESG controversies.

Industry Matters: Some Sectors Are More Vulnerable

The study also highlights that the impact of ESG controversies on risk is highly sector-specific.

Industries with Highest Sensitivity to ESG Controversies:

• Machinery
• Oil, Gas & Consumable Fuels
• Chemicals
• Metals & Mining
• Professional Services
  

These industries tend to face greater investor scrutiny due to their environmental footprint or governance challenges.

Industries with Lowest Sensitivity:

• Real Estate
• Food Products
• Electric Utilities

The lower sensitivity in these sectors may reflect stronger sustainability practices, regulatory protections, or reduced operational exposure to ESG risks.

Geographic Differences in Risk

In addition to industry effects, the research found that firms in certain countries face higher systematic risk linked to ESG controversies.

Countries with the highest predicted systematic risk included:

• Finland
• Portugal
• Poland
• Netherlands
• Sweden

Meanwhile, firms in Italy and Germany showed lower ESG-related risk exposure.

Implications for Investors, Risk Managers, and Companies

This study provides clear takeaways for finance professionals and ESG practitioners:

1. ESG Controversy Monitoring Is Critical
   Investors need advanced ESG monitoring tools to detect early signs of controversy, particularly for first-time incidents, which have the highest risk impact.
2. Tailor Risk Management to Industry
   Risk managers should take into account industry-specific vulnerabilities.
3. Machine Learning Enhances Risk Prediction
   Traditional risk models may overlook non-linear ESG effects. Machine learning offers a powerful, data-driven approach to anticipate market reactions to ESG incidents.
4. Proactive ESG Management Reduces Risk
   Companies should address ESG risks early before they escalate into high-profile controversies that damage reputation and investor trust.
   

Conclusion

This groundbreaking research bridges ESG and AI, demonstrating that ESG controversies are not just a reputational issue; they are a quantifiable financial risk. Machine learning models provide finance professionals with more accurate tools to assess and mitigate this risk.

About SESAMm

Discover SESAMm provides AI-powered solutions to help investors and companies identify ESG controversies and risks before they escalate. Using state-of-the-art AI, SESAMm analyzes millions of sources in near real-time, detecting ESG controversies across public and private companies worldwide. Whether for due diligence, portfolio monitoring, or supplier risk management, SESAMm enables financial professionals to stay ahead of emerging risks and make more informed decisions.
Learn more at www.sesamm.com.

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.

We are proud to share that SESAMm has been highlighted in Elaia's Annual Sustainability Report for 2022. Elaia has been historically committed to fostering sustainability and ESG excellence, and SESAMm is honored to be identified as a top ESG-focused company within its portfolio.

The report underscores SESAMm’s pioneering AI technologies, capable of analyzing billions of textual data, from articles to blogs. This technology equips private equity firms, asset managers, and leading financial institutions with accurate and timely insights into ESG controversies, trends, and positive impact indicators.

This acknowledgment supports SESAMm’s dedication to reshaping the ESG landscape. Our keen focus on aligning market operations with the UN Sustainable Development Goals (SDGs) positions us as a leader in the sustainable finance ecosystem.

To learn more about SESAMm’s role in advancing sustainable finance, we invite you to download the full report.

Reach out to SESAMm

TextReveal's web data analysis of over five million public and private companies is essential for keeping tabs on ESG investment risks. To learn more about how you can analyze web data or request a demo, contact one of our representatives.