Barcelona, QuantMinds International, November 2022

CEO Sylvain Forté joins QuantMinds correspondent Joanna Simpson in an interview highlighting the use of AI in ESG Investing and how we use it to detect greenwashing practices.

Below is an approximation of this video’s audio content. Watch the video for a clearer understanding of the topics discussed during the interview.

Joanna:  I'm Joanna Simpson here at QuantMinds International in Barcelona. Joining me now is Sylvain Forté, CEO of SESAMm. Thank you very much for being here.

Sylvain: Thank you.

Joanna: Tell me, how does it feel to be here at QuantMinds International?

Sylvain:  It feels very good, actually. We've been to the conference a couple of times already, so it's not our first year, and this time we brought several people from our team. We're all here together, presenting our technology and discussing some of the novelties in the space. It's very exciting and personalized.

Joanna: Great. And what role does artificial intelligence have to play in the future of ESG and ESG investing, in particular?

Sylvain:  ESG is a massive trend in the industry right now, not just in asset management and the quant space but also in private equity, in corporate space like tracking suppliers, clients, etc. And one of the key problematic themes that we see is data gaps. There's a lack of data in terms of coverage; small caps, mid caps, or even private firms are not well covered. The frequency of information tends to be lagging. There's a very low frequency, like quarterly updates or so. There's also a lack of transparency and the like.

So, I believe that AI is primarily a tool that can help build that information gap and, for example, cover millions of companies instead of just a few tens of thousands of companies manually. What we do at SESAMm is leverage a technology called natural language processing (NLP), where we screen text automatically to understand potential ESG controversies or positive impact events. This leads us to have a coverage of around 5 million companies, meaning every publicly listed company out there and private firms that no one else would cover otherwise. This enables many use cases.

There's also frequency; you can generate indicators every single day, more like a quantitative time series that people are used to, and this enables clients to get access to information even locally, like Raiffeisen, one of our clients, is tracking clients in Poland, in Austria, in Germany, or in Ukraine using NLP which would not be possible with traditional ESG metrics. I think that the key topic of AI is expanding the use, expanding the coverage in terms of ESG data, and making sure that data is systematic, follows a good process, and is transparent.

Joanna: What examples are there of ESG investing being enhanced by AI?

Sylvain:  We see two primary use cases.

The first one is more quantitative, where people are looking to leverage ESG NLP data in their systematic trading process. It's either for alpha generation; for example, we work with LFIS, an asset manager in France that created a fund based on ESG NLP data. Their primary goal is to enhance their strategy to generate outperformance, which is really a good use case in that space. This is the quantitative use case where you can use higher frequency data like daily data to leverage ESG like any other kind of alternative dataset and derive superior returns.

Then we have more discretionary use cases where we see large asset managers or private equity shops which are looking to perform risk management tasks or help their team prioritize the scoring of assets. Say they have a team that does their own proprietary scoring on assets with regards to ESG, but how do I prioritize? I have 3000 assets to follow, I need some kind of alert on that whole universe to make sure that I focus on the assets that could be most controversial today. That's one of the things that we provide; daily alerts using natural language processing where people can say okay, there is a massive shift right now; as an ESG analyst, I'm going to make a decision to look at this asset specifically to help cover it and update the score.

Joanna:  Can AI help with greenwashing in ESG investing, and if so, how?

Sylvain:  Yes, it's one of the other kinds of problems that you have in ESG is the lack of transparency on the methodology creates some anomalies in some cases. And one of the big anomalies is that there's this averaging effect where a firm that has both positive actions and negative topics is going to be, on average, neutral, which is really problematic.

We had a big example like this in France recently with Orpea, a listed company of nursing homes exposed to a massive scandal with regards to mistreating patients—so more like social washing than greenwashing. And the problem is their scores were pretty high because, at the same time, they had some positive impact. They were implementing new diversity policies and the like, so it was averaging up.

At SESAMm, we leverage NLP to completely differentiate positive and negative topics. So if a firm is doing good stuff that is aligned with SFDR, and they have positive actions, etc., great! That's going to be one score. But if, at the same time, they have very negative topics, there are a lot of risks we're going to still detect that's not going to be averaged. It's going to be very specifically focused on.

Joanna: Sylvain Forté, thank you for your time.

Sylvain: Thank you very much.

To learn more about how SESAMm uses Text Reveal to find ESG data, contact a representative today.

SESAMm is delighted to announce the addition of Emmanuel de La Ville, a renowned ESG expert, to our advisory board. Emmanuel’s extensive experience in ESG research and sustainable finance will undoubtedly bring invaluable insights and strategic direction to SESAMm as we continue to innovate and expand our AI-powered text analysis tools.

Background

Emmanuel de La Ville has dedicated the past two decades to advancing ESG (Environmental, Social, and Governance) standards in the finance industry. As the founder, ex-CEO, and senior advisor of EthiFinance, Emmanuel spearheaded the creation and development of an independent ESG rating agency in Paris, known for its specialization in the small and midcap segment. Under his leadership, EthiFinance became a well-respected European organization, conducting over 2,000 assessments annually.

Founding EthiFinance

In 2004, driven by a vision to transform the finance industry, Emmanuel founded EthiFinance. The agency focuses on evaluating companies based on their Corporate Sustainability performance. Beyond his track record in promoting ESG accountability towards listed companies Emmanuel’s innovative approach included developing a robust ESG research methodology and conducting due diligence for companies owned by private equity firms.

Views on SESAMm and the Applications of AI to ESG

In a recent interview, Emmanuel expressed his enthusiasm for joining SESAMm and highlighted the company’s commitment to leveraging AI technology to enhance ESG insights and drive responsible investment. Emmanuel believes that SESAMm’s innovative text analysis tools can play a crucial role in identifying and mitigating ESG risks, thereby promoting more sustainable business practices.

"SESAMm’s AI-powered tools are essential for analyzing vast amounts of data and uncovering hidden ESG risks and opportunities," Emmanuel stated. "This technology allows a more comprehensive understanding of a company’s performance beyond traditional financial metrics. It’s about creating value not just for shareholders but for all stakeholders."

Purpose-Driven Career in ESG and Ethical Practices

A deep sense of purpose has driven Emmanuel’s dedication to ethical practices and sustainability throughout his career. His journey from traditional finance to founding EthiFinance was motivated by a desire to align business practices with ethical values. Emmanuel’s belief in transparency, fairness, and the long-term well-being of all stakeholders has been a guiding principle in his work.

"Ethical behavior and sustainable practices have always been at the heart of my career," Emmanuel remarked. "I left the corporate world because I couldn’t see the sense in contributing to short-term profit maximization at the expense of broader societal and environmental well-being. Founding EthiFinance was about creating an approach to measure and promote responsible business practices."

The Future of ESG

Looking ahead, Emmanuel is optimistic about the future of ESG and the role of AI in shaping it. He foresees a growing demand for transparency and accountability in corporate behavior, driven by regulatory pressures and investor expectations. Emmanuel is particularly excited about the potential of AI to enhance ESG data quality and accessibility, enabling more informed decision-making.

Emmanuel de La Ville’s addition to SESAMm’s advisory board marks a significant milestone in our journey towards integrating AI with ESG analysis. His expertise and passion for ethical business practices will undoubtedly strengthen our mission to provide cutting-edge solutions for responsible investing. We are thrilled to welcome Emmanuel and look forward to the invaluable contributions he will bring to SESAMm.

"We are thrilled to welcome Emmanuel de La Ville to our advisory board. His profound expertise in ESG and unwavering commitment to ethical practices align perfectly with SESAMm's mission to provide advanced, responsible AI-driven solutions. Emmanuel's insights will be instrumental in guiding our efforts to enhance ESG analysis and promote sustainable investment practices." said Sylvain Forté, SESAMm's CEO & Co-founder

For more information about SESAMm’s AI-powered ESG insights and our latest developments, please visit our website.

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 to request a demo, reach out to one of our representatives.

By Magnus Billing, SESAMm advisor, with insights from Sylvain Forté, CEO of SESAMm

Investors have faced so-called “black swan” events throughout history: unexpected crises with severe consequences, often rationalized only in hindsight. Yet in an era defined by generative AI and vast, real-time data lakes, the question arises: could such events be understood and acted upon before they unfold?

The 2023 U.S. regional banking crisis offers a striking case study. The rapid collapses of Silicon Valley Bank and Signature Bank revealed how quickly stress can spread and how difficult it remains to connect early warning signs across sources.

While traditional financial analysis focuses on fundamentals such as capital ratios, liquidity positions, governance, and earnings, a new class of tools is expanding the lens. AI-driven controversy data aggregates and analyzes millions of public sources, from regulatory statements to media and industry discussions, to detect emerging issues as they surface. It does not replace quantitative and fundamental analysis; it complements it by tracking the visibility of risk as it enters public conversation.

This combination of approaches may offer investors a fuller picture: the structural risks visible in balance sheets, and the narrative risks revealed through public dialogue. To test this idea, we revisited the 2023 crisis through both perspectives, starting with what traditional analysis could have shown and what it missed.

Traditional Analysis and Its Blind Spots

In hindsight, the vulnerabilities of regional banks such as Silicon Valley Bank and Signature Bank were visible before the start of 2023. Unrealized losses on long-term securities, heavy reliance on uninsured deposits, and exposure to interest-rate risk pointed to potential liquidity stress. Yet these indicators were neither fully recognized nor connected in the market.

Traditional analysis has a tendency to evaluate banks based on their specific niches: Silicon Valley Bank focused on technology and venture financing, while Signature Bank served commercial real estate and digital asset clients. However, this approach risks overlooking the common and shared structural factors: concentrated depositor bases, high sensitivity to interest rate changes, rapid growth, and weaknesses in governance. Few, if any, observers recognized how rapidly these vulnerabilities could interact and escalate in a modern, digitalized banking environment.

While financial reports contained the data, there was little discussion connecting these risks in the public domain. But what about controversy data? Would it have caught the impending crisis? To find out, I asked Sylvain Forté, CEO of SESAMm, to provide an AI perspective.

What the Data Showed: Signature Bank

Signature Bank displayed a gradual pattern of emerging risk visible through public discussion. From mid-2022 onward, controversy data showed a rise in coverage related to governance practices, management oversight, and deposit concentration risks, often in the context of its ties to the digital-asset industry.

Importantly, it was not the crypto exposure itself that led to the bank’s collapse. The bank even announced in December 2022 that it would reduce its crypto-related business. Instead, the FDIC’s Supervision of Signature Bank report concluded that, “the root cause of SBNY’s failure was poor management. SBNY’s board of directors and management pursued rapid, unrestrained growth without developing and maintaining adequate risk management practices and controls.”

From a controversy perspective, those signals were publicly visible but fragmented. As shown in the chart above, AI-powered monitoring could have aggregated them into a clear view of a sustained drift in governance-related discussions, offering an early indication that oversight and internal controls were under pressure and risk was increasing.

What the Data Missed: Silicon Valley Bank

In contrast, Silicon Valley Bank presented a markedly different pattern. While controversy data registered some activity in late 2022,  including investor reactions to financial forecasts and coverage of routine business operations, these signals were fundamentally different in character from Signature Bank's governance-related warnings.

The September 2022 increase reflected market disappointment with financial guidance rather than operational or governance concerns. The subsequent activity captured normal business news, such as arranging syndicated loans. Critically, there was minimal public discussion of the bank's balance-sheet structure, unrealized losses, or depositor concentration risk until the crisis was already unfolding in March 2023.

This example underscores a key distinction: AI controversy monitoring excels at capturing reputational, governance, and operational risks as they enter public dialogue, but may not surface structural financial risks that remain confined to regulatory filings and analyst reports.

Lessons from Both Cases

The contrast between these two banks illustrates the complementary roles of quantitative and fundamental financial analysis vs AI-driven controversy monitoring.

In Signature Bank’s case, controversy data captured a steady accumulation of governance-related warnings, a slow build-up of risk visible through public discussion.

In Silicon Valley Bank’s case, the risks were structural but not yet discussed, leaving little for AI-powered controversy data to detect.

As Sylvain explains, “AI controversy monitoring helps investors understand how and when risks start to emerge in public dialogue. It does not replace fundamental analysis. It complements it by showing when the conversation begins to shift.”

Conclusion

Black swan events are often rationalized only in hindsight, but the 2023 regional banking crisis suggests a more nuanced reality. Some signals existed. What remained difficult was connecting them across sources before stress became contagion.

AI-driven controversy monitoring proved effective at surfacing governance and operational risks as they entered public dialogue, as Signature Bank demonstrated. Yet structural financial vulnerabilities like those at Silicon Valley Bank may not generate discussion until crisis forces the conversation, underscoring that no single lens captures all risk.

The advantage lies not in prediction, but in preparation: combining the structural risks visible in balance sheets with the narrative risks revealed through public discourse. In an era of real-time data and generative AI, the question is no longer whether information exists, but whether investors can connect it before it becomes consensus.

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 to request a demo, reach out to one of our representatives.