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.

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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.

Controversial business involvement screening is moving beyond its origins as a compliance exercise.

Under frameworks like SFDR and the EU Taxonomy, investors must prove that their portfolios not only promote sustainability but also exclude activities fundamentally at odds with environmental, social, or ethical principles. This marks a shift from static disclosure toward dynamic accountability, and it has broadened both the scope and ambition of ESG screening.

Historically, exclusions focused on a narrow range of activities - weapons, tobacco, or fossil fuels - and primarily applied to public equities. Today, that universe has expanded dramatically. Private markets, secondaries portfolios, and private credit exposures are now expected to undergo the same scrutiny as listed assets. This reflects not only regulatory alignment but also diversifying investor expectations, as institutions incorporate reputational, cultural, and mission-based constraints into their investment frameworks.

Modern exclusion policies increasingly include areas not yet covered by regulation but relevant to ethics, faith, or social impact. Examples range from pork-related activities in Sharia-compliant portfolios to emerging debates over cryptocurrency mining and trading, and even biotechnology topics such as human cloning or genetic manipulation that raise profound ethical questions. These additions illustrate how business involvement screening is evolving from a rule-based checklist into a reflection of each investor’s worldview and stakeholder commitments.

This evolution, however, brings complexity. Private assets and novel sectors often lack standardized data or public disclosures. ESG, compliance, and deal teams must process incomplete information, document decisions, and adapt quickly to new mandates - all without expanding headcount. The result is a growing need for automation that can adapt to human nuance.

SESAMm’s AI-powered business involvement screening meets that need. By allowing investors to screen based on their own exclusion categories and thresholds, it translates varied mandates - from regulatory to reputational - into a single, automated process.

Automating Controversial Business Involvement Screening in Public and Private Assets

SESAMm’s platform uses a new AI agent approach that scans and analyzes vast amounts of information. Below, we provide an overview of SESAMm’s business involvement screening capabilities and how they address investors’ needs for automation, thresholding, and flexible outputs.

Comprehensive Coverage through Big Data

SESAMm utilizes its AI engine to monitor over 30 billion articles and 10 million new documents daily from various sources, including news sites and NGOs. This extensive data collection spans multiple languages and local outlets, enabling it to detect obscure references to companies and raise alerts for issues such as misconduct. SESAMm's coverage encompasses millions of public and private companies, enabling users to conduct thorough screenings of any entity, including private companies and subsidiaries.

Customizable Exclusion Frameworks

SESAMm’s business involvement screening gives investors control over what to screen and how to classify it. Users can request customization of exclusion categories to mirror their own policy, whether based on regulation (e.g., SFDR, EU Taxonomy) or internal mandates (e.g., faith-based or reputational constraints). In addition to standard ESG categories like fossil fuels or weapons, investors can add custom topics. This flexibility allows ESG, compliance, and secondaries teams to tailor the tool to their precise needs,.

Threshold-Based Classification

SESAMm’s business involvement screening module is built around the concept of threshold-based flags. The AI utilizes structured data and unstructured signals to determine involvement levels. The output for each company is a clear classification: No Involvement, Limited Involvement, or Significant Involvement for each category. These classifications correspond to thresholds – limited might mean some involvement but below the exclusion threshold, significant means above the threshold or its a core business. By encoding the thresholds in the system, SESAMm ensures consistency with the investor’s policy. This is crucial for automation: rather than an analyst manually checking revenue percentages and news, the system does it automatically and provides clear justification.

Rapid Portfolio Screening Process

The system is designed for fast, self-contained screening. A user simply uploads a list or portfolio, and within hours receives a complete file summarizing involvement across all exclusion categories. The output includes company-level classifications, summaries of supporting evidence, and references to sources. This enables investors to integrate the results directly into due diligence workflows, risk committees, or regulatory reporting, with no ongoing manual data maintenance required.

Cost and Resource Efficiency

Automating this process saves substantial analyst time, particularly for rating agencies and secondaries investors managing high volumes of entities. Rating agencies can use the pre-classified results as a baseline input for their own ESG or credit assessments, reducing the manual data-gathering burden. LPs and GPs can run large private company universes in-house without additional research teams. In secondaries, where a full portfolio review can take days of analyst effort, SESAMm’s workflow compresses that timeline to just a few hours, enabling ESG validation to fit seamlessly into transaction schedules.

Auditability and Verification

Each classification is fully transparent. Analysts can drill down into the evidence behind a flag, including links to original articles, filings, or corporate statements, and verify the AI’s reasoning. Automatic translation ensures accessibility across languages. This transparency builds trust in the results and provides auditable documentation for LP reporting or regulator reviews.

As ESG investing matures, the leaders will be those who can implement exclusions transparently, efficiently, and in alignment with evolving norms. The next frontier is no longer just regulatory compliance - it is the ability to anticipate what clients and society will expect tomorrow, and to operationalize those expectations across all asset classes. SESAMm’s technology makes that possible: a platform that keeps pace with both policy evolution and moral expectations, bringing consistency and clarity to an increasingly complex ESG landscape.

Nous sommes fiers d’annoncer que SESAMm figure une nouvelle fois dans le Palmarès FinTech 100 France, qui distingue chaque année les 100 meilleures entreprises fintech du pays selon des critères tels que l’innovation, la croissance, la pertinence des solutions proposées et l’impact environnemental et social. Cette reconnaissance met en lumière les fintechs qui façonnent l’avenir du secteur financier.

Depuis 2022, notre progression dans ce classement prestigieux témoigne de notre engagement constant pour l’excellence et l’innovation : nous avons débuté en 63ᵉ position en 2022, grimpé à la 36ᵉ en 2023, puis à la 34ᵉ place en 2024 où nous étions également reconnus parmi les entreprises les plus actives sur les enjeux environnementaux, sociaux et de gouvernance (ESG). En 2025, nous avons franchi un nouveau cap en atteignant la 19ᵉ place, en hausse de 15 rangs par rapport à l’an dernier, et en nous hissant à la 1ʳᵉ place dans la catégorie Environnement, Impact et ESG.

Cette reconnaissance reflète le dynamisme de nos équipes et la pertinence de nos innovations pour accompagner les acteurs financiers dans l’analyse et la gestion des risques ESG. Nos dernières avancées, telles que nos AI ESG Assessment Reports et notre fonctionnalité de détection en temps réel des violations du United Nations Global Compact (UNGC), illustrent notre volonté de fournir des solutions toujours plus performantes et fiables à nos clients.

Merci à nos équipes, nos clients et nos partenaires pour leur confiance et leur soutien indéfectible !

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.