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.

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French

Paris, France – le 1er décembre 2025 – SESAMm, leader mondial des données de controverses ESG et réputationnelles, est heureux d’annoncer que la Caisse d’Epargne Rhône Alpes, grande institution bancaire régionale française, a sélectionné la plateforme SESAMm pour renforcer son évaluation des risques ESG et son suivi des controverses dans le cadre de ses activités de financement et d’investissement.

Présente dans l’une des régions les plus dynamiques de France, la Caisse d’Epargne Rhône Alpes accompagne les particuliers, les entreprises et les collectivités locales avec des solutions de financement, des produits d’investissement et des initiatives en faveur d’un développement régional à long terme. Dans le cadre de son engagement en faveur d’une finance responsable et durable, la banque évalue systématiquement les entreprises et organisations qu’elle finance sur les risques environnementaux, sociaux et de gouvernance, ainsi que les projets qu’elle mène. Elle s’appuiera désormais sur les données de controverses ESG de SESAMm pour identifier les manquements réglementaires, les incidents environnementaux, les cas de corruption, les violations des droits humains et d’autres indicateurs de risque.

« Cette collaboration reflète notre travail croissant avec les groupes bancaires français et les institutions régionales », déclare Sylvain Forté, CEO et cofondateur de SESAMm. « Nous sommes particulièrement fiers d’accompagner la Caisse d’Epargne Rhône Alpes grâce à une couverture renforcée des entreprises locales et non cotées, un domaine dans lequel  les jeux de données traditionnels manquent souvent de profondeur. Nos outils d’intelligence artificielle permettent de révéler les risques ESG les plus pertinents pour le financement et le développement régional. »

Grâce à SESAMm, les équipes de la Caisse d’Epargne Rhône Alpes bénéficient d’une visibilité en temps réel sur les risques ESG et réputationnels concernant les entreprises de la région Rhône-Alpes et du marché français au sens large. La plateforme offre un accès transparent aux sources à l’origine de chaque controverse détectée et propose un tableau de bord intuitif qui facilite les processus de due diligence. Des alertes automatisées permettent également d’identifier plus rapidement les risques émergents, renforçant ainsi la cohérence et l’efficacité des évaluations de projets.

« SESAMm a démontré une forte capacité à identifier les risques ESG concernant des entreprises locales et non cotées, un domaine dans lequel nous avons besoin d’une visibilité complète », déclare Sylvain Brissot, Directeur de la Coordination de la Transition Climatique, Caisse d’Epargne Rhône Alpes. « Nous prévoyons que les analyses de SESAMm amélioreront l’efficacité de nos évaluations ESG sur un large éventail de projets régionaux. »

À propos de la Caisse d’Épargne Rhône Alpes

La Caisse d’Epargne Rhône Alpes est une banque commerciale, régionale et coopérative de plein exercice présente sur cinq départements (Ain, Isère, Rhône, Savoie et Haute-Savoie) et sur tous les métiers de la banque. Elle agit au quotidien pour le développement de son territoire. Elle compte 1,4 million de clients, 445 000 sociétaires, 3 000 collaborateurs, 280 agences, 8 centres d'affaires.

À propos de SESAMm

SESAMm est un leader mondial des données sur les controverses, utilisant l’IA générative pour détecter rapidement les risques ESG, réputationnels et liés aux fournisseurs. Sa plateforme, propulsée par l’IA, fournit des informations en temps réel, même dans les marchés à faible niveau de transparence, sur des millions d’entreprises et de projets d’infrastructure. Elle permet ainsi de prendre des décisions plus éclairées, de renforcer la due diligence et de répondre aux exigences réglementaires à grande échelle. SESAMm collabore avec des acteurs majeurs tels que Carlyle, Warburg, Natixis, RBI, Sustainable Fitch, Oddo et bien d’autres. La société a levé 50 millions de dollars auprès d’investisseurs de premier plan et est présente sur quatre continents. Plus d’informations sur www.sesamm.com.

English

Caisse d’Epargne Rhône Alpes Chooses SESAMm to Strengthen ESG Risk Evaluation Across Regional Projects

Paris, France – December 1st, 2025 – SESAMm, a leading provider of AI-powered ESG controversy data, is pleased to announce that Caisse d’Epargne Rhône Alpes, a major French regional banking institution, has selected SESAMm’s platform to strengthen ESG risk assessment and controversy monitoring across its financing and investment activities.

Operating in one of France’s most dynamic regions, Caisse d’Epargne Rhône Alpes supports individuals, businesses, and local authorities with financing solutions, investment products, and initiatives that promote long-term regional development. As part of its commitment to responsible banking and sustainable growth, the bank systematically evaluates the companies and organizations it finances for environmental, social, and governance risks, including the projects they carry out. Caisse d’Epargne Rhône Alpes will now leverage SESAMm’s ESG controversy data to identify regulatory breaches, environmental incidents, corruption cases, human-rights violations, and other relevant risk indicators.

“This collaboration reflects our growing work with French banking groups and regional institutions,” said Sylvain Forté, CEO and co-founder of SESAMm. “We are especially proud to partner with Caisse d’Epargne Rhône Alpes by providing stronger coverage of local and non-listed French companies, an area where traditional datasets often lack depth. Our AI helps uncover ESG risks that matter most for regional financing and development.”

Using SESAMm, Caisse d’Epargne Rhône Alpes teams gain real-time visibility into ESG and reputational risks affecting companies across the Rhône-Alpes region and the wider French market. The platform provides transparent, source-level access to all detected controversies and an intuitive dashboard that simplifies due diligence workflows. Automated alerts also help teams identify emerging issues early, improving the consistency and efficiency of project evaluations.

“SESAMm demonstrated strong capabilities in identifying ESG risks among local and non-listed firms, an area where we require comprehensive visibility,” said Sylvain Brissot, Director of Climate Transition Coordination, Caisse d’Epargne Rhône Alpes. “We expect SESAMm’s insights to enhance the efficiency of our ESG risk assessments across a wide range of regional projects.”

About Caisse d’Epargne Rhône Alpes

Caisse d’Epargne Rhône Alpes is a full-service, regional cooperative bank operating across five departments (Ain, Isère, Rhône, Savoie, and Haute-Savoie) and covering all areas of banking. The bank works daily to support the development of its territory. It serves 1.4 million clients, has 445,000 cooperative shareholders, employs 3,000 people, and operates 280 branches and 8 business centers.

About SESAMm

SESAMm is a global leader in controversy data, leveraging advanced large language models and generative AI to uncover ESG, reputational, and supplier risks in seconds. Our AI-powered platform surfaces real-time insights, even in low-disclosure markets, on millions of companies and infrastructure projects, supporting more informed decisions, enhanced due diligence and regulatory alignment at scale. We work with leading firms including Carlyle, Warburg, Natixis, RBI, Sustainable Fitch, Oddo, and others. SESAMm has raised $50M from renowned investors and operates across four continents. Learn more at www.sesamm.com