SESAMm recently announced its successful Series B2 fundraising round. The news was covered by TechCrunch, a popular technology news website known for its comprehensive coverage of the latest developments in the tech industry.

Here are some excerpts, first published on TechCrunch:

SESAMm, a French startup that helps financial firms and corporates adhere to their ESG goals by using natural language processing (NLP) to generate insights from digital content, has raised €35 million ($37 million) in a round of funding to expand internationally.

Despite a growing backlash against ESG efforts from some politicians and vocal executives, companies are still cognizant of the reputational and commercial risks of ignoring their environmental, social, and corporate governance (ESG) responsibilities — this applies both to their internal practices and those of third-parties they do business with.

With that in mind, SESAMm enables businesses to track textual data from across the web — including news portals, NGO reports, and social networks — and convert this into actionable insights.

SESAMm has amassed a fairly impressive roster of clients from across the financial realm specifically, including U.S. investment giant Carlyle Group, French corporate and investment bank Natixis, Japanese multinational insurance holding company Tokio Marine, and U.K.-based asset management firm Unigestion.

Companies can access SESAMm’s flagship product, TextReveal, via several conduits, including an API that brings SESAMm’s NLP engine into their own systems. But on top of that, SESAMm also offers a web-based dashboard where companies can access data analysis, visualizations, and push notifications for various due diligence, compliance, and ESG scenarios.

For example, a company that wants to keep tabs on its supply chain partners can use SESAMm to track anything related to those partners that hits the public domain, such as emerging fraud litigation or other lawsuits. This allows them to proactively respond the instant they receive an alert via SESAMm — these ESG alerts, which SESAMm launched a few months back, can be delivered by email or system integrations, for example a customer relationship management (CRM) application.

Elsewhere, private equity firms can use SESAMm for due diligence on potential acquisition or investment targets. Indeed, SESAMm boasts a “20 billion article data lake” which it applies its NLP algorithms to to identify mentions on any type of company, with the data sliced, diced, and categorized into user-friendly dashboards.

“Private equity firms usually engage with consulting firms to perform due diligence on target companies,” SESAMm co-founder and CEO Sylvain Forté explained to TechCrunch. “The cost of doing this is very high, and the result is suboptimal as the amount of data on the web is enormous for individuals to go through it. Therefore frequently, the results are not comprehensive enough, leading to inaccuracies.”

However, the SESAMm platform can be configured for any number of use-cases, such as “share of voice” competitor analysis, or any other theme that might be relevant to a company.

“With the current attention on ESG in the industry, many of our use-cases are focused on that — however, we provide insights into several types of information,” Forté said. “This includes sentiment on brands, thematic stock baskets and indices, company leadership reputation, and web insights on macro-economic indicators such as inflation, among others.”

According to Forté, SESAMm pre-trains large language models, similar to that of ChatGPT, the generative AI poster child of the moment — on all the data it hoovers up, and fine-tunes the algorithms on its own datasets, which are annotated across the 100-plus languages it supports.

“SESAMm integrates a variety of data — over 20 billion articles in 100 languages with 14 years of history,” Forté said. “Data sources include highly-vetted news organizations, expert blogs, and social media. SESAMm also manages licenses for proprietary data sources from premium news channels.”

However, against the backdrop of the ChatGPT hype train and burgeoning corporate ESG commitments, the fact that SESAMm has raised a significant sum of money at a time when investors’ cash has seemingly dried up tells something of a story.

“Raising a significant amount during challenging market conditions highlights the relevancy of SESAMms focus on two key trends — AI and sustainability,” Forté said. “In turn, these tools enable organizations to make better decisions and fill the data gaps, particularly in ESG, on both public and private companies.”

Excerpts were first published on TechCrunch.

Paris, France, April 11, 2024 - Expanding its ESG controversy coverage and off-the-shelf solutions, Manaos, a subsidiary of BNP Paribas and a modular investment service platform, has entered into a strategic partnership with SESAMm, a leading authority in ESG controversy data powered by advanced Generative AI. This collaboration marks a milestone in providing comprehensive, AI-powered ESG controversies insights directly to financial institutions and corporate clients.

Through this partnership, Manaos will integrate SESAMm's cutting-edge ESG controversies data into its platform, offering a dedicated dashboard and controversies reports for institutional investors and asset management companies and adding private equity firms to its offering. This integration expands Manaos' coverage of controversies, not just for listed assets but also for private assets, tapping into the world’s largest set of events captured on controversies in over 5 million private and public companies, in addition to providing access to the most granular ESG controversy dataset in the world.

Sylvain Forté, CEO & Co-founder of SESAMm, highlighted the importance of the partnership, stating, "At SESAMm, we're committed to delivering transparent and comprehensive ESG controversy data, providing essential insights for our financial and corporate clients. By partnering with Manaos, we are not only expanding our reach but also empowering financial institutions to seamlessly integrate ESG considerations into their investment strategies."

This strategic partnership between Manaos and SESAMm signifies a forward leap in integrating ESG considerations into investment strategies, offering unprecedented access to sophisticated ESG controversy insights for the financial industry.

Benoit Guibourg, Head of Product of Manaos, added: "We're thrilled to partner with SESAMm, a renowned expert in ESG controversy analysis leveraging the power of AI. Thanks to this collaboration, Manaos aims to provide an extensive offer on ESG controversies and leverage the platform to incorporate comprehensive ESG reputational insights in custom or ready-to-use dashboards and reports. SESAMm's expertise extends our ability to guide clients through the complexities of ESG considerations, marking a significant milestone in our mission towards sustainable investing. Furthermore, we are eager to work with SESAMm to unlock the power of their extensive coverage of the private world and provide ready-to-use solutions for our clients."

For further information on Manaos and SESAMm, please visit their websites at www.manaos.com and www.sesamm.com.

About SESAMm:

SESAMm is a global leader in ESG controversy data, using advanced Generative AI. It automates monitoring and due diligence on public and private assets. SESAMm provides coverage for more than 5 million companies in multiple languages. SESAMm works with top international firms such as Carlyle, Warburg, Natixis, RBI, Fitch, and Oddo.

About Manaos:

Manaos, a technology subsidiary of BNP Paribas, powers an all-in-one platform that connects the traditional information systems of institutional investors and asset management companies with carefully selected rating agencies and fintechs to manage all their investment services seamlessly. In practice, the Manaos platform enables investors to collect their fund compositions from their asset managers, while standardising portfolio data and allowing for asset-level portfolio look-through. From there, Manaos empowers asset managers and asset owners to test and measure their ESG investments performance by connecting their portfolio data to a range of over 20 best-of-breed ESG data providers (including MSCI, S&P Sustainable 1, Morningstar Sustainalytics, Moody’s and ISS ESG). Once enriched with third-party data, Manaos offers flexible, multimodal portfolio data extraction along with seamless data visualisation and dashboards features. Finally, Manaos reporting solutions help investors with the production of EETs (European ESG Template), SFDR PAI Statements, TCFD, LEC Art.29, Taxonomy and SDR regulatory reports, client ESG reports and more. For more information, visit www.manaos.com

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.

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

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.