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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.

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.

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.