Tokio Marine & Nichido Fire Insurance Co., Ltd. (TMNF) tapped SESAMm for a joint research venture to predict future stock price movements. SESAMm provided various NLP indicators, such as digital sentiment calculated for single stocks or indices (seen as an entity), as well as its experience in machine learning to work on this task.

These studies concluded with two key findings:

1. Relationships exist between NLP data from news and social networking sites and investor behavior under specific circumstances. Researchers and investors can use the “digital sentiment” as an indicator of investor sentiment to anticipate price changes. They can then use this anticipation for a specific company or, more generally, any entity that can be isolated in a text (like an index).
   
   
2. By focusing on more stressed situations, like the 2015 market sell-off, the U.S.-China trade war, the coronavirus pandemic, and the start of the Ukrainian crisis, we could show that digital sentiment is beneficial in times of significant stress in the market. Digital sentiment more accurately reflects the stress level in these complicated situations. It, therefore, helps to predict stock price movements more accurately in these stressed cases, providing a tail hedge. It’s not biased by an excess of confidence linked to the “central banks put” for instance.

Providing safety and security since 1879

Tokio Marine Insurance Company was first established in 1879. Over the years, it has added products and services, acquired other businesses, and merged with other companies to eventually become Tokio Marine & Nichido Fire Insurance Co., Ltd. Commonly called Tokio Marine Nichido today, the company is a property and casualty insurance subsidiary of Tokio Marine Holdings, the largest non-mutual private insurance group in Japan. Its products and services provide safety and security to its clients and partners, contributing to more fulfilling lifestyles and business development.

One of the company’s philosophies is to be a good corporate citizen and fulfill its social responsibilities, including protecting the global environment, promoting human rights, creating a responsible working environment, and contributing to society and individual local communities. Recently, the Emperor of Japan awarded Tokio Marine Holdings, Inc. the Medal with Dark Blue Ribbon for donating to the Japan Student Services Organization to support students who face financial difficulty during the

COVID-19 pandemic. Individuals, corporations, or organizations are awarded the Medal with Dark Blue Ribbon for their outstanding contributions to the public.

Transforming and accepting the challenge to grow

According to TMNF, “The business environment surrounding the insurance industry is changing at a faster pace than ever due to changes in demographics, advances in technologies, such as autonomous driving and AI, and longer-term trends, such as the intensification and frequent occurrence of natural disasters, as well as further progress in digitalization due to the COVID-19 pandemic.”

“The business environment surrounding the insurance industry is changing at a faster pace than ever…”

“While these changes in the business environment pose a threat, we consider them to be excellent opportunities for transformation and the creation of new value.” So they’ve adopted the concept, “Transformation (“X”) and Challenge to Growth 2023: Aiming to be the company most chosen for quality and its passion.” Ultimately, it strives to support customers and local communities in times of need while contributing to social responsibility. Five social issues that it will prioritize are:

• Global climate change and the increase in natural disasters
• The increased burden of long-term care and healthcare due to the aging of society and advances in medical technology
• Technological innovation and its effects on the environment
• Symbiotic society and responding to the novel coronavirus
• Industrial infrastructure and how it supports economic growth and innovation

Leveraging a partner with the right technology

To secure and protect its clients’ assets while elevating social issues, Tokio Marine Nichido sought out an edge in the stock market. Under these circumstances, it was fortunate that TMNF discovered SESAMm in 2020 through the Plug and Play Japan program, a platform with an event that connects Japan to markets abroad. SESAMm had presented its NLP alternative data solution, TextReveal®, to which TMNF considered the platform for access to alternative data and sought collaboration with the SESAMm team for a research project.

“SESAMm has the technology to extract sentiment from news data with a neural network.”
– Tokio Marine & Nichido Fire Insurance Co. Ltd representative

Extracting relations between NLP data and the financial market

In 2021, Tokio Marine Nichido Insurance began collaborating with SESAMm to develop an AI analytics model for alternative data. It models the effect of news and social networking data on investor behavior for stock and bond markets. In other words, it structures text information into knowledge usable by TMNF.

Monitor risks and topics

NLP data can improve the understanding of the market’s behavior by exhibiting the most important topics over time, with a direct indication of the importance of the topics through the text volume (Figure 1).

Researchers can also use it to focus on a specific topic or a certain period. For instance, a short analysis of the most frequent keywords in the press, which preceded the market fall during the COVID-19 pandemic, showed the significant predominance of pandemic-related terms (Figure 2).

Focusing on the equity market

NLP tools provide specific data, like sentiment, to get more detailed information at the company level and for many underlyings. Indicators for equity indices, for instance, can be calculated and provide a clean sentiment to monitor markets.

In many situations of stress over recent years, such sentiment proved to be an early indicator of the market’s future degradation. For example, there was a time lag of as long as a month between the time COVID-19 became the main news focus and the time it affected the U.S. stock market. By using SESAMm’s technology to analyze news data during this period, the team found that the U.S. digital sentiment had already deteriorated sharply before stock prices reacted (Figure 3).

This sentiment deterioration occurred because of the fear of the coronavirus’s spread’s effect on the global economy (see Figure 2). Even with an all-time high S&P 500, U.S. investors didn’t initially consider this risk. In comparison, HSI companies were closer to the coronavirus spread risk. So as a result, HSI investors reacted ahead of their U.S. counterparts. In other words, by using natural language data, it was possible to capture a risk overlooked by U.S. investors but related in the publicly available texts and take action ahead of the market deleveraging.

Generalizing the results to the credit market

Tokio Marine Nichido also expanded the scope of the research to U.S. high-yield bonds index trade. In the credit market, a high yield has a high beta, which makes its risk comparable to the equity market.

Research shows that, on a risk-adjusted basis, the NLP-data-built signal has a positive and consistent performance through the timeline compared to the U.S. HY T.R. index benchmark (Figure 4). Its performance has a low correlation with the index (Figure 5), so the sentiment is diversifying. It not only acts as a diversifier but delivers higher returns than the benchmark when the U.S. High Yield market sold off (Figure 6). As such, the NLP signal diversifies, hedges, and protects against adverse periods. It provides a mechanical pick-up in risk-adjusted return when running alongside traditional strategy.

The NLP signal outperforms the index in realistic backtest conditions, including long allocation only, turnover constraints, and trading fees (Figure 7). The quantitative model integrates some macro indicators, but the previous NLP signal induces the main source of outperformance and risk mitigation.

TMNF is also applying the research to estimate the Fed’s stance—hawkish or dovish—using natural language data, too. It hypothesizes that the market will be focused on the Fed’s stance on interest rate hikes in the next few years.

“The model developed in collaboration with SESAMm is simple in structure, yet, it’s an orthodox and robust model that uses valid data as input.”

Summarizing the collaboration

In developing models, Tokio Marine Nichido believes it’s essential to consider “what data to consider” and to keep it simple. And TMNF achieved these tenets. The model developed in collaboration with SESAMm is simple in structure, yet, it’s an orthodox and robust model that uses valid data as input which is preferable to a risky over-fitting by increasing complexity.

Get in touch with SESAMm

To learn more about Tokio Marine Nichido’s case study or to request a TextReveal demo, reach out to us.

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.

Introductions

Sylvain: Thank you very much for joining this webinar. We are talking today about three practical techniques to monitor ESG on private companies. So we're going to talk a lot about private equity, private debt, but also high yield even supplies, and client monitoring on the corporate side. And, of course, we're gonna talk about ESG and sustainability, including a bit about positive impact with qualitative and quantitative.

So this webinar is 45 minutes, and we'll have 15 minutes allotted for Q&A. And without further ado, I'll let you introduce yourself, Navin, and I'll do the same afterward.

Navin: Thank you so much, and thank you to SESAMm, and SuperReturn for putting this together as well. And good morning, good afternoon, good evening, wherever you are in the world.

And I hope you look forward to the next one hour of, may I say, excitement with ESG. So call me Navin, call me Nav, whatever you are comfortable with. I basically sit as a partner running our sustainability practice in Tata, very specifically in Tata’s technology practice. And what we see and what we cover is a number of clients across financial services across corporates, and we also advise our Tata group of companies as well. So we are a huge industrial conglomerate in high-carbon, low-carbon, carbon-renewable energy, and good ESG practices as well. Oil, gas, hotels, retail call, manufacturing, you name it. But a day will not go by when you do not touch a Tata product as well.

Just to add, you know, I've been at Tata Consultancy Services for the past three years now. Prior to that, I worked a number of leadership roles in places such as EY, Credit Suisse, Commerce Bank, across risk and trading. So I look forward to speaking with you, and thank you so much for having me today, Sylvain.

Sylvain: Thank you very much, Navin. And we really appreciate you joining the webinar. A few words on my side, too. I'm Sylvain, CEO of SESAMm. I'm based in Paris, France. And I'm managing a team of around a hundred people at SESAMm that basically extract billions of documents from the web—articles, messages, news, social media—and leverage AI to identify ESG controversies monitor the positive impact on companies.

And specifically, and that's really the goal of this with our assets that no one else can cover. So private equity, private debt, supplier clients, a lot of assets that traditional ESG rating agencies would have difficulties covering. So at SESAMm, we serve a lot of private equity firms: asset managers, corporates, such as the Carlyle Group or Warburg Pincus for example, in the investment management side. Or on the corporate side, helping them monitor ESG topics on clients across Eastern Europe. And we’re really interested in private equity, but also private debt in how intricate the complexity can be in terms of ESG monitoring in that sector. So that’s it about me, and I think we are ready to start.

So the first thing that we want to discuss is the high-level topic of industry challenges with regard to ESG monitoring, especially on private assets and sustainability in general. So Navin, very happy to have your opinion and your thoughts on this.

Navin: Thank you so much. So I think when we think about this high level, let's take a step back from what do we mean by sustainability, right?

Someone in the organization can tap the word sustainability, but what specifically are they asking about in sustainability? Is it ESG? Is it climate change? Climate risks? Are we talking about carbon markets? Are we talking around sustainable financing? Are we talking around net zero? Are we talking about concepts such as circular economy, and the EU Green Deal as well? So when we talk sustainability and we try to think about what a sustainability mean, one of the challenges that you see is that you need to join all of the, that wording up together. And that's why it becomes crucially important, especially in an industry [00:05:00] such as private equity, where data is not widely open or not widely shared. Because when we look at companies, rival companies on listed stock exchanges as well, just the sheer amount of data disclosures that need to be done for ESG, climate risks, amount of sustainable financing, the firm has done their usage of carbon markets. And not last but not least, how are they going to get to net zero is very, you know, is extremely widely available.

Now, take a step back again. How do you take those words and those keywords, and what are we trying to measure? What is relevant to the PE company in scope? What is going to be most important for their strategy as well?

Sylvain: That makes sense. And you're right, actually, Nevin, it’s one of the primary, like the biggest problem is simply that private companies don't disclose as much information and that there is a lack of data globally in the industry. And to find that this is a problem because people have put costly processes in place to connect the data or because it poses a problem from a standardization perspective.

Navin: Of course.

And I think, “how do you standardize, right? Do you, for example, if we go into the crux of it, do we go and proxy, let's say, we've got a PE, oil and gas company? Do we just go and proxy to BP because that oil and gas PE company is based in the UK and has a similar decarbonization pathway? Or is managing its climate risks in a similar way to BP, for example? Or do we have to proxy that company because their investment is not primarily in the UK but maybe in emerging markets? And we might have to find a proxy, such as maybe Reliance Oil and Gas, which is an Indian oil and gas company. Or do we basically proxy against a shell, for example? And proxying can be great, and it's been used by quants for a very, very long time. But if we don't really understand how the listed company is going, is it gonna make sense for the PE company that you're trying to proxy?

Sylvain: Yeah, you're right. And we are actually seeing that a lot in ESG rating agencies that has that kind of human costs, that limits the scale that they can have in terms of rating new companies, including small caps in private markets and where that kind of proxy rating is used more and more. But it still means that you're lacking data on the companies themselves. Everything is more industry based, which also creates some issues sometimes.

Navin: And another thing. Yeah, yeah. Sorry. Go on. Keep going, Sylvain.

Sylvain: No, and I dunno what your opinion is on that, but one of the two key things that we observe from an industry perspective and issues that PE firms are facing is one, regulatory pressure is coming. It's been in asset management for quite some time. The SEC right now is scrutinizing private equity from a tax perspective in particular, and by doing this, it also puts the focus on ESG regulations and how SFR EU, taxonomy, et cetera, could apply to private equity. And I think there's also pressure from the LPs.

So kind of the first question is, what is your ESG strategy? That's something that we see a lot with GP that we work with. Is that something that you see also in the industry?

Navin: Very much so. So, I think that the issue is the strategy keeps on changing. And with economic headwinds, we are facing globally with higher rates, higher inflation, and the terrible war initiated by Russia on Ukraine as well.

Very volatile commodity prices that we are seeing as well. Sometimes ESG is taking a back step. Sometimes ESG is taking a front step. Now how do we navigate those global headwinds and factor that into ESG strategy? And one of the points that sometimes tend to get missed is that, you know, some governments are putting the environment on the back foot. And the good examples are the new UK government that we currently have in the UK, which is talking a lot about deregulation. Some governments, such as the EU and their taxonomy, are upping the standards and trying to push those standards.

So what does that mean again for your ESG strategy? And does that mean that country by country, I need to have an individual sovereign ESG view so I can understand the impact on the…if I'm a PE company and I'm invested in certain sectors as well, what does it mean? And that is the issue that you cannot have a generic ESG strategy and apply that globally.

Sylvain: You're very right. So yeah, regulatory pressures specificities across the different countries.

Industry challenges and technology overview

Sylvain: I think we're touching on a lot of high-level topics on industry issues. One of the things that we see a lot more as a data provider and AI solution is there is really a data issue beyond the regulatory pressure beyond the need. Once that need is expressed, there is actually a huge issue in the whole industry is the lack of coverage.

So there are like more than 20 million firms worldwide that private equity could access as financial assets, and they are less than 50,000 that are actually rated by rating agencies. And so I think the entire industry is really seeing that data gap that is costing in terms of standardization in the whole industry.

Because if the data is not there, it's very difficult to get together and find a standard process. [00:12:00] And what that means is that there is a lot of reliance on manual processes, questionnaires, which sometimes also lack objectivity, so we are seeing right now that contrary to asset management, most of the information is disclosed on an active basis instead of firms being observed by external parties. And I think that could create some issues in the industry, especially with greenwashing involved, et cetera. This could actually create a few issues for investors in the next few years.

Navin: And I think one of the examples to take there is, how do you say to, let's say, an asset manager or a bunch of investors who are, say, investing in PE companies or creating PE funds or funds of funds, and they are being asked, “Can you tell me what the climate risks are for those companies?” Can you tell me the underlying sector that the company sits in? What is their strategy, for biodiversity, for example? What is their strategy for social and governance metrics as well? Really, really super challenging questions, especially because the PE company does not has to disclose currently, right? Now, that’s where, you know, it goes back to the word, how can you use proxy, but how can you get other types of data that are mandated by sovereign legislation as well? Because remember, let's go back to the example of oil and gas, for example. So if you talk about oil and gas, you know, there are certain regulations around oil and gas where disclosure has to happen, whether you are listed or whether you are unlisted as well. And that can go right [00:14:00] down to carbon emissions. So the hunt for the data needs to take place, but mapping of the sector, and the sovereign legislation in the country, which will determine how do I get those, determine those data points are very, very critical. So, as they say, data is gold.

Sylvain: Yeah, it is. And what we found also is that even when you have the data, the journey is not over. So a lot of the issues are in a lot of private equity firms, there's gonna be a lot of data collection.

But then that data is not gonna be regularly updated. So it's gonna be stale pretty quickly, and after a year, the process will start over. It's tough to have that point-in-time history. So public markets are very aware of this because every event has, could have a high-intensity impact very rapidly.

But this is actually the case also for private equity. So still lack of frequent data is a bit problematic. And similar to equity markets and public markets, I think black box scores are also a bit of an issue. A bit less so because a lot of the processes rely on manual data collection. But at the end of the day, you still end up in a lot of cases with the sort of score that may not be entirely transparent.

Neither for portfolio companies nor for LPs, and sometimes they don't really understand the ranks and the scores. I think transparency is a bit of an issue also that people are trying to solve in the industry, progressing.

Navin: And I think that's where we are seeing these issues of greenwashing and social washing going out there in the industry. And in some cases, it's deliberate. In some cases, it's not deliberate, right? You know, there are certain companies which have just had, unfortunately, very, very poor data points or not understood the data, and relied on the validity and office, if I can pronounce that word, let's say the validity of the data correctly. And they've taken that to their board, and they've used that in pricing or to raise financing as well, and then basically found out that the media or research companies have then gone and challenged the assumptions that have been used in the data as well. And would you say that it's the industry's problem or challenge? You could maybe say yes and maybe no, but at the same time, we remember we've got a plethora of ESG standards out there.

So for PE, who are they going to choose? Right? Where will they lean towards? So the UN SDGs is a framework that's used very often in asset management, but there have also been certain criticisms about the methodologies that are used for UN SDGs as well. But then you could maybe go to the other side of the equation and look at what are the metrics being used by SASB, which is already folded into the ISSP now as well, starting from next year.

And SASB is an investor's point of view in ESG metrics. Or you could go to the holy grail, may I say, and not to be blasphemous, but the holy grail of ESG frameworks and metrics such as the GRI initiative and the GRI, which was one of the first frameworks to basically release their a framework to measure E and S and G issues, right?

So I think it goes back to, “Well, which framework do I use and which will be accepted and which will I not be put on the hook for?

Sylvain: Yeah, you're right. And there's UN global complex UN SDGs and the EU taxonomy and emerging frameworks, to be honest, in a lot of cases when the data is available, we find that investors are kind of happy to adapt. If it's SASB, it's SASB. If it's UN global compact, it's UN global compact. But the biggest issue that we see usually day to day is actually the coverage. So if you have to choose the framework, it's likely because the data is not there and it's not standardized. And we found that this puts kind of mounting pressure both on ESG teams, deal teams, even corporates because they have to make all of the choices. They have to actively collect the data, and they don't have that kind of passive approach that ESG rating agencies are providing to the public market where they can just say, “Okay, I'm, I'm gonna go to a big provider, buy that data off the shelf, and I'm just gonna use that framework, that exact taxonomy, and that specific focus.”

And to the extent that some of our clients in private equity ESG teams have even to be the ones due diligence from an ESG perspective on any new deal coming to the pipeline. So that burden can be pretty high, actually. It can be a very difficult task. And I see a lot of ESG teams that are significantly understaffed actually in private equity and where it's a bit difficult to really handle all these tasks without the passive data approach.

ESG monitoring on private companies

Sylvain: Right. So let's move to the core of our presentation and discussion for today. So as you understand, our perspective on the market is that with a lot of these difficulties to track private assets and the lack of that standardized data, AI and natural language processing, so algorithms to extract information from text, are one of the only solutions that can provide that level of standardized off-the-shelf data, and that can help complement questionnaires and other active approaches. So the goal for today is to start diving into specificities, a high-level review of NLP technologies, leverage for that context. And also, Navin, I would love to have your view on how you see NLP leverage in general in financing private equity for these types of use cases.

Navin: So NLP is very, very important and a very powerful technology competency that can be used, especially when we are thinking about where is [00:21:00] data sitting, right? You know, so when we think about, and let's go back to what I was saying when we opened and open our sustainability and what type of sustainability data, a lot of the data could be at a geospatial level. Some of the data could be location-based. Some of the data could be unstructured and structured, right? So let's say you wanted to basically pick up the flood risk data for where the PE has taken a position. And let's say, a social housing project, right?

Now, can I use NLP to basically go and read certain documents which describe climate conditions? Or can I use NLP, including AI and ML, to go and read, let's say, climate models? How do I look at, for example, let's say, let's go back to oil and gas, right? Because [00:22:00] everybody likes to hammer the oil and gas industry, right?

You know, how would you go and use technology to go and identify what's the methane plume or the methane leakage rate, or the CO2 leakage rate, you know, for a very specific location? And again, that data might be sitting in certain documents, regulatory documents, for example, where maybe the EPC in the U.S. has replied back, or the environmental agency in the UK has replied back.

So again, understanding, and this goes back to, you know, understanding the sector where the data is sitting and how can I go and then use NLP on those, you know, sector regulators and sector regulations?

Sylvain: Yeah, that makes sense. That makes sense. And by the way, you're right to point out that there are things beyond NLP. Geospatial data is also something that is really interesting in that space.

Our focus is really NLP, basically extracting insights from text. That's basically that branch of AI. And usually, we see that the main use cases are identifying context related to ESG and sustainable development goals. For example, at SESAMm, we track 90 different risk categories off the shelf. And the algorithms are detecting like a human being, “Okay. This is actually related to biodiversity. This specific article is mentioning a lawsuit, and this one is about a strike.” We're discussing train strikes right before, during the preparation, so all of these E, S, and G contexts. And without going too deep into the technology, a lot of this is transformers, language models, deep learning for those of you that are more on the tech-savvy side. And the technology has really drastically evolved in the past three years. So it's getting really, really good.

The advantage of NLP is that it's pretty universal, so any assets and company are gonna be mentioned on the web, it's gonna have a website. There are some very rare cases that are not the case. I can think of one oil company in Texas in which one of our clients was doing a due diligence that didn't have a website at all. But that's really, NLP, in general, is very universal. And whether it's in finding information in news content, including local content, so when you can parse local languages in Holland or Ukraine, like we're doing right now for some of our clients, and analyzing potentially controversial context.

Social media is also interesting. A lot of the big controversies, for example, fiscal tax evasion, Panama Papers, Pandora papers, and Whistleblower alerts. All of this is actually revealed on Reddit in many cases, so lots of relevant information there. And then you have NGO websites and sustainability reports. So yeah, whether you want to identify information coming from NGOs, for example, or you want to find out about the latest news controversy, NLP is really powerful to help you do that at scale on a large number of different sectors and companies. I think that's one of the very powerful aspects of the technology.

Navin: And I think just to add as well, what is the perception of a PE investing in a company or holding a certain proportion of a company and, you know, the branding? And that's where we're seeing more investment in sentimental scoring.

So how would you do NLP, take those outputs, and create a sentimental score? So then how can you basically see what is the perception of the public or the perception of the media in terms of what is society saying, and then how would you then go and benchmark that too, does this conform to my ESG strategy, for example, especially the S and the G, which tend to get overlooked because we all seem very hung up about the E, which is also important as well. But let's not forget the correlation between E and S and G.

Sylvain: Yeah, you're very right, and actually, you're, it's interesting to mention sentiment because in the algorithm that I used, sentiment analysis is very common. And for me, from an NLP perspective, ESG is a bit of an extension of adverse media monitoring.

So you're trying to identify a specific risk that an algorithm is going to classify as, okay, highly negative sentiment on an article. And then you're gonna associate a context extraction algorithm around specific ESG and SDG context that you then need to map to a specific regulatory framework. So there is an additional layer, but in a lot of cases, it starts either from reputational insight sentiment or information extraction. I'm looking for a specific number inside report carbon emissions specifically or something similar. So yeah, from technology-wise, it's really an extension of sentiment analysis, and a lot of companies come from that sector initially. And so does SESAMm. We started with reputational insight and, a few years ago, starting to really build that ESG expertise along with a partner ESG rating agency in France.

Navin: And I'm pretty sure let's take a case study such as Tesla. You know, Tesla was thrown out of the MSCI ESG index and replaced with who? Exxon. Mr. Musk, aka Iron Man, was not happy. But what did Mr. Musk not think about? The S and the G in the overall ESG context, but also, what is the sentimental [00:28:00] scoring analysis? Especially that, well, he's decided to buy Twitter now, but he likes to blast off on Twitter as well. So maybe not very great for the sentimental scoring side. Just a sense of humor there, you know, to keep everybody awake.

Sylvain: You're right. And not to advertise or anything, but actually, since I'm published on this, so you'll find that there is a study on Tesla, our blog, because we were interested in understanding whether from a reputational basis that could be justified. And from a web perspective and media perspective, their level of scandals that Tesla is generating on the social and governance side is pretty intense, actually. And even when you look at things like how viral is the information and how relevant it is to traditional ESG metrics. It's not always looking good for them.

Navin: Of course. I think this is where NLP plays its part, right? Because by understanding where you are now, you want to look at your future. You want to be thinking about how am I going do regression or an extrapolation out to look at where are carbon emissions going to be for my company or what amount of CapEx do I need to put in to basically either address or make better my ESG metric that I'm trying to measure as well. Because at the end of the day, if corporations do that, then financial services will say, “Well, you know, I'd like to take a slightly picker position, or I'd like to invest in this PE company because they're doing the right thing.” That will make my metrics look good. And also, I will minimize my reputational riskbecause the last thing a financial services firm wants is to be investing in a PE company that's invested in, let's say, unethical companies with very bad ESG scores and very bad ESG data. And I think that's where SESAMm comes in, Sylvain.

Sylvain: Yeah, exactly. And sometimes, these controversies are tough to track. We have cases where even the portfolio companies themselves are not aware that one of their subsidiaries is involved in a huge scandal in a specific Asian country for which they'd actually don't control the information. And that's actually a good segue into kind of the practical use cases. The main ones that we encounter for NLP ESG indicators right now are monitoring of private equity companies, private debt supply clients, and due diligence.

And due diligence is actually also important because, in a lot of cases, someone at the private equity firm has to check a box saying, “This company is not exposed to adverse ESG controversies.” And checking that box is not a neutral thing, and there's a lot of responsibility. It's gonna be either an ESG team doing that and being flooded with a lot of work, or it's gonna be the deal team who sometimes doesn't have the basically the knowledge of the regulatory framework or on ESG, or sometimes it's a bit more systematic. And so we also work with the data team or data science team. They're looking to embed more systematic data sets. So I think we can discuss these a bit more and in the context of specific examples. So on monitoring in due diligence. I’d be happy to have your view on this, Navin, and see [00:32:00] what you've seen on use cases such as supply chain due diligence, ESG and credit alerts, or ESG controversy monitoring, for example.

Deep dive: how technology assists private equity firms from an ESG perspective (3 examples)

Navin: Definitely. I mean, look, with a number of our clients that we work with on ESG, a lot of the focus has been on, is this company going to be creditworthy in five years’ time, in three years’ time? Because when we look at climate risks. It's been proven that there is a correlation between your credit rating and how you manage your climate risks. It's very also clear that how does consumer sentiment and consumer perception impact revenues of a product port if you have poor SMG and poor E scores as well.

And companies that we work with on the financial side and the corporate side are investing a lot of money in trying to understand, if I don't do X, Y, Z, what does that mean for the valuation of my assets on the balance sheet in five years time, 10 years time, 50 years time, and the savvy ones out to 50 years as well. Because we gotta remember climate change is a long-term subject that needs to be addressed, and climate change is becoming more frequent and more severe as well. And we can see that, you know, with numerous weather-related disasters that are reported around the world. But how about the supply chain? And a great example is especially when we came to sort of the end of the Covid Pandemic last year and how materials weren't readily available as well. Again, the types of data insights in terms of building dashboards to help our clients understand, you know, where in the supply chain do I have issues and where do I start flagging those issues, and how is it going to hold up production of my component, my product, sorry, as well.

And at the, you know, last but not least, again, controversy. Right? We've worked with a number of companies which have had certain issues that have come out in the press as well. Sometimes these companies have happened because of bad data or mismanagement of data, and we help our clients try to understand okay.

If you're gonna be invested in XYZ company, you really need to understand what does this mean from your S and your G. You need to put enhanced [00:35:00] diligence on the S and the G. So, for example, if you're producing electric batteries and you're supplying electric batteries to, hey a bunch of companies, Well, what about child labor? What about human rights? What about how people are being looked after in terms of a social element?

And again, a lot of this is done by, how do you do that? NLP. You need to basically be reading and understanding what is being reported out there via so many different sources of information. So not just the media and doing sentimental scoring, but also how do you do NLP, unlike, let's say, a human rights report or a UN report that is issued out. So technology is very key and critical to help us do that. But how do you standardize is the point, right? And how do you compare? And that's the probably number one challenge at the moment.

Sylvain: You're very right. And I think what you were saying about analyzing controversy, for example, on child labor, and like there's really a data aspect to that is how do I do that in the local language? That's actually a big issue, even for people doing it manually. It's actually an issue. You have to have international teams, et cetera.

At SESAMm, we cover around 20 different languages, and one of the use cases I wanted to mention today on supply chain detection, SDGs, et cetera is, for example, we worked with a large private equity firm in Japan on helping them evaluate the sustainability of a green tech investment that they were going into.

And so they wanted to make sure that the company was actually really well perceived from a UN SDG perspective in Japanese media and in Japanese consumer discussion. And in addition to that, they wanted internationally, in almost every country worldwide, to make sure that there was an appetite for, in that case, sustainable material that the company was producing, which would have taken multiple surveys and pretty difficult work to actually do this. And so that's a typical use case: early-stage due diligences, assessing the impact thesis around a specific company, and having to do that on an international basis is super difficult, and NLP is a good enabler for that specific topic that's applicable to impact fund, but also in the context of risk identification.

And…

Navin: I think also… Sorry, can I just add something there? You know, like you talking about the Japanese firm, right? That this could be any emerging market company or, let's say comp a non-English based country, right? You know, where English is another [00:38:00] primary language, but you might be a Western company, but you need to really understand, you know, what actually is going on.

So what is the media writing about? You know, right down to, you know, that local language, you might be a dialect of the country, sovereign language as well. And then just that's the sheer amount of people you need to get to go and work backwards to understand your E, S, and G. Sorry, Sylvain. I thought I would add that in there.

Sylvain: Yeah, that's a good point. And actually, it relates also I found on the credit side. So in a lot of cases where our clients want to do on the ESG side of credit like you said, is assess how, basically assess their, the relevance of ESG in terms of potential default. That's the key focus of a lot of private credit investing, and it's expressed most of the time in terms of early warming.

So that means that your goal is not just to have information that everyone else has, but really to dive into these local sources, social sources, et cetera, to make sure that you can detect potential, great risk in advance. And a good use case that we had a few years ago was on WeWork, for example. So yeah, you all know WeWork global company, et cetera.

But what's interesting is that at SESAMm, we're able to get inside local sources and more social information and local forums. To identify that there was a pretty significant default risk on WeWork at the top of that controversy, right before Fitch announced it, and then Fitch issued a report and said there is actual default risk on WeWork.

You guys should be careful. And the credit rating of the firm was significantly downgraded. So early warning is pretty critical in credit. And what's interesting is that it's not just private debt; it also applies to high yield. We see a lot of asset management firms they're looking also to get access to these types of early warnings from local news.

Navin: No. Yeah, I can't fault WeWork because I do enjoy the free beers that they provide as well. Maybe that could be part of an early warning system, the amount of beer being drunk, you know, and leading to a default.

Sylvain: I will not comment on that.

Navin: But look, you make a very good point, and on a serious note, Sylvain, and it is very, very key that we get on top of this correlation between how ESG performance is, what metric should be tracked, and what's the correlation to credit ratings, right? You know. And I think the biggest issue at the moment is that we have slightly different methodologies from different rating providers on looking at, let's say, an ESG rating that how do we basically close the disparity so then we can understand the correlation of, let's say, an early warning system that basically triggers you, that says, okay, ESG metric deterioration, what does that mean for credit risk, right?

Also, let's not forget ESG risks. So when we look at our 99 percentile, how do we capture those black swan events? And can black-swan events arise from ESG as well? Right. You know, and there have been many, there have been certain examples in the past where the environmental aspect of managing or mismanagement of climate risks has not been done correctly.

And companies have actually ended up, you know, see the prices spiking, default eventually happens, the company goes into bankruptcy, especially in the U.S. as well. So again, how do you use the power of technology to start flagging and start bringing that up, right? You know, and I think this is how the role of a risk manager as well is going to change, Sylvain.

You know, the role of a trader or the role of an investor, the role of an asset manager who is investing, and let's say PE is going to change, you know. What are the metrics or early warning metrics that they need to be monitoring? What type of reporting does a risk department need to do?

Sylvain: You're right. And actually, if we were like just talking about trading, some of our clients are doing CVS forecasts basically based on the data. In the PE space, what it looks like, and we promise practical use case also. And in the private equity space, in private credits, what we see is, for example, we have a large client in the U.S. that is tracking in a private credit portfolio of around 5,000 assets. And so they're not attempting to predict CD spread, et cetera. What they're doing is they're receiving daily alerts for both ESG and SDGs on the entire 5,000 assets. And private debt has a lot more assets than private equity, so it's particularly tough on the team to track these day to day, and so that helps them centralize monitoring.

They receive all of the alerts. They have access to the dashboard, etc. That makes this process really independent from deal teams, so they don't have to take that information from each deal team individually, and they receive email deliveries and files integrated into their own tools. And so with that, they're kind of safe in knowing that all of the information from all of those sources are already there.

They don't need to wait on anyone. They don't need to ask the company to report on [00:44:00] these topics. From a reputational ESG perspective, they are well covered, and they will not miss any ESG controversies. So that's one of the really practical use cases on private credit.

Navin: And I think it goes to show that I think when we look back at the past 45 minutes, what we've been talking about, right, that in private equity, ESG risks, climate risks, sustainability-related risks are now truly correlated, and truly part of financial risks now as well.

I think we had this historical perception that we would see carbon emissions as a non-financial risk. Okay. You know, we could say it has non-financial data, you know, maybe it's more scientific data, but we're now really bringing in that data and looking at what is the impact of financial risk and financial metrics.

Something that we, you know, that wasn't done extensively 10 years ago. So very exciting times.

Sylvain: It is, right? And that's very much one point with topics such as double materiality. How is ESG actually impacting financials? And yeah. One last use case I wanted to mention. We promised three, so we're gonna honor that promise, is actually controversies and ratings. Something that is done a lot in the financial market space and in public. There's one example that we recently had in France a few months ago. There's a nursing home company called Orpea that has been exposed to massive controversies, in particular coming from local journals, mistreatments of patients, which is, really, yeah, problematic on the social side.

And what's interesting is that rating agencies, public rating agencies at the time we’re [00:46:00] seeing, allocating the best ratings to the company because it has some social impact factors, et cetera, and they were missing on that local news information and on everything happening. And NLP is very powerful in helping make these ratings a bit less dependent on the company's reporting information, identifying the local information. And for us, Orpea was one of the companies we identified a lot of critical controversy risk and where the rating from an NLP perspective was really poor, simply because we had a bit more information. And in some cases, it's a way to complement the data that exists. So a lot of our clients are doing this, is leveraging these scores and leveraging the controversy alerts in order to do monitoring, like we discussed, in order to do due diligence.

We have a large private equity firm that does that, around 200 companies. And all of that data then gets integrated internal systems, CRMs, like dCloud, et cetera. And then people can then rank companies, which is something that people do in public markets every day. So as to say, okay, I'm gonna invest in the best-ranked companies, or I'm going to exclude the worst-ranked.

And this is, I think, something that is gonna continue to emerge in private equities, not just events controversies, but also ratings, actual scores that are useful to have a statistical view on the portfolio.

Navin: I think, you know, it's very sad to hear a company executing those types of actions. And I think this is where it shows technology can basically save lives and prevent further controversies.

Now, I think, taking a step back, how would you take an assessment framework? Will the health and social assessment framework from the French authorities? [00:48:00] And how would you map that into an ESG framework, right? Because as I said, when we opened 45 minutes ago, each sector has a different ESG problem. Each sector is measured in a different way.

So how do you go and take those regulations or sovereign regulations, sector, sub-sector, and then how do you do that sub-mapping to what the issues are from an E and S and G perspective? And I think that's where the government as well need to be clear about what are the E and S and G factors that are important to them.

That will give a lot of clarity to the financial sector and especially, you know, private equity firms.

Sylvain: Thanks. I think that's actually a good conclusion, and it's also important to know that not everything is easy in that sector. So we understand here that there is definitely a data issue. There are definitely a lot of problems still to solve, especially in the private equity space, around which framework do we use for that specific type of application.

I hope that we provided some answers and, as promised, three concrete use cases, which you can take back to your team and which you can discuss as practical applications. So I think we're gonna close this session here and go to Q&A and take some questions from the audience and see what people are asking about.

Q&A

Sylvain: So let's move to the Q&A part.

Great. Okay. So maybe as the first question for you and Navin, how is everything here related to SFDR and other regulations in particular [00:50:00] Europe, and how have tools evolved with new practices, standards, and market expectations? I think that's something that you touched on with the different regulations.

Navin: Yeah. Why? Well, why don't I take that one? So, look, SFDR, it's driving the baseline for the asset management industry, right? The SFDR is also telling you your classification of is your fund green or is it not green? Is it ESG compliant? Or if it's not compliant, right? But the issue with SFDR is that it's connected to the taxonomy, right?

And that's the EU taxonomy. And the taxonomy is obviously like a political football, given the current energy crisis that we are going through as well. And how would that be relevant to, say, private equity or, you know, companies? Now we've got to basically be very clear. Where does funding come from?

A lot of funding into these PE companies comes through certain areas from the asset management industry. So when you have such a substantial baseline regulation, such as the SFDR, such as the EU taxonomy, which is actually being used as a baseline standard around the world and being globally accepted, right?

It is going to find, its say, it's going to find its way in to basically assess, do assessment on PE companies. Very, very, very simple. But that answers the question.

Sylvain: I think it does. And so we have a couple others. Finding data on small firms can be challenging, whereas the coverage of private companies for these types of solutions.

[00:52:00] That's, yeah, I think I'll take that one. This one is a key data question. So yeah, there are millions of companies to track, and NLP is a good enabler for this. At SESAMm, we track around five million companies. So it covers every company, almost every company worldwide, beyond growth, so it's very applicable to private equity firms.

And that coverage is pretty high compared to traditional solutions in the market that are gonna cover on 50,000 firms or so. So this is where we see a big, big edge right now, [?] covering everything that no one else can cover because the companies are too small because they are too local because they don't disclose information.

So I think that's actually one of the key topics and discussions.

Navin: I think we have another question as well. Number three?

Sylvain: Yes. And…

Navin: what is the main benefit, or what is the main benefit of using AI in ESG? Can it also be applied to local websites, or why didn't I start and hand it over to you, Sylvain?

ESG continues to expand. There are thousands and thousands of metrics out there, right? Are you going to go and basically put a human being to basically look at every single metric in the E, and the S, and the G area?

Very challenging. And how are you going to find the data? I think AI, including ML, including NLP, is the way forward and something that we substantially use with our clients.

Sylvain?

Sylvain: I think you summarize it well. So coverage is a key topic. And local information is very critical. Actually, I think there's one for you, Navin. How much of an effect will ESG have on valuations? And how can a company attribute a quantifiable value to this?

Navin: Hugely. With… When we look at millennials and how they want to invest as well, they are taking more and more environmentally conscious decisions. And if they feel that they do not like a company, you know, they can get together, and they can actually cause a company just by public perception to be impacted. The other thing, you know, away from the millennial issue, which is not hugely, but it's important, we have to remember millennial mindset is changing and which kits they purchase and how they invest, but also the valuation of companies is very much driven by intangibility.

When we look at accountants, and they're looking at the balance sheet, when we looked at, and let's take listed companies, you know, 20 years ago, 20% of a balance sheet used to be intangible. Now 80% of a balance sheet is intangible. And where does intangibility come from? A lot of intangible tangibility is measured by ESG as well.

Carbon emissions, social-led governance issues, child labor, mental health employment rights, gender equality. These are all being looked at using techniques such as AI and NLP on the impact of a company on its balance sheet. And as accountants and especially high SSP get better at quantifying what the intangibility is, it’s gonna be even more and [00:56:00] vitally important to the valuation of a company, which will then be taken as a proxy for private equity companies.

Sylvain: Yeah. You’re very right. Then these three resonate with what we mentioned before, like double naturality, ESG events now just don't have an impact on the environment or on the different stakeholders. They actually have a financial impact, and it resonates. Also, we work with an ESG rating and credit agency in France called EthiFinance, which helps us put together some of the frameworks that we use for monitoring assets from the ESG perspective. And that's really their model of double materiality, taking to account the financial side of ESG.

And a lot of people have realized that in public markets, equities have been heavily impacted by ESG controversy, and the impact is growing bigger and bigger for every new event. So I think that's also a good thing or interesting thing to take from this, [00:57:00] is that ESG is having a real impact on a company's financial. And this is a growing trend over time.

Navin: And I think, just to wrap it up, that if we want to prove it, look at delivery. Which listed on markets look at boohoo, which is listed on the markets. And last but not least, Mr. Musk and Tesla. So some very fascinating movements in the valuation of companies because of ESG.

Sylvain: Thank you. That makes total sense. Thank you very much, Navin. Thank you, everyone, for listening in and for joining this webinar. We were super happy to tell you a bit more about these three use cases and ESG and natural language processing. And happy to have a follow-up conversation. Please feel to reach out to Navin and to myself, and have a great day.

Thank you very much.

Navin: Thank you so much, everybody. Have a great day.

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.

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.

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.