Introduction

As generative AI has grown from a fledgling concept to a force disrupting most industries, its broader implications have come under scrutiny. Public perception of generative AI has also evolved significantly due to its association with various Environmental, Social, and Governance (ESG) factors. In this article, we’ll offer an extensive ESG analysis of generative AI, focusing on how different industries react to it, the ESG risks it potentially fuels, and the ESG positive impact events it has given rise to.

Generative AI: Public Perception Since Launch

Generative AI was initially met with widespread enthusiasm as the next evolutionary step in artificial intelligence. OpenAI's ChatGPT garnered significant attention quickly upon its release in 2022, as it amassed 100 million monthly active users in just two months post-launch. However, as its capabilities have become more powerful and universal, many ESG controversies have emerged, impacting the public sentiment towards the technology. A notable drop in sentiment polarity was observed from October to December of ‘22, going from 0.4 to 0.22. The decline in polarity was attributed to some critical topics, notably the environmental toll of its energy consumption and the ethical difficulties posed by its potential to disseminate false information.

* Polarity, a proprietary metric developed by SESAMm,  ranging from -1 to 1, represents the aggregate of positive and negative sentiment.

Generative AI and its Implications on ESG

In What Industries Is Generative AI Mentioned More Often?

As expected, the IT industry was initially the most mentioned, along with Generative AI. However, as the technology became more widespread, other sectors have garnered more attention among web publications and social media. In particular, the communication and finance sectors are capturing a substantial share of the attention. In particular, data privacy in finance and communications are the main concerns, and fraud for finance is also being widely discussed on the web.

ESG Controversies Fueled by Generative AI

When we looked at ESG controversies and risks in detail, we found that most of the attention and mentions are related to social risks, particularly Human Rights (right to privacy), labor rights, and customer relations (customer privacy). Governance has also gotten its fair share of ESG controversies, primarily focused on anticompetitive practices (copyright infringement). On the environmental side, controversies are concentrated on water consumption (by Gen AI tools) and climate change, specifically energy consumption. However, the number of mentions and controversies has decreased considerably.

Data Breaches: The Focal Point

By far, the lion's share of ESG controversies and mentions gravitate towards social risks, specifically data breaches. From Italy banning Chat GPT in April to Samsung’s alleged data leak in August, controversies around data privacy have been among the most concerning topics surrounding Chat GPT ESG risks. In just five months, mentions of data breaches went from virtually 0% to over 10% of total mentions.

Digging deeper into data breaches at companies, we found that the number of breaches did increase significantly after generative AI tools became available. In particular, we see that the number of internal (employees) vs. external (non-company affiliated) data breaches increased by almost 50% when using generative AI tools from 14% to 21%.

The Silver Lining: ESG Initiatives Generated by Generative AI

Despite all the risks and controversies emerging, generative AI is also an enabler of positive ESG initiatives. Interestingly, on the positive impact side, we see a similar volume of mentions of initiatives on the three ESG dimensions.

Generative AI has shown promise in optimizing energy use, reducing waste, and even modeling and mitigating the impacts of climate change. On the environmental side, we see a rapid increase in mentions related to its applications in efficiency and productivity, asset reliability, operational safety, lower energy consumption, and reduced environmental impact.

The technology also has the potential to revolutionize healthcare by enabling more accurate and early diagnosis, thereby contributing to social well-being. Generative AI could also transform web surfing and make it easier for users to navigate the internet and find or generate information.

Conclusion

As our analysis shows, generative AI is bringing unprecedented capabilities and complex ESG risks and controversies. We expect to see it evolving, with public sentiment shifting and industries grappling with its ESG implications. But we are still in the very early stages of this new trend and will continue monitoring its evolution.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

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.

​FinTech and Big Data for financial markets – SESAMm raises €2.6 million Euros. Objective: to accelerate its international development!

SESAMm – a specialist in the exploitation of applied Big Data to asset management – has just finalized a capital raise representing a new acceleration phase of its growth. The company is aiming to become a leader in the fields of alternative data and artificial intelligence for the financial markets.  

Innovative and powerful solutions to forecast the markets

SESAMm, with its offices in Metz, Paris and Luxembourg, developed two highly innovative asset management solutions to forecast the markets. The first solution, since 2014, is the Data Stream Premium, a predictive trading signal generation service based on Big Data and artificial intelligence. It enables the alternative Investment Funds and the Hedge Funds to generate outperformance in the financial markets, and this first solution attracted major players in the sector.

In 2017, L’Humeur des Marchés was launched, a data visualization platform as well as an API access (Programming Interface), which offers alternative analysis and data on many thousands of assets, available in 8 languages including Chinese and Japanese. This perfectioning of the algorithms and tools, which improves the arbitration decisions, the research and exploitation of market opportunities, increases the international notoriety of SESAMm.

“The American multinationals are interested in us, this is very promising.” reveals Sylvain Forte, co-founder and CEO of SESAMm. “In one year, we have almost doubled our staff and we plan to hire more than a dozen of new employees until the end of 2018 in fields such as data science, software development, automatic language processing, and quantitative analysis. We have the objective of doubling our turnover for the year 2018 and we are aiming €20 millions of turnover by 2021.“

An expanding Startup

Today, the startup SESAMm has offices in 3 cities in France and Luxembourg; SESAMm has 18 employees and relies on top profiles in the fields of artificial intelligence and quantitative analysis. Its commercial activity is booming, with the recent signing of two big agreements with Nikko Global Wrap (one of the subsidiaries of Sumitomo Mitsui Asset Management, a major asset manager in Japan) managing JPY 1.7 trillion and La Française Investment Solutions, a subsidiary of the Group La Française (in the top 10 of asset management in France, managing $64 billion).

“Our objective is to enhance our positioning in France and then in Europe, particularly in the beginning of this year with a new subsidiary in London. Our development will also go through the United States. The growth prospects of our market are strong: from $67 billion in 2014, the market for software for asset management should grow to nearly $104 billion in 2019 according to MarketsAndMarkets”, stated Sylvain Forte.

Capital raising: confidence and growth

The bank “Caisse d’Epargne Lorraine Champagne-Ardenne” and the fund “Fonds Venture Numérique Lorrain” are the main investors in this capital raising in which BPI, the bank “Banque Populaire de Lorraine”, the network of “Bourgogne Angels” and other national and international business angels have confirmed and renewed their support and participation. And thanks to their supports, SESAMm can accelerate its national and international growth.

“We are proud for being the first investors to believe in the potential of SESAMm and we are looking forward to this new capital raising,” explains Marie Tribout, chairman of the fund “Fonds Venture Numérique Lorrain”. “We participate in this capital raise for two main reasons: first, our conviction about the relevance of the SESAMM project and its team excellence; secondly, this participation is part of our Neobusiness system extension, which is based on supporting closely the growth of startups in our region.” said Benoît Mercier, chairman the bank “Caisse d’Epargne Lorraine Champagne- Ardenne”.

“Our partners trust is precious” explains Pierre Rinaldi, co-founder and COO of SESAMm.” It validates the excellent performance of our technologies, which are improved continuously by our R & D center. We have new applications in the works.”

For this fundraise, SESAMm has been helped by Eric Bezard, specialist in administrative and financial management and equity capital markets, from the financial consulting firm TMA. SESAMm was also helped by the Parisian business lawyers firm Orrick with a team led by George Rigo, associate, with his collaborators Olivier Vuillod and Cécile Renaud.

Unique and innovative technologies

“SESAMm is a valuable partner for Eagle Alpha”, said Hugh O’Connor, the director of Data Sourcing and Partnerships at Eagle Alpha. “The SESAMm products provide a level of scientific rigor, transparency and flexibility that distinguishes the company from other market players.”

“The French Investment Solutions is collaborating with the SESAMm team in order to explore potential investment approaches based on artificial intelligence. The LFIS (La Française Investment Solutions) uses SESAMm technologies in order to exploit the Big Data in the field of quantitative management “, said Guillaume Garchery, the portfolio manager and head of the quantitative research R&D at La Française Investment Solutions.