Northvolt was Europe's flagship battery champion, backed by Volkswagen, Goldman Sachs, BMW, and BlackRock, and capitalized with over $13 billion in debt and equity. Yet between 2022 and 2025, it became the largest industrial bankruptcy in modern Swedish history, shocking the industry and investors alike. We took a look back at SESAM’s controversy data to see if there were any early warning signals.
The Warning Signs
In September 2022, the first public reports of production delays emerged at Northvolt's Skellefteå gigafactory. By the end of 2023, less than 1% of the planned 16 GWh capacity for 2024 had been delivered. Then came the fatal workplace accidents linked to electrical experiments, and in June 2024, BMW canceled a €2 billion contract. By September, 1,600 employees had been laid off, the cathode expansion was abandoned, and the CFO was replaced.
The Fallout
The consequences were swift. A Chapter 11 filing in the U.S. in November 2024. The resignation of founder and CEO Peter Carlsson. Over 5,000 jobs lost. Major write-downs across the cap table, from sovereign-backed pension funds to global automakers. In March 2025, Northvolt filed for bankruptcy in Sweden, the largest industrial failure in the country's modern history. The reputational damage extended well beyond the company itself, reaching investors, suppliers, and the broader European battery ambition.
Download the Case Study
Gain more insights into the largest industrial bankruptcy in modern Swedish history
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.
Paris, France and London, UK – July 11, 2024 – SESAMm, a leader in AI-powered text analysis for ESG insights, is pleased to announce that Sustainable Fitch, a leading provider of ESG data, analysis, and research for the sustainability and fixed-income market, will be integrating SESAMm’s data into its ESG Scores and Ratings.
Sustainable Fitch provides ESG data and analytics to both the public and private markets, including asset owners and asset managers. Sustainable Fitch will use SESAMm analytics to monitor companies and industries at a granular level, allowing them to provide further insights to clients to aid their investment and due diligence decision-making.
Sylvain Forté, CEO of SESAMm, commented, “We are excited that a recognized leader in ESG analysis is using our insights for their ESG analysis. Our AI-powered text analysis will provide deeper insights and broader coverage, helping Sustainable Fitch to deliver high-quality ESG data and ratings.”
Gianluca Spinetti, Global Head of ESG Analytics at Sustainable Fitch, added, “Working with SESAMm technology allows us to leverage their advanced solutions to enhance our ESG Scores and Ratings offering. By integrating SESAMm’s extensive data coverage, we can offer our clients more comprehensive ESG insights.”
About SESAMm
SESAMm is a global leader in AI-powered text analytics, specializing in providing insights on ESG controversies and positive-impact events. With its cutting-edge technology, SESAMm helps private equity firms, asset managers, and other financial institutions, as well as ESG consulting firms and rating agencies, monitor and analyze vast amounts of textual data to identify potential risks and opportunities in their investments. For more information, visit SESAMm.
About Sustainable Fitch
Sustainable Fitch provides rigorous, human-powered sustainability research, analysis, and data for the fixed-income market, including ESG Ratings, Second Party Opinions, thought leadership, and more, with a focus on ESG impact. Our objective and substantive suite of products provides transparency, consistency, and granularity that enables confidence in decision-making. Powered by the human insight that has differentiated Fitch for over 100 years, Sustainable Fitch brings experience and heritage to help the financial community make smarter decisions using the best ESG information available.
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.
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.
.png)