VIDEO: Monitor Clients and Suppliers Using AI - FinovateSpring 2023
June 22, 2023
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5 mins read
CEO Sylvain Forté demonstrates SESAMm’s NLP platform TextReveal® ESG Alerts and Monitoring for public and private companies at FinovateSpring 2023. He uses Wirecard, a German FinTech company that went bankrupt following a fraud accusation, as an example to illustrate the platform's ability to identify potential controversies and assign them severity scores automatically.
Furthermore, he demonstrates another use case with Twilio, an API messaging and phone services provider, which had previously been exposed to major cybersecurity issues. TextReveal ESG Alerts and Monitoring was able to immediately identify the controversial events, providing valuable insights to users.
In this video, Sylvain Forté also showcases what differentiates our solution from competitors while shedding light on our massive 20-billion article data lake, our advanced AI technology and algorithms, and how we combine both to provide major financial institutions, private equity funds, and banks with timely and accurate data to help them detect any issues with investments, suppliers, or clients.
Watch the full recording:
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The European Union faces a significant internal rift as its largest economies take opposing stances on the bloc's Corporate Sustainability Reporting Directive (CSRD) and CSDDD (Corporate Sustainability Due Diligence Directive), highlighting the delicate balance between environmental ambition and economic competitiveness in today's regulatory landscape.
A Continental Divide
According to recent reports, Germany and France—two of the EU's economic powerhouses—are pushing for a two-year delay to the CSRD implementation. This stance contrasts sharply with Spain and Italy, who advocate for maintaining the current timeline while potentially offering concessions to smaller businesses.
On one hand, at the Choose France summit on May 19, 2025, French President Emmanuel Macron called for the European Union to abandon the CSDDD, citing concerns over its potential impact on European competitiveness. Macron's stance aligns with German Chancellor Friedrich Merz, who also advocates for the law's repeal, arguing that it imposes excessive burdens on businesses, especially amid global competition from the U.S. and China. While some EU member states and industry leaders support revising or delaying the directive, others, including left-wing politicians and NGOs, defend it as essential for upholding European values and sustainability goals.
Spanish Environment Minister Sara Aagesen and Economy Minister Carlos Cuerpo, on the other hand, emphasized in a letter to the European Commission that sustainability reporting "supports the values and the priorities of the EU even beyond our borders, setting an example of leadership." Meanwhile, Italy's finance minister Giancarlo Giorgetti specifically urged against delaying CSRD for the tens of thousands of companies already preparing to report this year.
Why France and Germany Are Pushing Back: The Competitive Concerns
The Franco-German resistance to the current CSRD and CSDDD timeline stems from several key economic and practical concerns:
France's pushback comes amid broader economic concerns. The French government described the CSRD rules as "hell for companies," reflecting anxiety about imposing additional costs during a period of economic vulnerability. Both countries fear that excessive regulatory requirements could further weaken their competitive position against less-regulated economies, particularly the United States under the Trump administration, which has shown hostility toward environmental regulations.
Overlapping Regulatory Frameworks
German officials have pointed to the problem of multiple, uncoordinated sustainability reporting regimes. Kukies noted that "every CFO could tell absurd stories about how the same data has to be reported multiple times," arguing for a more streamlined approach where "each data point only has to be reported once."
Specific Reform Proposals
The German government has proposed significant changes, including:
This regulatory uncertainty creates significant challenges for businesses operating across the EU. Companies face difficult strategic decisions about whether to proceed with sustainability reporting preparations or wait for potential rule changes.
For investors, this division introduces several critical considerations:
Reporting Inconsistency: Different implementation timelines across EU countries could create a patchwork of disclosure standards, complicating investment analysis.
Competitive Impacts: Companies in countries maintaining stricter timelines may face higher short-term compliance costs than competitors in countries securing delays.
ESG Data Reliability: Delays could affect the quality and comparability of ESG data, potentially undermining investor confidence in sustainability metrics.
Strategic Positioning: Forward-thinking companies that continue sustainability reporting preparations regardless of potential delays may gain competitive advantages in attracting ESG-focused investment.
Looking Ahead
The European Commission plans to publish an "omnibus" proposal to simplify green rules for businesses, aiming to enhance competitiveness while responding to global regulatory pressures, including potential deregulation under a second Trump administration in the U.S.
This internal EU debate reflects a broader global tension between advancing sustainability standards and addressing immediate economic pressures. Navigating this evolving regulatory landscape will require flexibility, foresight, and a balanced approach to ESG integration for businesses and investors alike.
As this situation develops, stakeholders should closely monitor European Commission decisions and prepare for multiple regulatory scenarios across the EU's diverse economic landscape.
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.
The NZBA's announcement comes after a series of high-profle departures that began in late 2024. What started as a coalition of 43 banks at its 2021 launch had grown to over 140 institutions representing $74 trillion in assets by 2024. However, political pressure, particularly from Republican politicians in the US, warning of potential legal violations, triggered a mass exodus.
The departures followed a predictable pattern: Goldman Sachs led the way in December 2024, followed rapidly by all major Wall Street peers within weeks. Canadian banks soon followed, and the bleeding continued through 2025 with HSBC, UBS, and Barclays all exiting. Barclays' departure statement was particularly telling, noting that "with the departure of most of the global banks, the organisation no longer has the membership to support our transition."
Proposed Restructuring
The NZBA has now proposed transitioning from a membership-based alliance to what it calls a "framework initiative." This fundamental change would essentially transform the organization from an active coalition with binding commitments to a more passive guidance provider. The steering group believes this approach would be "the most appropriate model to continue supporting banks across the globe to remain resilient and accelerate the real economy transition in line with the Paris Agreement."
A member vote on this restructuring is currently underway, with results expected at the end of September. However, given the exodus of major institutions, the outcome seems predetermined.
Broader Climate Coalition Collapse
The NZBA's troubles reflect a wider crisis affecting climate-focused financial coalitions:
A member vote on this restructuring is currently underway, with results expected at the end of September. However, given the exodus of major institutions, the outcome seems predetermined.
Recent developments include a 23-state coalition warning the Science Based Targets initiative (SBTi) about potential antitrust risks, demonstrating that the pressure extends beyond banking to other ESG frameworks.
Market Implications
The NZBA's effective dissolution has several implications:
Fragmented Approach
Without coordinated frameworks, banks will likely develop individual approaches to climate commitments, potentially leading to:
Inconsistent standards and methodologies
Reduced transparency and comparability
Weakened collective bargaining power with policymakers
Regulatory Response
The vacuum left by voluntary coalitions may accelerate regulatory intervention:
Mandatory climate disclosure requirements
Government-imposed transition standards
Regional divergence in approaches
Investment Impact
For investors, this development signals:
Increased difficulty in assessing bank climate commitments
Greater need for individual due diligence
Potential opportunities in banks with strong standalone commitments
Looking Forward
The NZBA's pause represents more than just one organization's troubles; it symbolizes a broader retreat from coordinated climate finance at precisely the moment when such coordination is most needed. With climate risks accelerating and the urgent need for massive capital deployment, the financial sector's inability to maintain collective action represents a significant setback.
However, this may also create opportunities for more resilient, legally defensible approaches to climate finance. Banks that remain committed to transition goals may find competitive advantages in developing robust standalone frameworks, while regulatory bodies may step in to fill the coordination gap.
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.
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.
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