VIDEO: QuantMinds Interviews Sylvain Forté at QuantMinds International 2022
August 25, 2022
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5 mins read
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.
Over the past decade, many organizations have improved their carbon footprints, from recyclable and biodegradable packaging and single-use plastic to planting trees and reducing their greenhouse gas emissions. However, some businesses and companies looking to boost their eco-friendly image without committing to serious changes and addressing environmental issues have been associated with false green marketing. We call this "Greenwashing."
Defining Concepts
What is Greenwashing?
Greenwashing is a practice used by businesses to represent themselves as more sustainable than they truly are. Greenpeace and the Environmental Protection Agency define greenwashing as making false and misleading claims about a product's environmental benefits or practices, services, technology, or company practices. Greenwashing typically involves companies spending more money on advertising and marketing than on implementing sustainable business practices that minimize environmental impact. These false green claims can deceive consumers into believing that a product or company is more environmentally friendly than it is, leading to increased sales and profits. As a result, false advertising, misleading initiatives, and groundless claims have increased green investors' exposure to risks emerging from potential lawsuits from activist groups, image deterioration, and heavy losses in assets invested.
Greenwashing Mentions Over Time
In recent years, new concepts have emerged alongside greenwashing:
Greenwashing, Greenhushing, and Greenwishing Mentions Over Time
Greenhushing refers to a company’s refusal to publicize ESG information. The company may fear pushback from stakeholders who would find its sustainability efforts lacking or from investors who believe ESG undermines returns.
Greenwishing, or unintentional greenwashing, describes a practice where a company hopes to meet certain sustainability commitments but simply does not have the means to do so.
High-Profile Greenwashing Case Studies
When talking about greenwashing, the usual suspects are the oil and gas industry, the food and beverage sector, and other environmentally impactful industries. However, the financial industry has also been embroiled in its own greenwashing controversies.
It’s challenging to produce an accurate assessment of environmental, social, and governance (ESG) factors, which creates opportunities for companies to hide ineffective and fake green initiatives. According to Regtank, the main challenges to detecting greenwashing include:
Lack of reporting standards – There’s no universal set of standards for ESG compliance.
Lack of transparency – Companies often don’t disclose the specifics of their “green campaigns,” making it hard for investors and consumers to verify their claims.
Limited consumer awareness – Misleading marketing can exploit consumers’ eco-consciousness and brand loyalty, reducing scrutiny of false green claims.
These gaps lead to inaccurate ESG data and scores, allowing greenwashers to avoid accountability. Ultimately, detecting greenwashing requires careful scrutiny of company claims and a deep understanding of their supply chains and operations.
How Artificial Intelligence Detects Greenwashing
As greenwashing practices become more common, activist investors, journalists, and the general public are using social media, news outlets, and blogs to highlight false claims. Artificial intelligence (AI) has become an invaluable tool in the early detection of greenwashing by analyzing vast amounts of public data.
At SESAMm, we use generative AI and LLMs to identify greenwashing risks across billions of web-based articles. Our data lake covers over 25 billion articles in more than 100 languages from four million news sources, blogs, social media platforms, and forums, analyzing data on five million public and private companies. Through our AI platform, we generate reliable, timely, and comprehensive insights to detect greenwashing, monitor ESG controversies, and identify related risks.
The CSRD significantly strengthens the requirements for companies to substantiate their sustainability commitments. Mandating standardized and detailed ESG disclosures directly addresses the practice of greenwashing, where companies exaggerate their environmental credentials in marketing without meaningful follow-through. Under the CSRD, companies can no longer rely on vague or selectively presented data—any gaps or inconsistencies in their sustainability claims will be exposed in public filings, making greenwashing much riskier. This means an end to cherry-picked data and a shift toward more comprehensive, comparable, and verifiable ESG performance for investors and stakeholders.
The CSDDD (if it stands) further reinforces these efforts by obligating companies to go beyond marketing statements and prove they’re actively managing environmental and human rights impacts throughout their supply chains. This directive closes loopholes that greenwashing often exploits, such as highlighting only direct operations while ignoring supplier practices. By requiring due diligence on environmental impacts across the value chain, the CSDDD aims to turn sustainability from a branding exercise into a legal and operational priority. If real supply chain actions don’t support a company’s green claims, it could face legal action and reputational damage.
Looking Ahead
Looking ahead, greenwashing will continue to face intense scrutiny from regulators, investors, and the public. With evolving regulatory frameworks like CSRD and CSDDD, the pressure is on for companies to ensure genuine environmental responsibility—not just green advertising. At SESAMm, we believe that the combination of regulatory rigor and advanced AI technologies will play a critical role in uncovering false green claims and supporting investors in navigating ESG risks with greater transparency and accountability.
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.
In private equity, as in most industries, decision-making counts on accessing accurate and valuable information. However, these firms often encounter significant challenges when sourcing reliable data, especially when dealing with small, private companies. This article dives into the complexities of identifying high-quality information on smaller companies and underscores its value in investment decisions, operational efficiency, and risk management. It also explores how advanced artificial intelligence (AI) technologies are revolutionizing the identification of these risks, leading to higher rewards and more secure investments, thus providing a competitive edge.
The challenge of identifying valuable information for Smaller Firms
Lack of valuable data
Sturgeon's Law, which states that "Ninety percent of everything is crap (or noise)," becomes particularly relevant in the context of data sourcing. For private equity and investment firms focused on small companies, finding the golden nuggets of information amid the overwhelming amount of digital noise can be daunting. The data available on these companies is often sparse, fragmented, and difficult to uncover using conventional methods. This scarcity of reliable information makes it challenging for private equity firms to make informed decisions, heightening the risk of overlooking critical issues that could impact their investment process.
The difficulties extend beyond just locating information. Many small companies operate without a significant online presence or may not be required to disclose as much information as publicly traded firms. This lack of transparency can further blur critical data points. Furthermore, the data that is available is often unstructured, residing in various forms such as social media posts, obscure local news articles, or industry-specific reports. Extracting meaningful insights from these disparate sources requires sophisticated data processing capabilities, which traditional methods often lack. As a result, private equity firms are left with a significant challenge: how to separate valuable data from the noise without missing critical risk indicators, thereby optimizing their deal sourcing and investment strategies.
Diverse language and terminology
Smaller firms frequently face existential risks, and the potential rewards for identifying these risks early on can be significant for the private equity firms that invest in them. However, mainstream methods of risk identification often fall short, as these companies may not use standardized language to describe materiality. Instead, risks are discussed in varied and context-specific ways, complicating the task of recognizing relevant information. Therefore, it is essential to adopt a specialized approach that analyzes and decodes these firms' unique terminologies and business idiosyncrasies, ultimately translating them into a standardized language that can be effectively used in risk assessment.
The diversity in language is not just a barrier to risk identification but also to the communication of these risks within and between private equity firms. When a small firm uses industry-specific jargon or localized expressions to describe potential threats, it can lead to misunderstandings or underestimations of the actual risk. For instance, a manufacturing startup in a developing country might describe supply chain disruptions in terms that do not translate easily to a global investor’s risk framework. Additionally, cultural differences in how risk is perceived and reported can lead to further complications. This linguistic diversity necessitates the use of advanced natural language processing tools that can interpret data through a common lens while considering industry-specific contexts. For an insurance company, understanding financial models, insurance principles, and regulatory frameworks is crucial. Conversely, assessing risks for a beauty company requires a focus on product safety, consumer preferences, and market trends. By appreciating the specific contexts of each industry, private equity firms can better identify and evaluate potential risks, enhancing decision-making processes, risk and portfolio management strategies, and operational efficiency.
The dynamic nature of the industries themselves further complicates the challenge. For example, the tech industry evolves rapidly, with new risks emerging as technologies develop and consumer expectations shift. What might be considered a negligible risk today could become a significant issue tomorrow as regulatory landscapes, market conditions, and technological advancements alter the playing field. In contrast, industries like agriculture or real estate might have more stable risk profiles but are subject to sudden changes due to environmental factors or policy shifts. This variability across industries means that a one-size-fits-all approach to risk assessment is inadequate. Private equity firms must adopt flexible, industry-specific risk models that can adapt to the unique characteristics and evolving landscapes of the sectors they invest in, thus optimizing their AI capabilities.
The Power of AI in Enhancing Risk Management in Small Firms
AI technologies, particularly natural language processing (NLP) and machine learning algorithms, are important tools for private equity firms aiming to monitor and manage risks in small firms. These technologies can sift through vast amounts of data, extracting the valuable 10% and identifying patterns, trends, and subtle nuances in the language used to describe risks. By detecting these patterns, AI can reveal potential risks that might not be immediately apparent through traditional methods. This proactive approach to risk identification allows firms to address issues before they escalate, providing a more comprehensive and nuanced understanding of the risks facing small firms.
AI's ability to process unstructured data is particularly valuable in this context. Many of the risks that small firms face are discussed informally in places like social media, niche blogs, or local news outlets. Traditional risk management tools might overlook these sources, but AI-powered tools can analyze them in real-time, detecting emerging threats as they develop. Moreover, AI can cross-reference these insights with structured data from financial reports, regulatory filings, and other formal documents to create a holistic risk profile. This multidimensional analysis helps private equity firms not only identify risks but also understand their potential impact, enabling more informed, data-driven decision-making that enhances operational efficiency and competitive edge.
Beyond risk identification, AI also enhances risk mitigation strategies. By continuously monitoring data and learning from new information, AI systems can adapt to changing conditions, offering updated risk assessments that reflect the latest developments. This dynamic approach allows private equity firms to stay ahead of potential issues, making it possible to implement preventative measures rather than reacting to crises after they occur. In this way, AI capabilities contribute significantly to the optimization of risk management processes.
How SESAMm’s Advanced Technology Enhances Risk Assessment
SESAMm’s TextReveal® is at the forefront of this technological revolution, enabling private equity firms to efficiently navigate the vast digital landscape and extract the crucial information needed for informed decision-making. Through our proprietary data lake amounting to over 25 billion online articles with 15 years of historical data and our AI algorithms, TextReveal® can quickly identify and retrieve valuable insights, even when the information is deeply buried or highly specific. The tool's ability to analyze and understand the diverse language and terminology used in discussions about risks on the web empowers private equity firms to objectively assess the materiality of certain risks or identify emerging threats that have yet to be formally recognized.
TextReveal® goes beyond merely identifying risks—it categorizes them, providing context that helps private equity firms understand the severity and relevance of each risk. For example, if a small biotech firm is mentioned in discussions about regulatory hurdles, TextReveal® can determine whether these mentions are isolated incidents or part of a broader trend. It can also assess whether the language used suggests an imminent threat or a longer-term concern, enabling firms to prioritize their responses accordingly. Additionally, TextReveal® integrates sentiment analysis, which can gauge the overall tone of discussions surrounding a company, offering further actionable insights into potential reputational risks.
SESAMm has developed a proprietary metric – the Intensity Score, which calculates an event's relevance based on its news coverage and sentiment. It uses negative sentiment, article dispersion, and empirical ESG risk measures to determine how likely an article is to represent a high-risk controversy. The Intensity Score gives TextReveal users a clear understanding of which events require their attention.
Users can also opt to receive email alerts for the more severe controversies, ensuring they’re always aware of significant risks. In addition to the severity, controversies are also categorized by risk and sub–risk type, making it easy to analyze specific areas of concern.
Moreover, SESAMm's platform is designed to be intuitive and user-friendly, making it accessible to investment professionals who may not have a technical background. This ease of use ensures private equity firms can quickly incorporate AI-driven insights into their risk management processes without a steep learning curve. By streamlining the data analysis process, TextReveal® allows firms to focus on strategic decision-making, confident they have a comprehensive understanding of the risks and opportunities associated with their investments and portfolio companies. This level of operational efficiency and optimization is key to maintaining a competitive edge in the fast-paced world of private equity.
TextReveal’s Risk Assessment module enables deep company and thematic research in multiple languages through on-the-fly keyword searches. Users have full access to articles, sentiment analysis, and trending topics to get a complete understanding of the risks. We’ve even developed an AI Text Summary feature that provides a quick summary of a selected article, saving time and enabling a faster analysis.
In summary, the integration of AI tools and natural language processing technologies is transforming risk management in private equity, particularly for firms dealing with small, private companies. By leveraging these advanced tools, private equity firms can enhance their due diligence processes, better monitor risks and controversies, and ultimately make more informed investment decisions that lead to higher rewards and operational efficiency.
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. To learn more about how you can analyze web data or request a demo, contact one of our representatives.
PARIS, FRANCE, March 1, 2023 — SESAMm, a leader in natural language processing (NLP), a field of artificial intelligence, today announced the close of a Series B2 funding round of €35 million (USD 37 million) to accelerate its ambitious growth and global expansion plans.
Securing this funding will enable SESAMm to further expand into U.S. and Asian markets, support technology development to generate AI-powered ESG and sentiment analytics, and hire key talent across sustainability, technology, sales, and marketing.
The Series B2 round was co-led by Elaia, a deep tech VC firm, and Opera Tech Ventures, the venture capital arm of BNP Paribas (BNPP). Other participating companies include asset manager Unigestion, Raiffeisen Bank International’s (RBI) venture capital entity Elevator Ventures, AFG Partners, CEGEE Capital, and historical backers, including Carlyle (CG) and New Alpha Asset Management, who participated in the previous Series B1 round. This latest round brings the total funding raised to €50 million.
“We are delighted to support SESAMm's ambitious journey to leverage deep technology and create actionable data insights on ESG analysis and monitoring. We are impressed by the quality and seniority of the executive team, already leading operations in every continent with anchor and Tier 1 clients. The vision of bringing technology to the Finance industry and the corporate world to empower with ESG insights is a game changer, and we are very excited to be part of the adventure by their side,” said Pauline Roux, Partner at Elaia.
“In a context where it is increasingly critical for decision makers to feed their assessments with deeper data points, we found SESAMm’s product very relevant to help identify, filter out, weigh, and report key data insights on companies ranging from small private firms to larger corporations. We have found deep common ground with the team and are very proud to support SESAMm in its journey onward,” said Thibaut Schlaeppi, Managing Director at Opera Tech Ventures.
SESAMm is one of the fastest-growing natural language analytics data providers, serving global investment firms such as top private equity firms, hedge funds, and other asset management companies, in addition to corporations of all sizes. Leveraging its data lake, which comprises more than 20 billion articles and grows by 20% per year, SESAMm provides datasets and NLP capabilities to generate innovative analytics for use cases such as controversy detection, ESG and SDG sentiment scores, private equity due diligence, suppliers monitoring, and more.
“Since we started working with SESAMm as investors and clients over two years ago, we’ve been impressed with both the company’s growth and the advanced analytics that have supported our deal sourcing, diligence, and portfolio company value creation efforts,” said Matt Anderson, Carlyle’s Chief Digital Officer. “We are delighted to build on our strong partnership with SESAMm by participating in this latest round.”
CEO Sylvain Forté, COO Pierre Rinaldi, and CTO Florian Aubry co-founded SESAMm in 2014. With their team of nearly 100 data experts, they leverage the vast amount of textual information on the web, from news websites to NGO reports and social media, to translate it into powerful, digestible, and actionable insights.
Sylvain Forté, CEO and Co-Founder of SESAMm, said, “We are happy and grateful to close this €35 million Series B2 round, a testament to the dedication of our entire team and the strength of SESAMm’s C-suite leadership – my cofounders Pierre Rinaldi COO & Florian Aubry CTO, Marie-Charlotte Deucher CFO, Jorge Alvarez CMO, and Eric Sionnet CDO – to continue our growth journey and expand to new international markets such as Singapore. Raising a significant amount during challenging market conditions highlights the relevancy of SESAMm's focus on two key trends: AI and sustainability. In turn, these tools enable organizations to make better decisions and fill the data gaps, particularly in ESG, on both public and private companies.”
About SESAMm
SESAMm is a leading artificial intelligence company serving global investment firms and corporations around the globe. SESAMm analyzes more than 20 billion documents in real-time to generate insights for controversy detection on investments, clients and suppliers, ESG and positive impact scores, private equity due diligence and sourcing, sentiment analysis on financial assets, and more.
About Elaia
Elaia is a venture capital firm specializing in funding early-stage technology companies. The firm focuses on software, internet, and digital media startups and has a strong track record of backing companies that become successful and profitable. Elaia's team includes experienced investors and entrepreneurs who provide valuable guidance and support to the startups in their portfolio. With a focus on helping companies scale and grow, Elaia is a valuable partner for any entrepreneur looking to launch a technology business.
About Opera Tech Ventures by BNP Paribas
Opera Tech Ventures is BNP Paribas’ VC arm, launched in 2018 with the objective to invest in startups that transform or disrupt the financial industry. The fund is managed by BNP Paribas Asset Management France, as part of its Private Assets division, dedicated to private asset management. With a global scope, Opera Tech Ventures supports entrepreneurs building ambitious ventures from Series A to Series C with investments ranging from 3 to €15m.
About Carlyle
Carlyle (NASDAQ: CG) is a global investment firm with deep industry expertise that deploys private capital across three business segments: Global Private Equity, Global Credit, and Global Investment Solutions. With $373 billion of assets under management as of December 31, 2022, Carlyle’s purpose is to invest wisely and create value on behalf of its investors, portfolio companies, and the communities in which we live and invest. Carlyle employs more than 2,100 people in 29 offices across five continents. Further information is available at www.carlyle.com. Follow Carlyle on Twitter @OneCarlyle.
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