ESG Assessment: LVMH

Controversial business involvement screening is moving beyond its origins as a compliance exercise.

Under frameworks like SFDR and the EU Taxonomy, investors must prove that their portfolios not only promote sustainability but also exclude activities fundamentally at odds with environmental, social, or ethical principles. This marks a shift from static disclosure toward dynamic accountability, and it has broadened both the scope and ambition of ESG screening.

Historically, exclusions focused on a narrow range of activities - weapons, tobacco, or fossil fuels - and primarily applied to public equities. Today, that universe has expanded dramatically. Private markets, secondaries portfolios, and private credit exposures are now expected to undergo the same scrutiny as listed assets. This reflects not only regulatory alignment but also diversifying investor expectations, as institutions incorporate reputational, cultural, and mission-based constraints into their investment frameworks.

Modern exclusion policies increasingly include areas not yet covered by regulation but relevant to ethics, faith, or social impact. Examples range from pork-related activities in Sharia-compliant portfolios to emerging debates over cryptocurrency mining and trading, and even biotechnology topics such as human cloning or genetic manipulation that raise profound ethical questions. These additions illustrate how business involvement screening is evolving from a rule-based checklist into a reflection of each investor’s worldview and stakeholder commitments.

This evolution, however, brings complexity. Private assets and novel sectors often lack standardized data or public disclosures. ESG, compliance, and deal teams must process incomplete information, document decisions, and adapt quickly to new mandates - all without expanding headcount. The result is a growing need for automation that can adapt to human nuance.

SESAMm’s AI-powered business involvement screening meets that need. By allowing investors to screen based on their own exclusion categories and thresholds, it translates varied mandates - from regulatory to reputational - into a single, automated process.

Automating Controversial Business Involvement Screening in Public and Private Assets

SESAMm’s platform uses a new AI agent approach that scans and analyzes vast amounts of information. Below, we provide an overview of SESAMm’s business involvement screening capabilities and how they address investors’ needs for automation, thresholding, and flexible outputs.

Comprehensive Coverage through Big Data

SESAMm utilizes its AI engine to monitor over 30 billion articles and 10 million new documents daily from various sources, including news sites and NGOs. This extensive data collection spans multiple languages and local outlets, enabling it to detect obscure references to companies and raise alerts for issues such as misconduct. SESAMm's coverage encompasses millions of public and private companies, enabling users to conduct thorough screenings of any entity, including private companies and subsidiaries.

Customizable Exclusion Frameworks

SESAMm’s business involvement screening gives investors control over what to screen and how to classify it. Users can request customization of exclusion categories to mirror their own policy, whether based on regulation (e.g., SFDR, EU Taxonomy) or internal mandates (e.g., faith-based or reputational constraints). In addition to standard ESG categories like fossil fuels or weapons, investors can add custom topics. This flexibility allows ESG, compliance, and secondaries teams to tailor the tool to their precise needs,.

Threshold-Based Classification

SESAMm’s business involvement screening module is built around the concept of threshold-based flags. The AI utilizes structured data and unstructured signals to determine involvement levels. The output for each company is a clear classification: No Involvement, Limited Involvement, or Significant Involvement for each category. These classifications correspond to thresholds – limited might mean some involvement but below the exclusion threshold, significant means above the threshold or its a core business. By encoding the thresholds in the system, SESAMm ensures consistency with the investor’s policy. This is crucial for automation: rather than an analyst manually checking revenue percentages and news, the system does it automatically and provides clear justification.

Rapid Portfolio Screening Process

The system is designed for fast, self-contained screening. A user simply uploads a list or portfolio, and within hours receives a complete file summarizing involvement across all exclusion categories. The output includes company-level classifications, summaries of supporting evidence, and references to sources. This enables investors to integrate the results directly into due diligence workflows, risk committees, or regulatory reporting, with no ongoing manual data maintenance required.

Cost and Resource Efficiency

Automating this process saves substantial analyst time, particularly for rating agencies and secondaries investors managing high volumes of entities. Rating agencies can use the pre-classified results as a baseline input for their own ESG or credit assessments, reducing the manual data-gathering burden. LPs and GPs can run large private company universes in-house without additional research teams. In secondaries, where a full portfolio review can take days of analyst effort, SESAMm’s workflow compresses that timeline to just a few hours, enabling ESG validation to fit seamlessly into transaction schedules.

Auditability and Verification

Each classification is fully transparent. Analysts can drill down into the evidence behind a flag, including links to original articles, filings, or corporate statements, and verify the AI’s reasoning. Automatic translation ensures accessibility across languages. This transparency builds trust in the results and provides auditable documentation for LP reporting or regulator reviews.

As ESG investing matures, the leaders will be those who can implement exclusions transparently, efficiently, and in alignment with evolving norms. The next frontier is no longer just regulatory compliance - it is the ability to anticipate what clients and society will expect tomorrow, and to operationalize those expectations across all asset classes. SESAMm’s technology makes that possible: a platform that keeps pace with both policy evolution and moral expectations, bringing consistency and clarity to an increasingly complex ESG landscape.

The educational technology (EdTech) sector, including companies like Udemy, Coursera, and Byju's, has grown rapidly but faces major social and governance challenges. Key issues include costly legal battles over privacy violations and deceptive practices, which raise investor concerns. Financial instability is also prevalent, with layoffs and bankruptcies highlighting doubts about workforce rights and sustainable business models. While some companies manage ESG issues better, operational inefficiencies remain a concern for long-term stability.
What are the most pressing ESG challenges currently facing the EdTech sector? Read to find out.

BYJU’S: Treading Turbulent Waters Amidst Financial and Regulatory Challenges

Byju’s, an EdTech startup in India, has faced significant challenges since 2022. The company has dealt with controversies, including data breaches, financial mismanagement, and regulatory investigations. Key issues include the shutdown of its Kerala office, mass layoffs, allegations of Goods and Services Tax evasion, and defaults on loan payments. In 2023 and 2024, Byju's continued to struggle with layoffs, money laundering investigations, and the resignation of executives. As of 2024, the company is at risk of insolvency, with ongoing bankruptcy proceedings and regulatory action against its leadership, highlighting its financial instability after years of rapid growth.

Key Controversies:

• Congress MP Karti Chidambaram urges SFIO to probe Byju's finances
• Byju's shuts down Kerala office, forces employees to resign
• India Raids Byju’s Offices in Money Laundering Probe
• Byju's Lays Off Over 1,000 Employees, 2nd Mass Job Cut In A Year
• Byju's US Unit Files For Chapter 11 Bankruptcy Amid Financial Turmoil

Udemy: Battling Legal and Ethical Challenges in the E-Learning Landscape

Udemy, a leading e-learning platform, has encountered increasing legal, financial, and operational challenges. These include a settlement over deceptive pricing practices, antitrust issues leading to a board member's resignation, and multiple lawsuits related to Facebook data sharing and video privacy violations. The company has also restructured its workforce to streamline operations and faced concerns over its generative AI policy regarding consent and transparency among instructors. These controversies highlight the complexities faced by EdTech companies in navigating legal and ethical challenges.

Key Controversies:

• Udemy Agrees to Pay $4 Million Settlement Over Deceptive Pricing Practices
• Udemy Board Member Resigns Due to Antitrust Concerns by Justice Department
• Udemy Faces Lawsuits Over Facebook Data Sharing
• Udemy class action over violations of the Video Privacy Protection Act
• Generative AI policy at Udemy raises consent and transparency concerns
• Massive E-Learning Platform Udemy Gave Teachers a Gen AI 'Opt-Out Window'. It's Already Over.

Coursera: Navigating Challenges in Security, Ethics, and Workforce Dynamics

Coursera, another widely used online learning platform, has faced relatively few controversies as reflected by its Controversy Exposure Score. However, a few noteworthy challenges include security vulnerabilities, AI use, an investigation into misleading financial statements, and workforce reductions due to declining growth. The company is dealing with a class action lawsuit over subscription renewals and a data privacy lawsuit for sharing customer viewing history with Meta. Coursera also faces controversies related to AI, in this case on AI-powered tools for grading, providing feedback, and course-building, promising greater efficiency but raising questions about transparency and ethics. Finally, as part of restructuring, Coursera plans to cut its global workforce.

Key Controversies:

• Investigation for potential violations of securities laws related to misleading financial statements
• Coursera reducing workforce to counter declining growth
• Coursera Hit With Class Action Over Subscription Renewals
• Coursera data privacy class action lawsuit
• Coursera Faces Video Privacy Class Action Lawsuits

Conclusion

Each of these companies illustrates the growing ESG challenges in the EdTech sector. From maintaining data privacy and ethical AI use to ensuring sound governance, financial sustainability, and fair treatment of employees and customers, Udemy, Coursera, and BYJU’S all face heightened scrutiny. Recent controversies and operational troubles highlight the need for robust risk management as education technology firms mature on the global stage. The ESG risks – whether legal, social, or governance-related – not only threaten their reputations but also have material impacts on their long-term viability. By addressing these issues transparently and proactively, EdTech companies can work toward a more sustainable and responsible growth trajectory in the years ahead.

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 to request a demo, reach out to one of our representatives.

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.