On October 1st, SESAMm hosted its second annual “SESAMm Day” in Paris at the EY Impact Lab. The evening kicked off with the Paris 2043 immersive experience, an eye-opening scenario of Paris in 2043 should climate commitments fail. Designed to spark forward-looking discussions, the experience set the stage for a full evening of insight, exchange, and networking among peers across private equity, asset management, banking, and consulting.

The event also featured a dynamic 45-minute panel discussion moderated by Sylvain Forté, CEO of SESAMm, with three distinguished panelists:

• Dr. Julia Haake, Head of ESG Rating Agency at EthiFinance
• Elsa Couteaud, RSE Director at Praemia
• Abigail Arellano Sanchez, Sustainability Project Manager and Data Specialist at Natixis Investment Managers

From Data to Decisions

The conversation opened with a critical challenge facing the industry: transforming abundant ESG data into actionable insights. The panelists discussed their approaches to filtering signal from noise, focusing on how controversy alerts, automated reports, and ESG scores inform actual investment, financing, and rating decisions. The key, they emphasized, lies in establishing clear hierarchies and methodologies to prevent information overload.

Addressing ESG Skepticism

The panel also addressed the growing concerns around greenwashing, regulatory complexity, and "ESG fatigue." An interesting linguistic shift emerged during the discussion. One panelist noted that the term “sustainability” is increasingly preferred over "ESG" in job titles. In contrast, another panelist pointed out that 10-15 years ago, the trend was reversed, as the industry moved from sustainability to ESG.

While European skepticism focuses less on ESG fundamentals and more on complexity and costs, particularly with regulations like Omnibus, the panelists acknowledged growing operational fatigue. For example, the speakers highlighted that teams are demanding more pragmatism and concrete action over the burden of reporting.

The Future of ESG Data and Themes

Looking forward, the panel identified several emerging priorities:

Climate adaptation is taking center stage, with physical risks such as heatwaves and flooding becoming increasingly impossible to ignore. The future of ESG data will be increasingly forward-looking and predictive.

New themes are reshaping the ESG landscape. Biodiversity remains a work in progress requiring significant development. Responsible AI has emerged as both an ESG theme and a transformative force for the industry itself. Even defense has become a consideration as an exclusion or inclusion criterion, raising questions about which other exclusion themes might emerge or fade.

Supply chain risks are gaining prominence, particularly in emerging markets where local taxonomies and data remain scarce. One panelist shared that accessing reliable data on emissions and physical risks in these regions is challenging, with insurance data often providing the most qualified information.

A Call to Action

The panel concluded with a pragmatic vision: rather than being overwhelmed by ever-increasing data, the focus should shift toward enhancing real-world impact and climate resilience. With reputational and financial risks mounting, the message is clear: it's time to move from reporting to action.

Closing Reflections

SESAMm Day 2025 closed on a high note, with participants continuing the conversation over networking drinks. We extend our warm thanks to everyone who joined us, and in particular to our panelists for sharing their perspectives and making the evening both insightful and engaging.

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 a recent interview with Climate Action, Maha Chihaoui, ESG Analyst at SESAMm, discussed how SESAMm’s AI-powered solutions are reshaping ESG analysis. Maha, who leads ESG research and methodology development at SESAMm, outlined how the company addresses the challenges of self-reported ESG data, which can be inconsistent, biased, and outdated.Discover Maha’s take on how AI-driven insights and risk detection transform ESG analysis below.

1. Many ESG datasets rely on company self-reporting. What are the main limitations of that approach, and how does AI help address them?

Self-reported ESG data can be incomplete, inconsistent, or subject to bias, as companies may selectively disclose positive information while downplaying or omitting negative impacts. This lack of standardization also makes it difficult to compare ESG performance across different firms or industries. Additionally, self-reporting often lags behind real-time events, reducing the timeliness and relevance of the data.

At SESAMm, we take a complementary, “outside-in” approach using AI. Our state-of-the-art AI algorithms analyze millions of public documents every day, including news articles, NGO reports, legal filings, and more, to detect ESG-related controversies and risks. This allows us to surface controversies in near real-time, helping investors get a more accurate and timely picture of actual behavior.

2. One of SESAMm’s latest innovations is real-time UNGC violation screening. Why is the UN Global Compact such a critical framework for investors and corporates today?

The UN Global Compact (UNGC) holds critical importance for investors because it carries strong global credibility as a United Nations–endorsed initiative, signaling alignment with universally accepted norms that enhance corporate reputation and stakeholder trust.

The framework provides holistic ESG guidance across key areas—human rights, fair labor practices, environmental sustainability, and anti-corruption—enabling companies to manage risks and opportunities comprehensively. By committing to UNGC principles, companies proactively mitigate legal, operational, and reputational risks associated with violations in these areas.

For investors, especially those subject to SFDR, the UNGC is directly linked to regulatory obligations. PAI indicator #10 specifically asks whether a company has violated the principles of the UNGC or other international norms. Our tool is built on a clear and concise methodology that enables thorough screening, and with the support of advanced AI models, it makes the assessment faster, more consistent, and scalable—efficiently identifying violations or risks of violating the UN Global Compact principles across thousands of companies, thereby supporting both compliance and active risk management.

3. How does SESAMm's AI-driven UNGC screening work in practice?

The SESAMm's AI-driven UNGC screening identifies and classifies ESG controversy events based on their potential breaches of the UN Global Compact Principles into three risk levels:

• Violator (clear and severe breaches),
• Watchlist (possible but unconfirmed violations),
• Low Risk (concerns without clear evidence).
  

These risk statuses are dynamic, reflecting changes in a company’s behavior over time. The system emphasizes transparency by providing detailed explanations and audit trails for each event, enabling clients to investigate further rather than relying on opaque “black box” results. Ultimately, event-level flags can be aggregated to guide company-level decisions, such as exclusions from investment universes.

Clients can filter and explore these events within our dashboards or receive alerts and reports as part of their risk monitoring workflows. What makes this unique is the combination of speed, granularity, and global scale—we’re able to capture and classify relevant controversies days or even weeks before they appear in traditional ESG data sets.

4. Based on your experience, how are investors using real-time controversy data in their decision-making processes?

We’re seeing investors use real-time controversy data in several key areas. During due diligence, it helps identify hidden risks in acquisition targets or portfolio companies, especially in private markets where traditional ESG data is sparse. For ongoing monitoring, firms use our alerts to track emerging controversies that may affect their holdings or counterparties, from suppliers to borrowers.
We also see it integrated into ESG scoring models, exclusion lists, and engagement strategies. In some cases, controversy data prompts further investigation or direct conversations with company management. It enables investors to act sooner and with greater confidence—before a risk becomes reputational or regulatory damage.

5. SESAMm recently launched new AI ESG Assessment Reports. How do these differ from traditional ESG ratings?

Traditional ESG ratings are often backward-looking and based largely on disclosed information. Our AI ESG Assessment Reports take a different approach—they’re built entirely on public data analyzed by AI in near real-time. The reports cover company-level ESG controversies, regulatory and industry pressures, sanctions screening, and more.
What makes them powerful is the speed and coverage. Users can generate a detailed ESG report on any public or private company—globally—in under 30 minutes. That includes small or mid-cap firms that may not be covered by major rating providers. It’s an accessible, scalable solution for firms that need faster, more flexible ESG insights in today’s fast-moving environment.

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.

Inflation.

It's a word that most of us in the U.S. despise, almost as much as the word taxes. It's probably because, like taxes, we can't escape its wallet-draining effect when it increases. Maybe the way we feel about it is because the last time the U.S. economy deflated—giving us relief from it—was in the 1930s, when "Prices dropped an average of nearly 7% every year between the years of 1930 and 1933," according to Investopedia. But I digress.

We won’t go into how inflation works, but how the government calculates it—and how its categories affect it—has always been consistent. At least it was until the COVID-19 pandemic hit, that is.

What NLP text mining reveals about the U.S. economy inflation-rate factors and the online conversations about them

To ensure we're on the same page about how we came to the forthcoming information in this use case, let's cover a couple of basics on NLP text mining and inflation rate indexes.

What are NLP and text mining?

Natural language processing (NLP), an A.I. technology, automates the data analysis of mined textual, unstructured data. It includes natural language understanding and natural language generation to simulate a human’s ability to create language, and it’s a component of text mining that performs a special kind of linguistic analysis by deep learning algorithms so a machine can “read” text. Apps like Grammarly or Wordtune analyze text to improve a written text, for example, and chatbots use this technology to interact with customers.
Text mining, or text analytics, is the process of examining big data document collections. It’s a computer science discipline that converts unstructured text data in documents and databases into normalized, structured data and datasets for analysis by machine learning models. Deep learning machine-learning algorithms then analyze this data, analyzing semantics and grammatical structures, to gain new insight or aid research from human language.
Together, NLP and text mining are like a search engine on steroids.

The Consumer Price Index (CPI)

According to this Forbes Advisor article, "The two most frequently cited indexes that calculate the inflation rate in the U.S. are the Consumer Price Index (CPI) and the Personal Consumption Expenditures Price Index (PCE)." For this article, however, we'll only use the Bureau of Labor Statistics (BLS) method of CPI inflation calculation as a reference.
CPI observes a specific group of commonly-purchased goods and services to gauge how prices fluctuate. These foods and services include:

• Apparel: Women's and men's clothes, jewelry, etc.
• Alcoholic beverages: Beers, wine, liquor, etc.
• Energy and commodities: Gasoline, natural gas, electricity, etc.
• Food: Items bought by the average consumer, such as breakfast cereal, milk, meat, fruits, vegetables, etc.
• Housing and shelter: Rent, housing insurance, bedroom furniture, hotel or motel accommodation costs, etc.
• Medical care services: Physicians' services, prescription drugs, medical supplies, etc.
• New and used vehicles: Trucks, vans, sedans, SUVs, etc.
• Tobacco and smoking products: Tobacco-related items, such as cigarettes, cigars, bidis, kreteks, loose tobacco, etc.
• Transportation services: Airline fares, vehicle insurance, etc.

NLP text-mining process: web mentions matched to CPI categories

Using SESAMm's web text analysis engine TextReveal®, we analyzed textual data relating to the inflation topic within the U.S. from 2017 until now. For this analysis, we defined co-mentions as the articles and social media posts that mention "inflation" and at least one of the CPI categories. Note: Although we can analyze more than 100 languages, we focused on English in this case. Also, we didn’t conduct a sentiment analysis from the information extraction.

From 2017 to 2019, inflation co-mentions within the U.S. are relatively stable (see Figure 1). But this trend changes with the first shift in 2020, continuing its rapid growth and peak by the end of 2021 due to this surge of inflation reaching record levels.

What was one of the main drivers of the inflation surge? Used cars.

3 used-car and inflation trends uncovered through NLP Text Mining

According to the U.S. Bureau of Labor Statistics, the cost of used vehicles was one of the main drivers of the inflation spike. How did used cars contribute to inflation? The chain of events occurred like so: The increased used-car demand was fueled by a new-vehicle supply shortage caused by a chip shortage generated by supply-chain interruptions due to the COVID-19 pandemic.

As the pandemic-induced supply-chain interruption unfolded, used-car trends developed. Here are three we found in our data mining research:

Trend 1: Co-mentions percentage for used vehicles more than doubled

Based on the percentage of co-mentions compared to other topics, the used-car topic moves from the number eight spot to the number four spot in 2021 (see Figure 2).

Before 2020, mentions were relatively steady. However, we observe an increase in used-vehicles mentions caused by disruptions in supply chains leading to chip shortages (see Figure 3) as early as January 2020. These shortages led to a decrease in new vehicle inventory. The Statista report, indicating an increase of the used vehicle value index by 49 points compared to the price index recorded in 2020, supports our findings.

Trend 2: Used vehicle prices rose with used-car co-mentions

Because of the pandemic, car production nearly stopped along with the sale of cars, which created two situations: 1. high inventory to sales ratio and 2. historically low car production (see Figure 4). Vehicles sales picked up later, but car production was still suffering because of supply-chain disruption. That meant the inventory to sales ratio dropped to virtually zero.

So consumers with little-to-no options for new vehicles turned to used cars, increasing their demand and therefore increasing their prices. We confirm this hypothesis with increasing mentions within the used-vehicles topic, coinciding with an inventory volume decrease. All in all, used-vehicle prices rose 40.5%.

Trend 3: The COVID-19 pandemic and new vehicle inventory shortage increased demand

A smaller new-vehicle inventory wasn't the only reason consumers sought out used vehicles. They also wanted used cars because of the pandemic.

For 2020, we observe that consumers avoided public transportation by rising co-mentions between pandemic-related mentions and the demand for secondhand vehicles (see Figure 5).

Used-car and inflation trends summary

We can summarize the used-car and inflation trends with one phrase: It's a used-car seller's market. For example, online retailers like Carvana have leveraged these factors to grow significantly. In contrast, due mainly to significant supply chain disruptions, motor companies have had the opposite effect, with the Automotive industry projected to lose $210 Billion. Judging by the number of mentions in public web forums and social media, the chip shortage and used-car boom affected General Motors, Ford, and Toyota the most (see Figure 6).

About SESAMm and TextReveal’s® NLP Text-mining Capabilities

SESAMm is a leading company in alternative data and artificial intelligence, delivering global investment firms and corporations descriptive, prescriptive, or predictive investment analytics worldwide. TextReveal is SESAMm's premiere NLP text-mining product, a solution that allows you to fully leverage NLP-driven insights and receive high-quality results through data streams, modular API and dashboard visualization, and signals and alerts. In other words, we organize, categorize, and capture relevant information from raw data for you.

READ MORE: Check out "Alternative Data Trends – How Reddit Helped Fuel The Great Resignation."

Ready to uncover the invisible data about your investments and target companies? Request a TextReveal demo today.