SESAMm Incorporates Generative AI to Enhance ESG Risk Mitigation and Process Efficiency in the Finance Sector
July 12, 2023
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5 mins read
SESAMm Incorporates Generative AI to Enhance ESG Risk Mitigation and Process Efficiency in the Finance Sector
FOR IMMEDIATE RELEASE
PARIS, France - July 12, 2023 - SESAMm, a leading player in financial technology, announces a transformative initiative to incorporate Generative AI solutions into its operations and product offerings. This strategic move is geared towards assisting financial firms in enhancing risk mitigation focused on ESG controversies and streamlining their processes.
The implementation of Generative AI follows a three-pronged strategic approach. This comprises the integration of large language models into their tech stack, the development of a client-facing conversational agent, and fostering a culture of AI utilization across all teams.
"With Generative AI, we are not only enhancing our internal processes but also focusing on the development of new features that redefine industry standards," stated Sylvain Forté, CEO & Co-founder of SESAMm. "These include intuitive dashboards, automated ESG/SDG event analysis tools, and a client interaction chatbot - all created to streamline data interaction and boost efficiency in risk management."
The integration of Generative AI has significantly enhanced SESAMm's product functionality already. This includes quicker and more intuitive interaction with data and introducing new features, such as ESG/SDG event summarization and automatic competitor searches for public and private companies.
SESAMm is also employing Generative AI for advanced risk mitigation. "Our innovative approach provides our clients a virtual team of ESG analysts and experts for detecting risk and ESG controversies, enhancing their risk mitigation strategies in a robust and comprehensive manner," Forté added.
SESAMm is preparing to launch a suite of AI-powered features later this year. "These new features, powered by Generative AI, reinforce our commitment to developing solutions that enhance risk mitigation and streamline processes for financial firms," Forté emphasized.
To explore more about SESAMm's Generative AI solutions and how they can boost your firm's operations, watch the video below:
Also, make sure you join our upcoming webinar, where Sylvain Forté will discuss live the future of fintech with Generative AI and how SESAMm is incorporating Generative AI into its processes and products. To register for the webinar, click here.
About SESAMm
SESAMm is a leading artificial intelligence and NLP (natural language processing) technology company serving global investment firms, corporations, and investors, such as asset managers, banks, private equity firms, hedge funds, and index providers. With over 100 employees and six offices worldwide, SESAMm celebrated its 9th anniversary in 2023.
As 2024 comes to a close, I’m proud to reflect on SESAMm’s achievements and energized by the opportunities that lie ahead. This year has been a milestone for our growth, partnerships, and technological advancements, setting a strong foundation to tackle the challenges and embrace the possibilities of 2025.
Looking Back on 2024: Key Achievements
Strengthening Client Partnerships and Expanding Our Reach
This year, SESAMm welcomed an impressive roster of new clients, in particular working more closely with LPs such as Swen Capital, banks, and asset managers such as Natixis, alongside numerous mid-market asset managers and private equity funds. These organizations are turning to SESAMm for more control over their ESG data and access to granular controversy insights, reaffirming our role as a trusted partner in sustainable finance. We also launched impactful partnerships with Ramboll, ARX, FinGreen, and CybelAngel, among others, broadening our reach and capabilities.
Building a Stronger Team and Advancing Our Technology
Internally, we strengthened our team with strategic hires, including our first team member in Canada, to better support our clients locally. On the technology front, we achieved significant milestones: introducing new platform features, launching a comprehensive product documentation help page, and reaching the capacity to process nearly 30 billion documents—our largest scale yet.
Adapting to a Dynamic ESG Landscape
Globally, the ESG landscape was marked by notable developments. Europe focused heavily on CSRD compliance, while Asia advanced new ESG mandates and regulations in South Korea, Japan, and Singapore. Despite regulatory shifts in the U.S., SESAMm experienced strong growth in North America, demonstrating our ability to adapt and thrive globally.
Innovating with Generative AI
This year also saw the integration of generative AI into our solutions, reshaping how we deliver value to clients. Risk Reveal, for example, enables automated controversy report generation and real-time insights.
Looking Ahead to 2025: Rising to ESG Challenges
Embracing ESG Challenges
As we close out 2024, the momentum in ESG shows no signs of slowing down. With new regulations like CS3D and evolving global frameworks, companies face mounting demands to monitor not only their investments but also their supply chains while improving transparency across the board. SESAMm remains committed to enhancing its tools to meet these challenges, delivering faster, more actionable insights to corporate and investment clients alike.
Harnessing the Potential of AI
The evolution of AI presents a major opportunity. Advances in generative models will enable us to further increase the scale and quality of our data processing. Our focus will remain on refining interpretation and reporting capabilities, empowering clients to make smarter, data-driven decisions on millions of companies with minimal friction.
The year ahead will undoubtedly bring its share of challenges, but it also holds incredible potential for progress. SESAMm is committed to remaining at the forefront of ESG and AI innovation, helping businesses not only adapt to change but lead it. None of this progress would be possible without the trust and collaboration of our clients, partners, and team members. Thank you for making this year a success. Together, we are shaping the future of finance and sustainability. Here’s to another year of growth, innovation, and positive impact in 2025!
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.
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.
Figure 1: Inflation co-mentions by category and percentage.
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
Figure 2: Used vehicles co-mentions increase percentage-wise.
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).
Figure 3: Used-car co-mentions begin in early 2021 and exceed those for new cars.
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
Figure 4: In 2020, inventory spikes as production and sales plummet, affecting inflation.
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.
Figure 5: The pandemic and new-vehicle supply shortage became bigger reasons for consumers to seek out used cars over cost.
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).
Figure 6: General Motors, Ford, and Toyota suffered pandemic-related shortages the most based on co-mentions.
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.
Held from June 21–29, London Climate Action Week (LCAW) 2025 brought together over 45,000 participants across 700+ events, emphasizing London’s role as a global hub for climate finance and leadership. As geopolitical uncertainty clouds climate ambitions, this year’s event signaled a broader market pivot: investors are now prioritizing regions with regulatory clarity and policy momentum, namely Europe and Asia.
He also outlined plans for new corporate sustainability reporting standards, a move intended to improve transparency, build investor confidence, and ensure alignment with the UK's net-zero targets. These commitments were part of the UK’s post-Brexit green industrial strategy, distinguishing it from recent ESG policy slowdowns in Brussels and Washington.
Climate Finance and Market Confidence
One of the most prominent themes throughout the week was capital mobilization. At the “Finance Live” forum, asset managers, banks, and insurers debated how to align their portfolios with net-zero goals while navigating geopolitical instability and rising greenwashing scrutiny. Key discussions included scaling blended finance vehicles, investing in transition technologies, and strengthening ESG data governance.
Meanwhile, sessions like the Nature Hub spotlighted biodiversity and natural capital, moving beyond carbon to more holistic definitions of environmental value. This reflects a growing consensus that an effective climate strategy must include nature-based solutions and ecosystem restoration.
The Broader Message: A Shift in Global Climate Leadership
While the U.S. backtracks on core climate regulations, London and Europe are entering a leadership void. For global investors, that means that developing a climate strategy now includes not only where to invest but also where to trust. In that context, LCAW 2025 offered both policy and finance updates and a credibility reset.
The takeaway is clear: in an age of fragmented regulation and climate politicization, market trust flows towards stability. London Climate Action Week didn’t just reflect that shift; it helped define it.
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