SESAMm is pleased to announce that Nathalie Wallace is joining our Advisory Board. Nathalie brings more than 20 years of experience at the intersection of investment management, sustainability strategy, and executive leadership. She has built a career helping global investment organizations integrate sustainability into investment decision-making and capital allocation.
Commenting on the appointment, Sylvain Forté, CEO of SESAMm, said, “Nathalie brings a rare combination of investment experience, strategic vision, and deep understanding of how sustainability considerations translate into real-world investment decisions. Her perspective will be invaluable as SESAMm continues to support financial institutions navigating increasingly complex risk and regulatory environments.”
She previously served as Chief Sustainability Officer at Edmond de Rothschild, where she contributed to the firm’s sustainability strategy across asset classes. Prior to that, Nathalie was Global Head of Sustainable Investment at Natixis Investment Managers, where she was a member of the executive, investment, and seed committees, chaired the CSR–Sustainable Investment committee, and served on the boards of Mirova and Ostrum Asset Management. Earlier in her career, she was Global Head of Strategy and Business Development at Mirova, supporting its growth and positioning as a leading sustainable investment platform.
As Senior Advisor to SESAMm, Nathalie will support the company’s strategic direction, bringing her perspective on sustainable finance, investor expectations, and the evolving role of data and AI in risk analysis and investment processes.
“SESAMm’s approach to risk and sustainability intelligence reflects how investment teams are evolving their processes,” said Nathalie Wallace. “I’m excited to contribute my perspective as the firm continues to support investors with timely, decision-relevant insights.”
We are delighted to welcome Nathalie to SESAMm and look forward to working together as we continue to support financial institutions with forward-looking risk and sustainability insights.
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 our newest research, "ESG Controversies: A Comparative Study of Public vs Private Sectors," our ESG and Research & Analytics teams present an exhaustive study on the nuances of ESG controversies across public and private sectors. We combined artificial intelligence with our extensive dataset of over 25 billion documents to extract ESG controversies in both sectors. This research highlights the increased visibility and scrutiny of public companies compared to the more discretion in private companies. A case study on IKEA uncovers challenges in product safety and human capital, underlining the importance of proactive sustainability practices. The study examines these sectors' alignment with major ESG frameworks, including the UN Global Compact and Sustainable Development Goals, offering invaluable insights for enhancing corporate ESG strategies.
Key takeaways:
Public companies are under constant observation, leading to higher exposure to ESG risks such as pollution, labor disputes, and governance failures. This visibility is partly due to regulatory requirements for transparency, making every aspect of their operations subject to public and investor scrutiny.
Private companies, while benefiting from less regulatory oversight, encounter substantial repercussions from ESG controversies. These can manifest as sudden shifts in investor confidence, challenges in securing financing, or damage to reputation, underscoring the critical need for comprehensive risk management approaches that encompass environmental, social, and governance factors.
The case study on IKEA provides an in-depth look at specific issues like product recalls due to safety concerns and the complexities of managing a global workforce. It highlights IKEA's efforts to implement forward-thinking sustainability initiatives and human capital management practices as key components of its corporate strategy, demonstrating the tangible benefits of such measures in mitigating ESG risks.
ESG controversies and breaches of SDG goals vary notably between public and private sectors. Public companies frequently encounter more visible and consistent ESG risks, while private companies, although subject to less scrutiny, experience significant impacts when controversies occur.
Dive deeper into ESG controversies and uncover strategies for navigating these challenges effectively. Download "ESG Controversies: A Comparative Study of Public vs Private Sectors" and equip your organization with the insights needed to enhance your ESG practices for a sustainable future. Fill out the form below to access your copy and lead the way in corporate sustainability.
In an era where information increases at an unprecedented pace, the necessity for intelligent and efficient methods to filter and analyze large datasets is more critical than ever. This need is particularly emphasized in the finance industry, where private equity firms and asset managers require real-time, AI-powered ESG monitoring to make informed investment decisions.
Harnessing the power of AI for ESG monitoring
As Tyler Cowan noted, even if one could read an article in a second, it would take a lifetime to consume the volume of data available. At SESAMm, we analyze over 20 billion records, representing 250 terabytes of dense information. The challenge is, how can professionals navigate this ocean of data in a reasonable timeframe to make critical decisions? Natural language processing and AI-powered techniques provide the solution. These technologies enable us to comprehend and navigate a multitude of documents, from newspapers to niche blogs, in mere seconds.
The need for AI-powered ESG monitoring
For private equity firms and asset managers, AI-powered ESG monitoring is not just a trendy concept but a necessity. Identifying potential ESG controversies and understanding the impact of various ESG factors on investment portfolios is crucial for risk management and investment strategies. At SESAMm, our approach is similar to a "machete, then sandpaper" method. We first eliminate the unnecessary information and then gradually refine the data. We construct a knowledge graph that includes a broad range of entities, from companies and executives to brands and products. By employing custom indices and advanced algorithms, we focus on the most relevant data points. And in the last year, generative AI has been helping us to refine this process even further, achieving a high level of accuracy in our results.
Leveraging AI for ESG insights
Using AI and algorithms like DistilBERT and the Universal Sentence Encoder allows us to process vast amounts of information swiftly. By utilizing a hybrid model that combines on-premises servers with cloud-based solutions, we ensure speed without compromising cost-efficiency. Our specific workflows for identifying ESG controversies leverage this technological prowess. We understand the importance of not sending our clients on wild goose chases with false positives. Our AI-powered ESG monitoring system is designed to identify only the most relevant and likely material risks. This approach saves time and ensures our clients have the insights they need without being overwhelmed.
From vast data to actionable insights
Our journey begins with over 20 billion records, but the destination is concise, actionable insights tailored to your industry and needs. We focus on what truly matters, employing AI, NLP, and strategic data processing techniques to transform a deluge of information into a manageable stream. For private equity and asset managers, our AI-powered ESG monitoring provides the critical insights needed to make informed decisions. By prioritizing precision and reducing noise, we ensure that the information we present is not just accurate but also relevant.
SESAMm's approach
The age of information has called for intelligent, systematic detection of ESG controversies. Through AI-powered ESG monitoring and careful consideration of unique requirements, SESAMm delivers unparalleled insights tailored to the world of finance. If your firm is engaged in private equity or asset management and is keen on leveraging data to identify potential ESG risks and controversies, SESAMm's offerings are designed to meet your exact needs.
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.
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.
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