Forced labor is often assumed to be a problem of distant supply chains. The case of Packers Sanitation Services Inc. (PSSI) dismantles that assumption entirely.
Forced labor is often assumed to be a problem of distant supply chains. The case of Packers Sanitation Services Inc. (PSSI) dismantles that assumption entirely.
PSSI was a leading U.S. industrial cleaning contractor, servicing major meatpacking plants and backed by a top-tier private equity firm. Yet between 2022 and 2024, it became the center of one of the most significant child labor scandals in the U.S., one that had been quietly signaling its risks for years. SESAMm's controversy monitoring platform captured those early signals long before regulators intervened.
The Scandal
In November 2022, the U.S. Department of Labor discovered that PSSI had employed minors as young as 13 in hazardous overnight roles across 13 locations in 8 states. A federal investigation confirmed 102 children had been illegally employed, many handling dangerous chemicals and machinery. Three years earlier, in 2019, PSSI had already been sued for wage violations. The signal was there. It went unheeded.
The Fallout
The consequences were swift. A $1.5 million DOL fine. Contract terminations by Cargill and JBS. A DHS trafficking investigation. A replaced CEO. By late 2024, PSSI had shut its corporate office entirely. Even the private equity owner, Blackstone, faced direct scrutiny from pension funds, a reminder that labor violations travel up the ownership chain.
The Lesson
Every warning sign in this case was publicly visible before the crisis broke out. Wage lawsuits, labor complaints, and media coverage are all available in the public domain. Real-time controversy monitoring can surface these signals early, giving companies and investors the chance to act before exposure becomes unavoidable.
Forced labor is not only a humanitarian crisis. It is a material risk that demands better data, earlier detection, and stronger accountability.
Download the full case study infographic to see the complete timeline of events and key takeaways
The AI field is growing, and whether good or bad, people are doing more than talking about it; they’re using it more than ever. However, despite this increased use, I’ve noticed that, for some, their perception tends to alternate between false and too-high expectations of AI.
One case, in particular, was in 2021, Gartner placed natural language processing (NLP) at the top of its list of loaded expectations in terms of the Gartner hype cycle. As a result, many expected a potential “winter of AI,” so to speak. Yet, in 2022, we discovered the potential that we haven’t even touched on the true value AI could deliver.
Will there be a “winter of AI,” and are expectations bloated?
No, I don’t think so. As the past year has shown us, AI still has more to offer, a pocket of value that we have yet to see. I believe that while many people now accept that AI will be a transformative force—thanks to the fast democratization of large language models—our society hasn’t yet fully considered the actual changes it will make by lowering the barrier to access intelligence globally.
Progress in image generation, analysis, and computer vision—think autonomous driving—has leaped and bounded in the past year, and so has the progress in NLP, particularly in thenatural language understanding (NLU) and natural language generation (NLG) aspects. We’re at a tipping point that will likely transform our world in the same way that the internet has.
Tipping point for AI
Today, we’re seeing the development of natural language processing through large language models, such as with the emergence of ChatGPT based on OpenAI’s large language model version GPT-3.
Astounding fact: ChatGPT’s growth in user adoption skyrocketed past one million users within a week of launching. In comparison, no other tech company has reached this feat in this short of a time frame. But the adoption rate is only part of it.
This advance has profoundly affected creative jobs because this might be the first time an AI generative system can create high-quality content. In public mode, users have tapped ChatGPT to do everything, from generating basic reports and ideas to writing lectures and producing code.
With a high adoption rate comes great opportunity. Any startup seeing this level of success could become the most funded project ever. And more, there’s revenue. OpenAI, as the example, could make one billion dollars by 2024, according to a report via Reuters.
On the other side of the same coin, however, there are greater risks due to AI generative system advancement. For example, with AI assistance, human hackers can develop more sophisticated phishing campaigns—hacking mechanisms based on social engineering.
This image was generated with the assistance of DALL-E 2 by OpenAI with the prompt: An oil painting in classical style of an artificial intelligence holding the whole world in its hand. Realistic.
Competition, specificity, and focus for AI advancement
Despite the risks, we still haven’t seen what’s yet to come with generative AI. GPT-4, for instance, is rumored to launch in 2023. I believe it will be a massive improvement over GPT-3, which is already mind-blowing.
And on the point of NLG and these large language models, there’s a lot that’s feasible in process automation. For context, creative content gets the most attention; it’s the area that makes more headlines. But I would also watch advancements in technical content and automated code generation, for example.
Process automation
Because of today’s AI advancements, it’s now possible for tools like ChatGPT to generate near-ready-to-use source code. That means instead of only being fun to play around with, these are becoming enterprise tools, making it possible for developers to automate technical tasks at scale.
NLP—specifically natural language understanding, which SESAMm works on—is not untouched by these applications. Many of these large language models can perform zero-short learning, which means NLU can be performed without pre-training, a huge advance in this industry. However, zero-short learning is insufficient for many advanced sentiment and ESG analysis tasks. We still need additional data sets to fine-tune the data for a specific purpose.
What does this mean for the natural language generation sector? Many startups—especially anything around chatbots—have folded, some just in Q4 of 2022. ChatGPT’s success means it’s solved and replaced the need for many of them, and basically, anything content creation on the B2C side has and will struggle.
Defensive edge
Otherwise, things are looking good in our sector. For example, at SESAMm, we’re focused on what I call “last-mile AI.” In our specific business application, you can’t bypass the need for a data set because we’re trying to attain a precise result for specific, often risk-related applications. Open-source large language models like GPT-3 and BERT can get you mostly there, and that’s fine for general purposes. But for “last-mile AI” applications, there’s a lot you can’t do without additional work.
And here lies what I think is one of SESAMm’s defensive edges: the “last-mile AI.”
Instead of finding ways to protect its algorithms, the AI business community would do better to defend its use cases because the algorithm’s value will decrease progressively. In contrast, the value of a use case’s purpose and the data set used to achieve the use case will grow.
Competitive edge
Computing power and the resources it takes to train large language models remain challenging to applications like OpenAI. It takes electricity, heat, and money to train these models, and AI has an environmental impact. So far, we’ve justified this cost in the name of optimization—meaning that we put in this extra cost upfront so that the likely efficiency will offset or reduce that cost later—but it’s still a cost to incur.
AI companies, especially those in the NLG space, will do well to find their competitive edges, areas optimized for a specific purpose like “last-mile AI.” Companies like OpenAI will likely continue to optimize their models for quicker responses but don’t necessarily have the problem of solving for a specific use case.
At SESAMm, for instance, a big challenge and expertise we developed in-house is inference time—or how quickly we can apply the model to an article or an individual sentence. Because we’re processing so much live content, the more time it takes to process—milliseconds multiplied by a billion—the more costly it is.
Our data lake currently holds over 20 billion articles, messages, etc., from over 14 years, and we add 10 million more daily. That’s a lot of content to analyze. But we make it so our clients can access the data within seconds.
The need to optimize models for fast inference and adapt to deep industry-specific use cases will remain one of the key reasons companies will have to continue re-training their own models. That doesn’t mean large language models don’t add value here. Their open-source versions simply become an impressive building block for any NLP application and accelerate the rate of innovation and productivity in the whole field.
My summary thoughts on AI for 2023
When Google launched BERT in November 2018, we quipped that Google had open-sourced this system as a joke because no one could put it into production because BERT was so big. Many companies didn’t have the computing capabilities to do anything with it at the time. Now we do.
This year, Google did it again; they released a model that’s even bigger than GPT-3. Of course, almost no one besides Google can put that model into production now. But my point is that there will always be computing, resources, and other challenges to making AI advancements. That’s why I think AI companies must focus on defensive and competitive edges.
Regardless of the challenges, I see good things happening in the NLU space being massively improved by large language models. I see improvements as we incorporate these models today compared to deep-learning models trained from scratch a few years ago. I also see a significant decrease in the amount of data we need to fine-tune results, reaching and focusing on the final client use case more quickly.
From a natural language generation perspective, I believe large language models will transform the world. And I’m really excited about this era because this transformation supports my deepest purpose, leveraging AI to accelerate innovative decision-making. We do this by giving decision-makers access to technology that analyzes research content, news, and discussions. And if we increase the rate of innovation or the quality of decision-making by 10% globally, the impact could be huge for all industries: healthcare, finance, fashion, you name it. Industry leaders can make better ESG and SDG choices that will affect our world on a grander scale.
2023 will be an exciting time for AI, specifically for NLG and NLU. Of course, we’ll continue to see AI innovations. But more importantly, leaders will have better insights to make better decisions, creators will create more—and more complex—content, and overall, the applications will become more specific to solving the needs of particular use cases.
Here’s to the new era of AI in 2023. Cheers!
About SESAMm
SESAMm is a leading NLP technology company serving global investment firms, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms. SESAMm provides datasets and NLP capabilities through TextReveal® to generate alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporate studies, and more. With access to SESAMm’s massive data lake, comprised of 20 billion articles and messages and growing, its clients can make better investment decisions.
As the year closes, it's time to reflect on some of our favorite moments, so we've compiled a list of the top 10 blog posts highlighting the most popular and insightful content we've published. From alternative data trends to NLP and ESG topics, these posts have resonated with you, our readers, and hopefully, continue to provide valuable information and inspiration. Join us as we look back at the top 10 posts of the year and see what made them stand out.
It's truly humbling that this blog post, celebrating our eighth anniversary, made the top 10 list. It reflects on the progress we've made and the milestones we achieved over the past eight years. We also discuss our plans for the future and our commitment to continuing to innovate and provide high-quality solutions for our clients.
#9 3 Remarkable Trends NLP Text Mining Exposes About Used Cars & U.S. Inflation
Back in May, we revealed inflation trends in the used car market and identified specific trends and patterns that can help predict changes in the market and inform investment decisions.
Suppose you're interested in learning how AI technology can improve the efficiency and accuracy of financial analysis. In that case, you won't want to miss this blog post. In it, we discuss a case study involving Tokio Marine Nichido, one of the largest insurance companies in Japan. By partnering with SESAMm, they were able to implement our AI-powered solutions and achieve impressive results, including increased productivity and a more comprehensive understanding of their data. This post provides valuable insight into the potential of AI technology and its real-world applications in the financial industry.
Are you curious about how we use AI technology to gain valuable financial insights from news and social media? This blog post is a must-read. We discuss how SESAMm's AI-powered solutions can analyze a vast amount of data from these sources and extract valuable information that can inform investment decisions. From predicting market movements to identifying trends and patterns, our technology provides a unique and powerful way to stay ahead of the game in the financial industry.
Learn more about the role of alternative data in the financial industry. In this blog post, we discuss how SESAMm's AI technology can analyze a wide range of data sources, such as news and social media, to identify controversies and potential risks in the market. By providing a more comprehensive view of a company or investment, our solutions can help investors make informed and strategic decisions. Overall, this post offers valuable insights into the potential of alternative data and its role in the financial industry.
Read this quick guide to natural language processing, a subfield of AI technology that focuses on the interaction between computers and human language. We discuss the basics of NLP, including its history and applications, as well as the challenges and opportunities it presents. It also provides an overview of SESAMm's NLP technology and its role in the financial industry. This post offers a valuable introduction to NLP for those interested in learning more about this exciting field of AI technology.
One of the year's biggest news events was the exclusion of Tesla from the S&P 500 ESG index, a huge hit for the electric car manufacturer and a sign of the growing importance of ESG criteria in the financial industry. This blog post discusses the event's significance and provides insight into the potential of ESG data and its role in informing investment decisions.
This blog post discusses how knowledge graphs are at the core of text analysis and provide valuable insights for investors. We explain how these graphs, representing real-world entities and their relationships, can analyze large amounts of data and extract valuable information. We also discuss how SESAMm's AI technology can generate and use knowledge graphs to provide a more comprehensive view of a company or investment and support decision-making in the financial industry.
Learn how organizations use NLP technology to detect greenwashing, the practice of making false or misleading claims about a company's environmental practices. We explain how SESAMm's AI technology can analyze large amounts of data, including news and social media, to identify inconsistencies and potential risks in a company's ESG claims. This post also offers valuable insight into the potential of NLP technology to support responsible investment and combat greenwashing in the financial industry.
At number one, your favorite post is about the potential of AI technology to provide valuable insights into ESG data. Here, we discuss how SESAMm's AI-powered solutions can analyze a wide range of alternative data sources, including news and social media, to provide a more comprehensive view of a company or investment's ESG performance. This post offers valuable insight into the role of AI in responsible investment and the potential of alternative data in informing ESG analysis.
Thank you for reading through this year's 10 most-popular blog posts. Which is your favorite, and how would you rate them?
November 11, 2022, FTX, a $32 billion cryptocurrency exchange company that many believed would “change the world,” filed for bankruptcy. This news shook the crypto and financial communities, compelling many to debate the future of the crypto market and its platforms.
How did FTX collapse?
You could say that FTX’s collapse began before the news broke, but here’s a summary of events as The New York Times and ABC News details:
Breaking news
In early November, CoinDesk, a crypto publication, broke the news on a leaked document from FTX. The balance sheet showed that the hedge fund run by Sam Bankman-Fried (SBF), Alameda Research, held a substantial amount of FTT tokens. In short, SBF had set up Alameda (his trading firm) and FTX (his exchange firm) in such a way that if one unit experienced trouble, such as dropping cryptocurrency prices, the other experienced it, too.
First domino falls
By the way, FTT is used for various functions, including traders’ payment of operation fees. Also, by the way, Changpeng Zhao, Binance’s Chief Executive, sold his stake in FTX to SBF in 2021, partially with FTT. So, “due to recent revelations,” Binance (Zhao) announced on November 6, 2022, that it would sell its FTT tokens.
Other dominos follow
Traders responded; they hurried to pull funds out of FTX out of fear, and FTT’s price fell. Meanwhile, FTX processed withdrawal requests over three days, amounting to an estimated $6 billion. The liquidity crunch was upon it.
Then, on November 8, Binance said it would bail out FTX. But on November 9, Binance backtracked and announced in a Tweet that it would not “as a result of corporate due diligence,” while also citing regulatory investigations and reports of mishandled funds.
Things get worse
The next day, November 10, the Securities Commission of the Bahamas froze FTX’s assets, citing the public statement about potentially “mishandled” and “mismanaged” customer funds. On November 11, FTX filed for Chapter 11 bankruptcy protections, and SBF resigned as CEO. John J. Ray III—famously known as the CEO who headed the infamously known energy company, Enron, through its collapse in the 2000s—replaced SBF on November 17.
Fallout
Today, FTX faces federal investigation for securities laws violations based on a report by The Wall Street Journal regarding FTX lending customer deposits to Alameda Research for liabilities, of which the company’s top executives were aware. Investors have suffered loss, traders have suffered loss, and the greater crypto community and regulators are asking questions.
FTX and SBF web data analysis
News about FTX’s collapse generated tons of web data for us to scour. With this data, here’s what we aimed to find out:
How did the public web react to FTX’s collapse?
Could we have seen red flags before the news broke?
What was FTX’s collapse’s effect on the cryptocurrency market’s sentiment?
Is it possible to evaluate cryptocurrency exchange companies’ ESG risks and opportunities?
Was FTX’s collapse unprecedented? If not, what does web data tell us about that?
FTX and Sam Bankman-Fried mentions analysis
Web public sentiment for FTX and SBF was consistently positive until Q1 of 2022. As mentions volume increased, their sentiment polarity decreased (Figure 1). The mentions spike for both in November when CoinDesk broke the news. Likewise, polarity dips into the negative range for both.
Definition: Polarity represents the aggregate of positive and negative sentiments (opinions or reviews) on a company. A 0 score means there is as much positive as negative sentiment expressed. The dotted and dashed lines represent sentiment in the following charts.
Figure 1: FTX and SBF mentions and sentiment over time.
Looking closer at Q1 (Figure 2), we find that mentions affecting sentiment increased for FTX and SBF during this period. What are the mentions about, and why did they affect polarity negatively?
Figure 2: FTX and SBF pre-bankruptcy mentions and sentiment.
It turns out that SBF is linked to other keywords—we call these co-mentions—and between January 2022 and November 2022, SBF/withdrawal co-mentions (Figure 3) spiked in July when SBF defended Terra Luna’s founder, who was accused of peddling a Ponzi scheme.
Figure 3: FTX and SBF withdrawal co-mentions.
If withdrawal co-mentions brought up possible reasons why SBF and FTX experienced dips in sentiment, what other co-mentions could give us more insight? How about donations, SEC, and U.S. elections?
Figure 4: Donations, SEC, and U.S. elections co-mentions with SBF.
Corporate governance stands out when evaluating SBF’s ESG risks, but his social risks are nothing to ignore either.
Figure 5: SBF governance risks over time.
Two areas of governance risks to note are money laundering and board of directors (Figure 5). Money laundering as a co-mention has been an issue as early as February 2022, but it became a bigger issue in October. These risks may be popping up due to allegations of manipulating the price of the APT token and a securities violations probe.
If you’ve read this far, you by now get an impression of FTX and SBF, from mention volume to sentiment analysis and ESG risk. But how did FTX’s collapse affect the overall cryptocurrency market? Let’s find out.
In comparing the sentiment polarities for FTX and the crypto market from January 2021 through November 2022 (Figure 6), the sentiment for crypto remains relatively steady despite FTX’s sentiment taking a hit.
Figure 6: Effect of FTX collapse on the crypto market.
When comparing other cryptocurrency exchanges to FTX (Figure 7), sentiment polarity for them is hardly affected, except Binance, because of its connection with FTX. Oddly enough, eToro experienced a boost in sentiment, possibly because of its core values around openness and transparency, the fact that they’ve been around since 2007, its early compliance with regulations (i.e., AMF, FCA, ASIC, BaFin, and ACPR), and that it also proposes investing in stocks and ETFs, a contrast to most other crypto market exchanges. Bitfinex has its own issues, so its dip in sentiment might not be correlated.
Figure 7: FTX sentiment comparison across competitors.
At this time, FTX’s ESG risks based on the mention volume are only surpassed by Bitfinex (Figure 8), which its risks are based on many other reasons we won’t get into in this article.
Figure 8: FTX and competitors ESG risks by mention volume.
Centralized vs. decentralized crypto exchange platforms
FTX’s collapse also affected sentiment around the centralized vs. decentralized debate. Since October 2022, sentiment for centralized exchange platforms, such as FTX and its competitors, has fallen (Figure 9).
Figure 9: Centralized vs. decentralized mentions and sentiment over time.
Likewise, the mention volume for self-custody has more than doubled in the last couple of months (Figure 10). Although centralized platforms offer quicker and easier access to crypto trading, traders are considering complex but more secure options such as crypto wallets and keys because, like banks, centralized exchanges can do what they will with cryptocurrency while it’s in their possession. With self-custody, owners are in control.
Believe it or not, FTX was not the first crypto exchange to collapse. In 2014, Mt. Gox—the biggest crypto exchange at the time—lost half a billion dollars worth of Bitcoin due to a hack. How did Mt. Gox’s collapse affect sentiment for the crypto market then? The short answer is: It didn’t.
Figure 11 shows that while Mt. Gox’s sentiment polarity fluctuated, even reaching negative territories, the sentiment for the crypto market remained relatively stable and positive.
Figure 11: Mt. Gox and crypto sentiment comparison.
Is FTX’s collapse a warning for investors?
Our analysis is that investors should treat cryptocurrency exchanges like any investment opportunity. Do your due diligence and monitor your portfolio with tools like SESAMm’s TextReveal®.
As for the cryptocurrency market, data shows that sentiment for it remains level and positive. We speculate that cryptocurrency and centralized exchanges are here to stay. However, based on historical data and current news, we suspect conversations about crypto regulations to increase.
Reach out to SESAMm
For a deeper analysis of FTX’s collapse and access to all charts and supportive-article links, reach out to a representative today.
If you've been following SESAMm, you've probably noticed that we care about environmental, social, and governance (ESG) factors when researching, analyzing, and monitoring companies for portfolios. After all, ESG data is useful when making business and investment decisions. But what about SESAMm? Does it care about ESG as a company?
On the one hand, SESAMm could analyze ESG risks and opportunities on private and public companies worldwide without regard to its own ramifications on the world at large. On the other hand, what kind of service provider would SESAMm be if it didn't "walk the talk," as the saying goes? What good would SESAMm produce if it didn't follow the same ESG guidelines it delivers scores about?
SESAMm wants to operate the same way its clients would expect from firms in their portfolios, a way that it would support and be proud of. That's why management has developed SESAMm's ESG manifesto.
SESAMm's ESG manifesto purpose
ESG analysis, monitoring, and alerting are a substantial part of SESAMm's activities, so it's the company's mission to actively develop ESG and UN Sustainable Development Goals (SDG positive impact) indicators on millions of public and private companies. Many of the largest financial institutions and corporate ESG teams trust SESAMm to help monitor assets, suppliers, and clients.
SESAMm's ESG beliefs
SESAMm's management believes that:
The financial sector and large corporations have a considerable role in ESG. They are vital to achieving the goals set by The Paris Agreement.
ESG data is essential in enabling the transition toward responsible investment for financial institutions and corporations.
Current offerings lack ESG ratings coverage, lack transparency, and provide inadequate indicators, making it difficult for leaders to set objectives.
AI is the technological solution that will enable leaders to solve these issues and generate the right data with the right transparency, coverage, and frequency.
SESAMm's environmental actions
First, to reduce its ecological footprint, SESAMm has chosen to work with server hosting providers that use only renewable, non-carbon energy. Scaleway, for example, is a French internet hosting company that optimizes its hardware by reducing the incidence of server overheating.
Second, SESAMm has a lease contract to rent and replace computers every three years. The computers are then refurbished and reused, significantly reducing their environmental impact. Similarly, cloud infrastructures such as AWS or Scaleway recycle some hardware, such as hard drives, also to preserve the environment.
Third, SESAMm favors environmentally-friendly office accommodations. For example, the company measures and limits transportation needs by enabling employees to work remotely up to four days a week. It has also implemented waste sorting throughout its offices and taken actions to reduce energy consumption and waste, such as offering reloadable batteries and water dispensers to avoid plastic bottles. Further, most of SESAMm's offices are in co-working spaces, like WeWork, which offers reusable tableware and solar-powered energy.
Notes: In 2022, SESAMm is the only French company selected for the Green Finance Subsidy Program for Tokyo Market by the Tokyo Metropolitan Government. In the future, SESAMm plans to launch climate workshops ("La Fresque du Climat") to increase employee awareness.
SESAMm's social actions
Diversity, equity, and inclusion
SESAMm makes efforts to promote diversity and ensure an inclusive workspace for everyone. For example, even though the lack of women in tech companies is a challenge shared by many startups, SESAMm's workforce currently comprises 25% of women, of which 25% are managers. Some of SESAMm's women workforce also hold highly strategic positions, such as Chief Financial Officer, HR Manager, Technical Lead, and Project Manager, to name a few.
Moreover, SESAMm's teams are composed of ten nationalities across various international offices. And when possible, the company promotes international mobility of its talents, such as a transfer to New York or talent passports to enable employees abroad to work in France.
Diversity in education and training
SESAMm also values diversity in education and training. For instance, the company favors the internal sharing of knowledge through mentorship and internal training and enables employees, newly graduated or in professional reconversion, to be trained and develop new skills for their careers. Currently, the team accounts for 21% of employees who were first interns at SESAMm and now hold permanent positions. 68% of employees hold advanced graduates (Master's degrees and more), 30% are undergraduate (associate and bachelor's degrees), and 2% hold only a high school diploma.
Work-life balance
The company values a healthy work-life balance and is committed to improving working conditions and ensuring well-being. SESAMm has implemented several measures to reinforce work-life balance, mainly by allowing employees to organize their week as they wish and to work remotely up to four days a week. In addition, SESAMm published a charter on the right to disconnect to strengthen everyone's right to disconnect from their professional communication tools. SESAMm won three awards from ChooseMyCompany: HappyAtWork, TechAtWork, and WorkAnywhere in 2021 for providing employees with a healthy work-life balance.
In 2021, SESAMm implemented several tools to automate processes and facilitate the employees' daily life, including an HRIS and a tool for annual reviews. The management of skills and careers and the implementation of mentoring and training are at the heart of SESAMm's HR roadmap for the years to come. The company also implemented an anonymous internal tool, Poplee Engagement, to measure employees' well-being at work and collect their suggestions for the company.
SESAMm's governance actions
SESAMm relies on a supervisory and strategic board and an internal executive committee, which are consulted for every important matter, even if not required by the SHA. The board comprises investors with diverse expertise from different geographies—the U.S., Canada, and Europe. The founders are all board members and own 50% of the voting rights. SESAMm is a fundamental trade-off between entrepreneurs' freedom to pursue their distinctive visions for value creation and investors' need to ensure management accountability.
Employees are involved in the company's success through option plans, and wages are homogeneous across the company. The executive committee is transparent with SESAMm's employees and shares information on the strategy during regular "Ask Me Anything" meetings occurring once every six weeks, during which collaborators can ask any questions anonymously or not.
Since the recruitment of senior profiles, such as the Scrum Master and a Head of Agile, and automation through the implementation of new internal tools, the company has a better organization with clarified roles and responsibilities. SESAMm also optimized the structure of KPIs reportings, enabling a better long-term plan and decision-making process.
To reinforce these actions, notably in ecology and social matters, the company has implemented several charters and guidelines in the last few years, like an ethical charter, teleworking charter, right to disconnect, and IT charter.
SESAMm's ESG manifesto summary
SESAMm's leaders believe we can't sit on the sidelines while global issues pervade society. We must play an active role in addressing our greatest challenges by looking in the mirror and looking at ourselves. Through hard work, perseverance, and accountability, we believe we can make a difference to help our people, economy, and planet thrive.
As the 2022 United Nations Climate Change Conference wraps up, governments and, by proxy, companies are charged with fulfilling new recommendations, especially for non-State entities to commit with integrity to Net-Zero. COP27, as the conference is also called, is the time and place where we claim as a united society at the world's center to make change for the better.
But COP27 is over. Now what? Do we go back to business as usual? Do we wait and see if we stick to any of these new agreements? Or worse, do we say we'll make changes but fall short of making those changes?
I say no. We can do better, and here's why…
We need to talk about climate change
Climate change effects are more than global warming. Global warming consequences include:
Rising sea levels
Stronger and more intense hurricanes
More droughts and heat waves
Longer wildfire seasons
And more
Why do I bring these up? Because all of these effects will impact your business in one way or another.
For example, did you know that the Rhine River, one of Europe's major rivers, is suffering from drought? Water levels are so low that barges are limited, and it's disrupted river cruises because levels are currently 38 centimeters below the minimum required.
The same goes for the Mississippi River in the U.S. The Mississippi River has dropped to the lowest levels they've ever been in 34 years, driving up shipping costs. This challenge is also a big deal because the river carries 92% of agricultural exports.
Also, in the past year, damaging hurricanes and typhoons have damaged infrastructure in South Korea, South Africa, China, Japan, and the U.S., to name a few countries, affecting crops, manufacturing operations, supply chains, and much more across the globe.
I could go on about how each effect influences enterprise, but the bottom line is climate change is bad for business. And supporting companies that enable climate change is also bad for business, which brings us to the topic of environmental, social, and governance (ESG) measures.
We need to talk about ESG
ESG has become mainstream since the UN shared a report in 2006, a joint initiative by a group of financial institutions to develop policies and guidance on how to better incorporate ESG issues in securities brokerage services, asset management, and associated research functions. This introduction has helped industries establish goals through:
Managing ESG risks
Anticipating regulatory action or accessing new markets
Contributing to the sustainable development of their societies
However, with ESG policies come ESG data challenges. For example, ESG measuring, its data, and how companies report them are inconsistent. ESG data providers deal with "data gaps" differently, so their approaches can lead to discrepancies. And as ESG data becomes available publicly, how ESG data providers interpret the data varies, too.
We need to talk about greenwashing
In simplest terms, greenwashing occurs when a company misleads its stakeholders, investors, and consumers about its environmental practices, specifically by communicating positive environmental performance contrary to its actual, less flattering execution.
On the surface, you might think, "What's the big deal? We all exaggerate, right?" But as sustainability awareness among investors and eco-conscious customers grows, so has their scrutiny over business conduct to disclose information about a company's performance and its "environmental-friendly" products. Their scrutiny, coupled with the growing number of companies reporting their environmental footprints, reveals that many companies misreport and publish information about their ecological impacts, which regulators consider misleading or deceptive.
How do we know? Let's take a look at greenwashing mentions by industry.
We analyzed greenwashing mentions in web data. On the X-axis, we list the industries. The Y-axis measures the ratio of greenwashing mentions by N° of companies per industry (N=1166 companies) since 2015; this extraction method corrects sampling bias. Each industry is defined by a significant sample of large- and mid-capitalization-sized companies in developed countries.
This greenwashing mention chart clearly shows that the Energy industry has the highest ratio of greenwashing allegations. While many fossil fuel companies claim to be transitioning into clean energy, most mentions link these companies to advertisements and campaigns that don't align with the Paris Agreement goals. In contrast, fossil fuel companies are growing their carbon-intensive operations and products. It's a concerning trend because according to The Intergovernmental Panel on Climate Change (IPCC) report, "Climate Change 2021: The Physical Science Basis.", the data shows that emissions from fossil fuels are the primary cause of global warming, contributing up to 91% of global carbon dioxide emissions in 2018 as an example.
Second, on this chart is the Financial industry. It has fallen short of its commitments to climate action while continuing to finance fossil fuels. According to eMarketer, financial institutions have allocated $4.6 trillion for fossil fuels while promoting sustainable finance and supporting global energy transition.
Further, the mentions volume has grown year over year since 2015—when it was almost zero—to more than 1500 present day.
Clearly, this greenwashing problem is getting worse. So what can we do about it?
We need to talk about a solution
We don't have any control over what companies will do to fulfill their agreements, but we can understand their ESG data better and make better investment and portfolio decisions.
How? With AI.
AI, specifically natural language processing (NLP) algorithms, help us read billions of news articles, forums, and web text and extract unstructured data for analysis. With SESAMm's TextReveal®, we can see an entity's ESG controversies or events in near real time, providing a unique perspective to ESG data and details, filling the data gaps more accurately.
So when Company A reports on its ESG goals, we can help verify if the results are accurate and find any potential controversies that didn't make the report. We also don't need to wait until ESG reports come out; we can extract this data from the web on an as-needed basis. Moreover, we can look at all types of companies across the globe, public or private. As long as web data exists for an entity (or concept), we can analyze it.
My final thoughts
COP27 might be over, but our agreements and commitments carry on. We have an opportunity today to make a positive difference toward climate change while still maintaining profits. In fact, I think we can be even more profitable if we support green and sustainable initiatives.
I'd like to hear your thoughts; feel free to reach out on LinkedIn and share them with me.
About Alexandre Tiesset
Alexandre Tiesset is the Head of ESG at SESAMm. He's worked in finance for seven years in various ESG-related roles, such as Credit Analyst, Sustainable Investing Specialist, Index Product Specialist, and more. He holds a Master of Science degree in Finance and Financial Analysis. His passion lies in the intersection of finance and general knowledge and making new connections.
Reach out to SESAMm
SESAMm is a leading NLP technology company serving global investment firms, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms, by providing datasets or NLP capabilities to generate their own alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporation studies, and more.
To learn how you can generate NLP-enhanced ESG data for your firm, or to request a demo, reach out today.
It’s a valuable tool that’s become a standard measurement in sustainable finance for corporate stakeholders.
However, due to the growing demand and need for accurate and timely ESG data in investment decision-making and the ESG finance field, it’s also difficult to attain.
And if you’re reading this, it’s because you likely use ESG data regularly and are looking to improve your data or insights into the data. Or you’re new to ESG data and want to understand it better and how to get accurate and timely data using AI. Whatever your reason, we’ve got you covered.
But before we dive into these points, let’s cover a quick history of ESG.
Who created ESG (plus when and why)
Kofi Annan, former United Nations Secretary-General, invited a group of financial institutions to develop policies and guidance on how to better incorporate ESG issues in securities brokerage services, asset management, and associated research functions. In 2004, this joint initiative published “Who Cares Wins: The Global Compact Connecting Financial Markets to a Changing World,” a report that the UN later shared in the 2006 United Nations Principles for Responsible Investment (PRI) report. It would be the first time ESG criteria are incorporated in companies’ financial performance evaluations.
Stronger, more resilient, and sustainable
According to the “Who Cares Wins” report, the contributors were convinced that “in a more globalized, interconnected, and competitive world, the way that environmental, social, and corporate governance issues are managed is part of companies’ overall management quality needed to compete successfully.” The report goes on to state that “Companies that perform better with regard to these issues can increase shareholder value by, for example, properly managing [ESG risks], anticipating regulatory action or accessing new markets, while at the same time contributing to the sustainable development of the societies in which they operate.”
The cohort believes ESG issues can significantly affect a company’s reputation and brand, an essential part of its value. And as the report puts it, “Endorsing institutions are convinced that a better consideration of environmental, social, and governance factors will ultimately contribute to stronger and more resilient investment markets, as well as contribute to the sustainable development of societies.”
As a standard measurement, ESG becomes a way for companies to demonstrate accountability, trust, and transparency in their ESG goals to appeal to customers, employees, and investors. But how is this data produced and seen?
Where does ESG data come from?
Besides implementing ESG principles and policies, companies are asked to provide information and reports on related performance in a consistent and standardized format. This ESG reporting includes identifying and communicating key challenges and value drivers through normal investor relations communication channels. Companies are also encouraged to mention ESG information in their annual reports.
As you might notice in this scenario, ESG data comes primarily from the very companies we want to evaluate. See a conflict here?
Today’s ESG data challenges
At their core, ESG metrics capture a company’s performance on a given ESG issue. When this aim is achieved, investors can use the data to evaluate and hold companies accountable for their ESG performance. But how would you know whether ESG data accurately capture a firm’s performance?
ESG measuring, data, and how companies report them are inconsistent.
Lack of benchmarking transparency undermines the reliability of peer performance ranking.
ESG data providers deal with “data gaps” differently, and their gap-filling approaches could lead to significant discrepancies.
Interpretation differences among ESG data providers are considerable and are growing with the quantity of data becoming publicly available.
“Although 92% of S&P companies were reporting ESG metrics by the end of 2020, according to a 2020 BlackRock survey of clients, 53% of global respondents cited ‘poor quality or availability of ESG data and analytics; and another 33% cited ‘poor quality of sustainability investment reporting’ as the two biggest barriers to adopting sustainable investing.” — Deloitte
Artificial intelligence (AI) to meet rising ESG data demands
Even as investors consider ESG one of many major market factors, sourcing and analyzing data remains a problem. “The absence of standardized ESG datasets and reporting methodologies makes it difficult for issuers to disclose meaningful information on sustainability,” according to a post on WorldQuant.
But despite the ESG data limitations, ESG investing demands continue to grow. For instance, in its 2021 Key Findings, RBC Global Asset Management found that 75% of respondents of 800-plus institutional investors had integrated ESG principles into their investment approach, an increase from 67% since 2017.
Machine learning helps with this demand. For instance, advances in natural language processing (NLP) in machine-learning techniques have made it possible to extract unstructured data from web sources, like news, blogs, forums, and social media, to gain timely and accurate ESG insights. This alternative data has been integral for seeing an entity’s ESG controversies or events in near real time, providing a unique perspective to ESG data and details, filling the data gaps more accurately.
How to get ESG data using natural language processing (NLP)
NLP algorithms can read billions of news, articles, and text-based web data. It categorizes extracted data and can determine positive and negative sentiments, producing potential predictive indicators. Investors and researchers can use NLP to mine keywords and categories of underlying data to evaluate portfolio companies or see their exposure to ESG factors.
Some ESG rating agencies are now integrating or outsourcing NLP-derived datasets into their processes to extrapolate ESG scores. Likewise, investment firms, like asset managers, are incorporating NLP-enhanced web data into risk management, especially when looking into private-equity-type assets. Many are meeting their needs with NLP companies, such as SESAMm and others.
SESAMm is better at extracting ESG-related data for many reasons. First, it has one of the largest data collection sources to extract data from (data lake). Second, its NLP machine learning algorithms are tuned specially to key indicators.
1. SESAMm’s massive data lake
What makes SESAMm’s data lake unique and ideal for investment research and advanced analytics? SESAMm’s data lake is:
Broad and large
Includes more than 100 languages
Updated in near real time
Including data since 2008, the data lake consists of more than four million data sources made up of more than 20 billion articles, forums, and messages, such as professional news sites, blogs, and social media, increasing by an average of six million per day. The data lake is also updated hourly to give investors near real-time insights into their investment interests.
Moreover, the coverage is global, with 40% of the sources in English (U.S. and international) and 60% in multiple languages, including Japanese, Chinese, and Eastern European. We select and curate these sources to maximize coverage of both public and private companies, focusing on quality, quantity, and frequency to ensure a consistently high input value.
SESAMm’s developers tune the machine-learning algorithms for key indicators such as mention volume, sentiment analysis and emotion, ESG, and SDG. Additionally, they optimize the structure and schema for optimized SQL queries.
For example, our knowledge graph, a digital representation of a network of real-world entities, puts the schema in context through semantic metadata and linking, providing a framework for analytics, data integration, sharing, and unification. In other words, we map and label the concepts, entities, and events and connect and identify their relationships for quick and accurate recall.
In October 2022, this multinational integrated energy and petroleum company found itself in the crosshairs of two non-government organizations (NGOs), accusing it of "exploiting a gas field used to manufacture kerosene used by Russian planes in their bombings in Ukraine," according to the French daily Le Monde. The accusations expressly point out the 16 March 2022 strike, which killed around 600 civilians taking shelter at a Mariupol theatre.
The company? TotalEnergies.
The NGOs? Razom We Stand (Ukraine) and Darwin Climax Coalitions (France).
Also, in October, TotalEnergies posted a third-quarter net profit amid these allegations. "The French group reported an adjusted net income of $9.86 billion, compared with $4.77 billion for the same period in 2021 and $9.8 billion in the second quarter of this year," per Reuters.
Should investors and asset and portfolio managers be concerned? Is this one instance of allegations a nothing burger, or is it a sign—one of many red flags—to evaluate? Let's find out in this edition of Alternative Data Trends: TotalEnergies.
After the spike in controversies in March 2022, TotalEnergies's web mentions continued to increase. And as a reflection of the mentions volume increases, the company's polarity decreases, moving in the direction of negative sentiment. News events triggering these movements include the legal case for allegedly fuelling Russian bombers and refinery strikes over wages.
Let's take a closer look at TotalEnergies's environmental risks, specifically for general environmental strategy, climate change and atmospheric pollution, and biodiversity. The topic of climate change and atmospheric pollution takes the largest share of mentions. Web data driving this volume include:
TotalEnergies's social risks reveal even more controversies, looking at human rights, human capital, and customer relations topics. Human rights and human capital topics almost equally dominate topic mentions. Controversies highlighting the human rights topic include:
Finally, let's look at TotalEnergies's governance risk, specifically within topics for influence strategy and communication, corruption, and board of directors (executive and company management). The influence strategy and communication topic dominates the mentions volume. These controversies relate to greenwashing, such as:
Recapping TotalEnergies's ESG risks and public perception
We only scratched the web data surface for information on TotalEnergies, but even with this overview, you can probably see a pattern. Should investors, asset managers, and portfolio managers be concerned about the latest allegations against TotalEnergies? Maybe. At the very least, the data shows you should dig into the research and ask more questions to mitigate any risks in your portfolio companies.
Reach out to SESAMm
SESAMm is a leading NLP technology company serving global investment firms, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms, by providing datasets or NLP capabilities to generate their own alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporation studies, and more.
For questions, access to the full report, or to request a TextReveal® demo, contact us here:
SESAMm has a large data lake of more than 20 billion articles (growing by 5–10 million a day) and 14 years of data in 100 languages. But its size alone is not what makes it good; it’s a refined process to find the exact data you want that makes it better.
Here’s an example to help explain the point. We’re sometimes asked for help researching data to forecast and monitor the commodities market, even by large companies with their own commodities desk of traders and quant researchers. Why would they seek help from outside their firm?
Simply put, traders want an edge. They want information advantages that others are likely to miss, so they look to alternative data from various sources, anything that adds value and is from different angles. And, as it turns out, commodities are a more challenging segment to analyze when it comes to alternative text data. Unlike for companies, commodity texts are scarcer and need more domain knowledge to unravel their implications. A simple sentiment analysis doesn’t bring enough relevant information.
For a more in-depth view, join us as we discuss NLP-derived alternative data, its benefits, challenges for researchers, and why bigger isn’t always better in the world of data.
The macroeconomic environment is moving quickly—inflationary pressures, war in Europe, political instability, and plenty of other topics to make a trader's head spin. While there’s an abundance of structured macro data, it's much more difficult to extract value from unstructured text on the web.
However, with the right partner and the right tools, it doesn't need to be difficult to rein in this complexity. But more on that later.
More data, more problems
We're obviously in the information age; we have more data within reach than ever before. And you'd think that more data would make it easier to find consistent relationships between the macro economy and price returns. The hard truth is that it doesn't.
Of course, you have access to comprehensive historical information, but developing economically intuitive and worthwhile systematic strategies from historical data alone is challenging. Despite having this data, it could still be incomplete, missing that bit of nuance for a theme you're examining.
Nowcasting for more complete, current data
Nowcasting—a contraction of the words now and forecasting—is the prediction of the present and the near future using data from the recent past as an economic indicator. Nowcasting models can be applied in real time as a proxy for official measures, such as monitoring the state of the economy, themes, or sectors: food, transportation, energy, and so on.
For example, you could look into what's being said about supply chain disruption for semiconductors. How is the topic trending across industries or the broader public? And how positively or negatively is that topic perceived over time? This information helps give financial data context and direction, a way to predict what happens next. So where do you turn to for reliable, timely nowcasting data?
Nowcast-enhancing platform
At SESAMm, we have a flexible, adaptable, and modular platform to nowcast pretty much any macroeconomic theme: inflation, supply chains, unemployment, and everything in between. If you can gauge it, we can find data on it.
How do we do this? Our natural language processing (NLP) platform makes sense of all available news, articles, and forums on the web. Currently, there are more than 20 billion articles in our data lake, and it's growing by millions daily. And because we update our data lake multiple times a day, you can read nowcast macroeconomic indicators in near real time.
This flexible approach to building themes goes way beyond off-the-shelf sentiment feeds, and you can adapt to new, emerging factors on the fly.
Use case: inflation insights
With the TextReveal® API and Dashboards, you can generate custom proprietary inflation requests—or pull existing queries by country and sector—and use this data in your nowcasting models (Figure 1).
Figure 1: TextReveal's dashboard highlights inflation topics on the web and associated sectors.
In this example extracted from our API (Figure 2), the number of sources mentioning inflation is relatively stable until 2021, when it starts to increase rapidly, in anticipation of actual inflation readings.
Figure 2: The number of sources mentioning inflation increase in early 2021.
As you can see, you can track a topic and map it to various segments, creating a signal that accurately follows that theme over time. But that's not all. Macro teams can inject their expertise into building these queries, too. So if they have specific ideas on keywords and themes to capture—for example, inflation in Brazil—they have complete control over them.
Ultimately, you can break down the data by volume, sentiment, sector, language, or country. Do you want to know what the Japanese market makes of rising inflation in the U.S.? With SESAMm's platform, you can slice the data in different ways to find out.
Results with transparency
All that inform the results of your queries are available for scrutiny. Say you want to understand why a topic or theme is trending one way or the other, or maybe the sentiment isn't what you expect. You can drill down to the source articles to see why (figure 3).
Figure 3: An example of source articles affecting sentiment score.
Use Case: predictive signals for macro factors and commodities
We worked with a client as part of an asset allocation strategy to build indicators reflecting the tone of the Fed fund rate to see what we could predict based on the indicators.
[figure 4]
Figure 4: The Fed tone indicator successfully anticipated the major changes in the fed rates—a reduction during the COVID-19 crisis and a rise in 2022.
In Figure 4, the language we uncover becomes increasingly dovish, as indicated by the blue line, the aggregate of the hawkish and dovish indicators. It's proceeded by the fall in interest rates, the start of covid, and the recent inflationary period. Then, the indicators spike way before interest rates move up. Of course, it isn't the only factor, and it's not 100% predictive, but it does reflect future movements. Inflation is at an eight-year high right now, so it's indicative of continuing inflation returns and continuing rising interest rates.
Nowcasting and forecasting with TextReveal
With TextReveal, you can nowcast any macro theme by building expert-driven queries and predictive forecasting signals to get insights into volume, sentiment, and more.
If you want to find data relationships that accurately reflect economic trends and macro themes to what's happening online in near real time with a high degree of control, reach out for a demo.
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