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 the natural 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.

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.

#10 Open SESAMm: Our 8th Anniversary

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.

#8 Predicting stock price movements using news and social media data

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.

#7 How AI Power Turns News and Social Media Content Into Financial Insights

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.

#6 How Alternative Data Identifies Controversies Before Mainstream Sources With Examples

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.

#5 What Investors Ought to Know About Natural Language Processing: A Quick Guide

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.

#4 S&P 500 ESG Index Drops Tesla: This Analysis Supports the Decision

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.

#3 What Investors Ought to Know About Knowledge Graphs: The Core of Text Analysis

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.

#2 How Organizations Are Using NLP To Detect Greenwashing

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.

#1 Gain Insights From Financial and ESG Data Using AI: A Comprehensive Guide

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?

Let us know what you think on Twitter or LinkedIn.

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.

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?

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.

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?

Bankman-Fried has exhibited patterns of making political statements, attempting to influence crypto regulations, and donating funds before elections. It should be no surprise that SBF’s co-mentions with C-level unethical practices yield additional data.

Note: To see the co-mentions with unethical practices chart, contact a SESAMm representative today.

Sam Bankman-Fried ESG risk analysis

Corporate governance stands out when evaluating SBF’s ESG risks, but his social risks are nothing to ignore either.

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.

Note: To see the social risks over time chart, contact a SESAMm representative today.

FTX and crypto market web data analysis

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.

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.

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.

Note: To see a comparison chart of social and governance risks for each exchange, contact a SESAMm representative today.

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).

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.

Note: To see additional charts, such as competitive data share for crypto wallets, contact a SESAMm representative today.

FTX not the first exchange collapse

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.

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:

1. The financial sector and large corporations have a considerable role in ESG. They are vital to achieving the goals set by The Paris Agreement.
2. ESG data is essential in enabling the transition toward responsible investment for financial institutions and corporations.
3. Current offerings lack ESG ratings coverage, lack transparency, and provide inadequate indicators, making it difficult for leaders to set objectives.
4. 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.

Learn more: “How Successful Investors Are Using AI to Get ESG Data: A Quick Guide”

Reach out to SESAMm

Is ESG data important to you? Reach out to one of our representatives and let us know how we can help you with your ESG strategy.

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.

Environmental, social, and governance (ESG) data. It has become a standard input for institutional investors, asset managers, and private equity firms, shaping investment decisions from first screening to portfolio monitoring and regulatory reporting. Yet the kind of ESG data most investors rely on is often self-reported, slow to update, and hard to compare from one provider to the next.

If you're reading this, you likely use ESG data regularly. You may be comparing ESG data providers, or you may have noticed that a company's ESG rating and its headlines don't always tell the same story. In this guide, we cover where ESG data comes from, why traditional ESG metrics fall short, how AI and natural language processing fill the gaps, and how to choose an ESG data provider.

Who created ESG?

Kofi Annan, former United Nations Secretary-General, invited a group of financial institutions to develop guidance on how to better integrate environmental, social, and governance issues into asset management, securities brokerage, and research. In 2004, they published "Who Cares Wins: Connecting Financial Markets to a Changing World." The work fed into the UN Principles for Responsible Investment, launched in 2006, and helped make ESG criteria a standard part of how investors evaluate companies.

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 endorsing institutions concluded 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."

Two decades later, that idea supports a large market for ESG data and ESG ratings, and a growing body of regulation, from SFDR and CSRD in Europe to the EU ESG Rating Regulation.

Where does ESG data come from?

ESG data covers a wide range of issues: carbon emissions and other environmental impacts, climate change exposure, labor practices and human rights, product safety, corporate governance, and business ethics. It reaches investors through three main channels.

• Company disclosures and sustainability reporting. Most ESG data starts with what companies publish: sustainability reports, annual filings, and policy documents. ESG frameworks such as the Global Reporting Initiative (GRI) and the Task Force on Climate-related Financial Disclosures (TCFD) aim to make ESG reporting more standardized, and regulations like the EU's CSRD are making parts of it mandatory. Standardization helps with benchmarking, but it doesn't change a basic fact: ESG data mainly comes from the same companies it is meant to evaluate.

• ESG ratings and ESG rating agencies. ESG rating agencies, including some data vendors, such as MSCI, Sustainalytics, and Bloomberg, collect disclosures, add their own research, and turn them into ESG scores. These scores make it easier to compare a company's ESG performance against peers, and they feed into ESG funds, indices, and screening rules.

• Outside-in data. The third channel is what others report about a company: news coverage, NGO investigations, regulatory actions, court records, and trade press. This is where a company's actual conduct shows up, often well before it reaches a sustainability report or a rating. It is also the hardest source to use, because it is spread across millions of documents in dozens of languages.

Today's ESG data challenges

At their core, ESG metrics capture a company's performance on a given ESG issue. Measuring that consistently has proven difficult. In the Journal of Applied Corporate Finance, Sakis Kotsantonis and George Serafeim identified four recurring problems:

1. ESG measurement, data collection, and company reporting practices remain inconsistent.
2. A lack of transparency in benchmarking makes peer rankings hard to rely on.
3. ESG data providers fill data gaps in different ways, which can produce large discrepancies.
4. Differences in how providers interpret the data are considerable, and they grow as more data becomes available.

The problem shows up clearly in the ratings themselves. Researchers at MIT Sloan's Aggregate Confusion Project found that ESG ratings from six prominent ESG rating agencies correlate at just 0.54 on average. By comparison, credit ratings from Moody's and Standard & Poor's correlate at 0.92.

Beyond inconsistency, traditional ESG data has four structural blind spots:

• It's self-reported. Companies decide what to disclose, and few volunteer their own controversies, which is what makes greenwashing possible.
• It's slow. Sustainability reports are annual, and ratings often refresh on a similar cycle.
• It dilutes severe issues. Blending many ESG factors into one score lets strong governance metrics offset a serious environmental or human rights breach.
• It misses private companies. Coverage is thin in low-disclosure markets, where much of private equity invests.

Artificial intelligence (AI) to meet rising ESG data demands

Even as the debate around ESG investing has become more polarized, demand for ESG data keeps growing. In BNP Paribas' 2025 ESG survey of 420 institutional investors, 85% said they integrate sustainability criteria into their investment decisions.

"Nearly half of investors (48%) anticipate allocating more budget to their sustainable investment strategy on ESG data acquisition." (BNP Paribas ESG Survey 2025)

Spending more on the same disclosure-based data doesn't close the gaps above. However, advances in machine learning, natural language processing, and large language models make it possible to extract ESG information from unstructured sources such as news, NGO reports, regulatory filings, and court records. This outside-in data surfaces ESG controversies and events as they are reported, often long before they appear in a rating, and it covers companies that publish little or nothing about themselves.

How to get ESG data using natural language processing (NLP)

Natural language processing (NLP) algorithms can read billions of documents across languages, detect which ones describe an ESG issue, and separate a genuine allegation from a passing mention. Large language models add the ability to summarize what happened, map it to ESG frameworks such as SFDR, CSRD, or the UN Global Compact, and assess its severity.

Two steps make the difference between noise and reliable ESG data:

• Attributing each mention to the right company. Entity resolution and a regularly updated knowledge graph link every article to a single, verified entity, including private companies, subsidiaries, and brands. Without this step, a scandal at a namesake ends up in the wrong portfolio.
• Keeping every signal traceable. Each flag should link back to its source documents. As regulations like the EU ESG Rating Regulation raise the bar for transparency, investors need to show where an assessment came from, not just what it says.

Several ESG rating agencies now integrate NLP-derived datasets, and asset managers and private equity firms use them in their ESG risk management. The same approach powers adverse media screening and adverse media monitoring, which banks, insurers, and investors use to check clients and counterparties for reputational and KYC risk.

Why does ESG risk show up in controversies first

An ESG controversy is an adverse event or conduct attributed to a company in public sources: a chemical spill, a corruption probe, a data breach, a forced labor allegation, a misleading climate claim. Controversies are where stated policy meets actual practice. That is why they are often the earliest visible sign of ESG risk, and why investors increasingly track them alongside ESG ratings.

Controversies also capture risks that disclosures tend to understate. Social factors like labor conditions in a supply chain rarely appear in a sustainability report until something goes wrong. Neither does the climate risk behind a delayed decarbonization plan, or the environmental impact of a single project.

Not every controversy matters equally, so severity is the key question. Two factors drive it: whether the harm can be reversed, and how far it reaches. SESAMm's Controversy Exposure Score is built around that idea. It rates a company's exposure to reported ESG controversies on a 0 to 100 scale over a rolling 24-month window. Rather than averaging, it applies "worst-of" logic, so a company's most severe controversy drives the score, whichever pillar it falls under. It measures impact on people and the environment and reflects what has already been reported, which makes it a complement to ESG ratings rather than a prediction.

For ESG due diligence and portfolio monitoring, that shift from what a company says to what is reported about it changes what investors can see, especially for private companies with little or no disclosure.

How to choose an ESG data provider

ESG data providers fall into roughly three groups. Traditional ESG rating agencies score companies mainly from disclosures. ESG reporting and compliance platforms help companies collect and publish their own data. Risk intelligence providers monitor what is reported about companies from the outside in. Many investors combine more than one. Whichever you choose, five questions separate a strong ESG data provider from the rest:

1. Does it cover the companies you actually hold? If your portfolio is mostly private or in emerging markets, a provider built on listed-company disclosures will leave much of it uncovered.
2. Can you trace every data point to its source? You should be able to click from an ESG score or alert to the documents behind it.
3. Is the methodology public? You need to know how ESG metrics are weighted, how severity is judged, and what a score doesn't measure.
4. How fast does it update? Annual refreshes suit reporting. Due diligence and monitoring need data that updates as events happen.
5. Is it ready for regulation? Ask how the provider supports SFDR, CSRD, and the EU ESG Rating Regulation, and whether it is seeking authorization with ESMA.

For a deeper checklist, see our 7-question buyer's guide to choosing an ESG data provider.

‍

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.

Analyzing TotalEnergies's ESG and sentiment data

Company polarity and mentions volume

In the last year, mentions of TotalEnergies have gradually increased, spiking in March 2022. The spike in mentions coincides with a dip in the company's sentiment. These abrupt changes occurred when news broke about a lawsuit targeting TotalEnergies, claiming that the oil and gas group misled the public in its rebranding campaign, and a former French presidential candidate, Yannick Jadot, accused TotalEnergies of being complicit in war crimes.

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.

ESG risks

A screening of TotalEnergies's ESG risks uncovers a similar trend. For example, our platform detects an increase in social risks as early as January 2022. This signal coincides with the company's exit from Myanmar because of human rights abuses.

Examining TotalEnergies's environmental risks

Environmental-related mentions volume

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:

• Accusations of downplaying climate risks
• TotalEnergies sued for alleged greenwashing.
• Investors pressuring TotalEnergies to align with the Paris Climate Agreement
• Pan-African civil society pressures TotalEnergies to halt EACOP
• Oil leak at TotalEnergies in Antwerp
• Paris and New York join climate litigation against TotalEnergies.

Studying TotalEnergies's social risks

Social-related mentions volume

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:

• Total and Le Drian turn a blind eye to a torture center in Yemen.
• Lawsuits regarding affiliations with oil projects in East Africa linked to the eviction of more than 100,000 people.
• And more.

Human capital topic controversies include:

• Multiple strikes on TotalEnergies's UK offshore platforms
• French union calls all-out strike for wages at TotalEnergies

Gauging TotalEnergies's governance risks

Governance-related mentions volume

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:

• Protests due to false claims regarding the company's transition to a "zero oil platform"
• Clashes with Greenpeace over 'inaccurate' greenwash claims
• Controversial rebranding
• Downplaying climate risks
• Misleading commercial practices

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.

Read the full article on Quant Finance.

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).

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.

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).

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]

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.