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Hydropower's ESG Paradox: Why the "Green" Asset Class Tops the Controversy Charts

August 20, 2026
5 mins read
Hydropower tops ESG controversy volume across 250,000+ projects, outranking coal. Why the greenest label in energy hides the heaviest social risk.

An analysis of over 250,000 infrastructure projects reveals that the sector most often filed under "clean energy" carries the heaviest environmental and social controversy footprint of any asset type assessed.

In the taxonomy of energy infrastructure, hydropower occupies a comfortable position. It is renewable, dispatchable, and long-lived, and it enters transition frameworks, green bond eligibility criteria, and net-zero roadmaps with minimal friction. Where coal is a legacy liability to be managed down and nuclear invites a specialized debate, hydropower is largely treated as settled. What these projects have actually done does not support that treatment.

Belo Monte, an 11,233 MW complex on the Xingu River in Pará, Brazil, is the sharpest test of the point, because it was built to answer this exact objection. Approved after decades of opposition to a far larger design, it was engineered as a run-of-river plant to minimize flooding, and its reservoirs cover 478 km², of which 274 km² was already river channel at high water, a 61% reduction compared with the 1980s proposal, according to the operator's own regulatory filing. The mitigation was designed from the start, and everything that follows happened regardless.

Biodiversity: the cost of a physical footprint

Environmental controversy across infrastructure concentrates on industrial accidents, water pollution, and biodiversity, and hydropower leads the third, outright, because dams require the permanent conversion of river systems and the land around them. Mexico's Federal Electricity Commission won environmental approval in September 2014 for the Las Cruces dam on the San Pedro Mezquital, upstream of Marismas Nacionales, a Ramsar-protected wetland, even though the project's own impact statement conceded that the damage to Indigenous ceremonial sites could not be mitigated. Along the Mekong River, river health and fish populations fell as dam construction spread through the basin. In Brazil, the Doce River carried a mass release of toxic material after an upstream failure. Elsewhere, the record includes violations of the Endangered Species Act and documented disruption to rainfall patterns.

At Belo Monte, the consequences have been measured rather than projected. The plant diverts water into a canal that bypasses a 130-kilometer stretch of the Xingu known as the Volta Grande, which has received less than 30% of its natural annual discharge since 2019, and some 86% of the stretch's seasonally flooded vegetation, 30,748 of 35,600 hectares, can no longer be inundated at all. The gap lies in the regulator's own file: IBAMA's technical staff called for 10,900 cubic meters per second in February, the historic peak month, compared with the 1,600 that the operating regime actually releases. Seven years of underwater video survey data published in Scientific Reports recorded total fish species richness falling from 62 to a post-operation average of 51, with the steepest losses near the dam and in the rocky rapids, which hold roughly 2.6 times as many species as sandy reaches. The zebra pleco, whose entire known range lies inside the dewatered stretch, now sits on Brazil's national list of threatened species as critically endangered.

None of this is an accident or a failure of operation. It is a structural consequence of the asset. A well-run dam still floods a valley, and a dam engineered specifically not to flood one still dewater the river below it.

When engineering fails: hydropower's physical risk profile

Coal mining leads infrastructure on industrial accidents, where the record is dominated by human tragedy and safety negligence: explosions, collapses, fires, and repeated, incremental failures. Hydropower ranks second, but its accidents take a different form, because in this sector, industrial failure means catastrophic engineering failure at scale. The record includes pipe ruptures causing severe land erosion, oil leaks, and dam collapses that killed and displaced people across whole regions, while PG&E's settlement over damages to the Middle Fork American River Hydroelectric Project and the litigation still running in Brazil after dam collapses give a sense of the exposure a single event can generate. For anyone underwriting these assets, the distinction is financial as much as physical: a coal mine's safety record is a rising cost curve, while a dam's structural integrity is a low-probability, near-unbounded loss.

At Belo Monte, that exposure has so far been financial. The project was budgeted at R$28.9 billion when Brazil's development bank approved a then-record R$22.5 billion loan in November 2012, and by late 2017, actual investment had reached R$38.6 billion, roughly 34% over. The operator owed R$28.3 billion to lenders and debenture holders at the end of 2024. Aliança Norte Energia Participações, the Vale and Cemig vehicle holding a stake in the project, discloses a possible loss of R$3.05 billion from a single construction-delay claim and describes the operator's liquidity as its principal point of attention and a source of investor alert. Neoenergia wrote off its own 10% holding by R$482 million in the fourth quarter of 2021.

The physical risk has been closer than the absence of a collapse suggests. In October 2019, the operator wrote to the national water regulator declaring an emergency, because reservoir levels had fallen far enough to expose an unprotected section of the Pimental dam's earthfill base to wind-driven wave erosion and, in the company's own words, structural damage. It cut outflow below the level agreed with the environmental regulator to protect the structure, and the letter surfaced only through investigative reporting.

Beyond the environment: displacement, water, and chronic corruption

Right to property

Hydropower ranks first among infrastructure sectors for property disputes, a direct function of the footprint a dam and reservoir require. The record shows land seizures, forced displacement, compensation that arrives short or not at all, communities never consulted before ground was broken, and blasting that cracked the foundations of nearby homes. Those affected are frequently the least equipped to hold an operator to account.

Fifteen years after Belo Monte broke ground there is still no audited count of who lost their homes. Estimates run from 20,000 to 40,000 depending on the definition used, against the operator's account of rehousing some 6,000 urban families. Landowners say expropriations are priced at unadjusted 2013 values while the project's own construction boom inflated the market, and as of 2025 none of the land required for the riverine resettlement program had been bought. A petition filed with the Inter-American Commission in 2011 still has no ruling.

Community health and safety

Hydropower sits alongside coal and nuclear as a leading source of community health disputes, but it arrives by a different route. Coal delivers PM2.5, nuclear delivers radioactive anxiety, and hydropower delivers water mismanagement: overconsumption that strips farmers of a livelihood, contaminated water reaching local crops. The grievance is agricultural rather than industrial, which widens the affected population considerably.

On the Volta Grande, catch per fisher fell from 11.1 kilograms a day between 2001 and 2008 to 4.53 kilograms between 2020 and 2023. A randomized household survey found 38.5% of residents in Belo Monte's resettlement neighborhoods living with moderate or severe food insecurity, against 28.3% across the surrounding city. In June 2026, federal prosecutors sought as interim relief for 635 families along the reduced-flow stretch the emergency delivery of three and a half to five liters of drinking water per person per day.

Corruption and bribery

Corruption and bribery accounts for close to 30% of governance controversy across infrastructure. What separates hydropower is the pattern. In airports, nuclear, and coal, corruption surfaces as discrete scandals: a probe opens, executives are charged, attention fades. In hydropower it keeps returning, tied repeatedly to falsified records and payments to local officials to secure land and water rights. Isolated scandals point to isolated actors. A pattern that recurs points to how these projects get permitted.

Brazilian prosecutors alleged that Belo Monte's construction contracts carried bribes worth 1% of their value, and three contractors admitted cartel conduct and kickbacks under leniency agreements that carried immunity. Everything after that was procedural closure rather than a finding of liability: the principal defendants were acquitted and the acquittal upheld on appeal in 2024, the competition authority archived its bid-rigging case in 2025, and no individual has been convicted in connection with the project. An investor screening for enforcement outcomes would have found a closed file. The costs landed elsewhere, in permitting delay, financing conditions, and a minority stake that has been for sale since 2022 without a buyer.

Hydropower's risk concentration: what this means

Hydropower's classification as clean energy is accurate on the metric it was designed to measure, because generation is low-carbon. But carbon intensity is one dimension of sustainability, and it is not the dimension that produces operational friction, legal exposure, or the loss of a social license.

What drew sustained opposition to these projects was water rights, displaced communities, cracked foundations, converted wetlands, and permits secured through local payments. None of it appears in a carbon accounting framework.

For investors, insurers, and lenders seeking transition-aligned infrastructure exposure, that is a material blind spot: an asset class that screens well on the primary criterion while carrying the heaviest social burden in the dataset, and carrying it on behalf of people who have no employment relationship with it. Belo Monte was engineered to avoid precisely that outcome and produced it regardless, which suggests the exposure is not a function of how a dam is built but of what a dam is.

The label is not wrong. It is simply measuring something other than risk.

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In private equity, as in most industries, decision-making counts on accessing accurate and valuable information. However, these firms often encounter significant challenges when sourcing reliable data, especially when dealing with small, private companies. This article dives into the complexities of identifying high-quality information on smaller companies and underscores its value in investment decisions, operational efficiency, and risk management. It also explores how advanced artificial intelligence (AI) technologies are revolutionizing the identification of these risks, leading to higher rewards and more secure investments, thus providing a competitive edge.

The challenge of identifying valuable information for Smaller Firms

Lack of valuable data

Sturgeon's Law, which states that "Ninety percent of everything is crap (or noise)," becomes particularly relevant in the context of data sourcing. For private equity and investment firms focused on small companies, finding the golden nuggets of information amid the overwhelming amount of digital noise can be daunting. The data available on these companies is often sparse, fragmented, and difficult to uncover using conventional methods. This scarcity of reliable information makes it challenging for private equity firms to make informed decisions, heightening the risk of overlooking critical issues that could impact their investment process.

The difficulties extend beyond just locating information. Many small companies operate without a significant online presence or may not be required to disclose as much information as publicly traded firms. This lack of transparency can further blur critical data points. Furthermore, the data that is available is often unstructured, residing in various forms such as social media posts, obscure local news articles, or industry-specific reports. Extracting meaningful insights from these disparate sources requires sophisticated data processing capabilities, which traditional methods often lack. As a result, private equity firms are left with a significant challenge: how to separate valuable data from the noise without missing critical risk indicators, thereby optimizing their deal sourcing and investment strategies.

Diverse language and terminology

Smaller firms frequently face existential risks, and the potential rewards for identifying these risks early on can be significant for the private equity firms that invest in them. However, mainstream methods of risk identification often fall short, as these companies may not use standardized language to describe materiality. Instead, risks are discussed in varied and context-specific ways, complicating the task of recognizing relevant information. Therefore, it is essential to adopt a specialized approach that analyzes and decodes these firms' unique terminologies and business idiosyncrasies, ultimately translating them into a standardized language that can be effectively used in risk assessment.

The diversity in language is not just a barrier to risk identification but also to the communication of these risks within and between private equity firms. When a small firm uses industry-specific jargon or localized expressions to describe potential threats, it can lead to misunderstandings or underestimations of the actual risk. For instance, a manufacturing startup in a developing country might describe supply chain disruptions in terms that do not translate easily to a global investor’s risk framework. Additionally, cultural differences in how risk is perceived and reported can lead to further complications. This linguistic diversity necessitates the use of advanced natural language processing tools that can interpret data through a common lens while considering industry-specific contexts. For an insurance company, understanding financial models, insurance principles, and regulatory frameworks is crucial. Conversely, assessing risks for a beauty company requires a focus on product safety, consumer preferences, and market trends. By appreciating the specific contexts of each industry, private equity firms can better identify and evaluate potential risks, enhancing decision-making processes, risk and portfolio management strategies, and operational efficiency.

The dynamic nature of the industries themselves further complicates the challenge. For example, the tech industry evolves rapidly, with new risks emerging as technologies develop and consumer expectations shift. What might be considered a negligible risk today could become a significant issue tomorrow as regulatory landscapes, market conditions, and technological advancements alter the playing field. In contrast, industries like agriculture or real estate might have more stable risk profiles but are subject to sudden changes due to environmental factors or policy shifts. This variability across industries means that a one-size-fits-all approach to risk assessment is inadequate. Private equity firms must adopt flexible, industry-specific risk models that can adapt to the unique characteristics and evolving landscapes of the sectors they invest in, thus optimizing their AI capabilities.

The Power of AI in Enhancing Risk Management in Small Firms

AI technologies, particularly natural language processing (NLP) and machine learning algorithms, are important tools for private equity firms aiming to monitor and manage risks in small firms. These technologies can sift through vast amounts of data, extracting the valuable 10% and identifying patterns, trends, and subtle nuances in the language used to describe risks. By detecting these patterns, AI can reveal potential risks that might not be immediately apparent through traditional methods. This proactive approach to risk identification allows firms to address issues before they escalate, providing a more comprehensive and nuanced understanding of the risks facing small firms.

AI's ability to process unstructured data is particularly valuable in this context. Many of the risks that small firms face are discussed informally in places like social media, niche blogs, or local news outlets. Traditional risk management tools might overlook these sources, but AI-powered tools can analyze them in real-time, detecting emerging threats as they develop. Moreover, AI can cross-reference these insights with structured data from financial reports, regulatory filings, and other formal documents to create a holistic risk profile. This multidimensional analysis helps private equity firms not only identify risks but also understand their potential impact, enabling more informed, data-driven decision-making that enhances operational efficiency and competitive edge.

Beyond risk identification, AI also enhances risk mitigation strategies. By continuously monitoring data and learning from new information, AI systems can adapt to changing conditions, offering updated risk assessments that reflect the latest developments. This dynamic approach allows private equity firms to stay ahead of potential issues, making it possible to implement preventative measures rather than reacting to crises after they occur. In this way, AI capabilities contribute significantly to the optimization of risk management processes.

How SESAMm’s Advanced Technology Enhances Risk Assessment

SESAMm’s TextReveal® is at the forefront of this technological revolution, enabling private equity firms to efficiently navigate the vast digital landscape and extract the crucial information needed for informed decision-making. Through our proprietary data lake amounting to over 25 billion online articles with 15 years of historical data and our AI algorithms, TextReveal® can quickly identify and retrieve valuable insights, even when the information is deeply buried or highly specific. The tool's ability to analyze and understand the diverse language and terminology used in discussions about risks on the web empowers private equity firms to objectively assess the materiality of certain risks or identify emerging threats that have yet to be formally recognized.

TextReveal® goes beyond merely identifying risks—it categorizes them, providing context that helps private equity firms understand the severity and relevance of each risk. For example, if a small biotech firm is mentioned in discussions about regulatory hurdles, TextReveal® can determine whether these mentions are isolated incidents or part of a broader trend. It can also assess whether the language used suggests an imminent threat or a longer-term concern, enabling firms to prioritize their responses accordingly. Additionally, TextReveal® integrates sentiment analysis, which can gauge the overall tone of discussions surrounding a company, offering further actionable insights into potential reputational risks.

SESAMm has developed a proprietary metric – the Intensity Score, which calculates an event's relevance based on its news coverage and sentiment. It uses negative sentiment, article dispersion, and empirical ESG risk measures to determine how likely an article is to represent a high-risk controversy. The Intensity Score gives TextReveal users a clear understanding of which events require their attention.

BioNTech

Users can also opt to receive email alerts for the more severe controversies, ensuring they’re always aware of significant risks.
In addition to the severity, controversies are also categorized by risk and sub–risk type, making it easy to analyze specific areas of concern.

Moreover, SESAMm's platform is designed to be intuitive and user-friendly, making it accessible to investment professionals who may not have a technical background. This ease of use ensures private equity firms can quickly incorporate AI-driven insights into their risk management processes without a steep learning curve. By streamlining the data analysis process, TextReveal® allows firms to focus on strategic decision-making, confident they have a comprehensive understanding of the risks and opportunities associated with their investments and portfolio companies. This level of operational efficiency and optimization is key to maintaining a competitive edge in the fast-paced world of private equity.

TextReveal’s Risk Assessment module enables deep company and thematic research in multiple languages through on-the-fly keyword searches. Users have full access to articles, sentiment analysis, and trending topics to get a complete understanding of the risks. We’ve even developed an AI Text Summary feature that provides a quick summary of a selected article, saving time and enabling a faster analysis.

In summary, the integration of AI tools and natural language processing technologies is transforming risk management in private equity, particularly for firms dealing with small, private companies. By leveraging these advanced tools, private equity firms can enhance their due diligence processes, better monitor risks and controversies, and ultimately make more informed investment decisions that lead to higher rewards and operational efficiency.

Reach out to SESAMm

TextReveal's web data analysis of over five million public and private companies is essential for keeping tabs on ESG investment risks. To learn more about how you can analyze web data or request a demo, contact one of our representatives.

CEO Sylvain Forté demonstrates TextReveal’s® latest features applied to supply chain and risk analysis on the main stage of FinovateEurope 2023 in London. He also presents a quick use case on the latest Silicon Valley Bank fallout.

Discover how TextReveal can help automatically analyze millions of companies based on web content at SESAMm.

Watch the full recording:

SESAMm has secured Series B2 funding, and I want to share my excitement about this milestone and reflect on the journey.

After months of hard work from our team, we closed on a €35 million Series B2 funding round led by Elaia and Opera Tech Ventures (BNPP), which followed a €7.5M B1 round led by New Alpha and Carlyle in January 2021. That means we can now pursue the next phase of SESAMm’s growth.

We want to accomplish many things with this investment, and I’m incredibly grateful to all of our investors, partners, and clients who helped us during this process. But before I talk about them and SESAMm’s future, I can’t help but think about how the company started.

Academic beginnings

It’s still hard to believe that SESAMm sprouted from an idea, an academic project, and three college students. I still remember Pierre Rinaldi asking me to develop the first algorithms that used social media to analyze financial markets while in school. Pierre worked at a bank trading desk while finishing his studies and doing a research internship at a behavioral finance lab, where the idea came up for the project. I studied engineering and was passionate about AI already, so I started coding, and the first project was born.

Eventually, this project outgrew my capabilities. The volume of data involved needed hardcore algorithms to process them, the kind of algorithms Florian Aubry could build. Florian was the best developer in my engineering school, and lucky for us, he was also my friend.

SESAMm founders
From left to right: COO Pierre Rinaldi, CEO Sylvain Forté, and CTO Florian Aubry.

Before long, we started renting servers, optimizing code, and building signals based chiefly on Twitter data, reproducing research papers that also used text data to generate sentiment indicators for financial markets. You could say that this project had become more than academic at this point. But it really only became a company once we won our first startup award.

The award that sparked the launch of a company

We participated in a competition organized by local incubator SEMIA with support from Société Générale as a way to gain credibility for the project, and would you know, we won. The award was monetary, and we could receive it on one condition: that we launch an actual company.

You probably already know that starting a business is a big decision, at least it was for Pierre, Florian, and me. After all, we were still in school, trying to finish our degrees and looking to start new careers. Our choices could be life-altering. Pierre, for instance, planned to take a full-time job at the bank he was working at.

In the end, we chose SESAMm. Pierre didn’t take the job at the bank. I did my end-of-study internship at SESAMm, which allowed me to focus on the company 100% while finishing my degree. And Florian dedicated all his time and energy to the startup, too. In other words, we were all in, and our school project had officially become a business.

Pressure to build

Setting up a company was relatively easy. Building a business, as it turns out, is way harder. How would we take SESAMm’s product and put it to work? Pierre’s contacts, that’s how.

One by one, Pierre called on everyone he knew from school that worked in trading, offering them a chance to be the first to test the latest AI innovation: using social media for financial analysis. Back then, Pierre had more of a sales role. He’s now COO and head of HR, in case you were wondering.

Our first client was a trader from London. He put $50,000 into a strategy based on SESAMm’s signals. With our signals, the trader grew his portfolio with such performance that he began to raise capital, including for a hedge fund. Ultimately, he was trading $50 million from the strategy. It was the kind of success and track record we needed in the finance industry to take SESAMm to the next level.

Compelled to move…literally

Having demonstrated success, an investment firm, a venture firm called Fonds Venture Numérique Lorrain, reached out to us with interest in investing in SESAMm’s first round of funding. But they had two conditions:

  1. SESAMm must move to Metz, a startup-friendly hub in the Lorraine region in France.
  2. SESAMm must seek additional funding from other investors.

At the time, SESAMm was based in Strasbourg because that’s where Florian and I studied. And for all intents and purposes, we hadn’t planned on moving.

Also, aside from Pierre reaching out to school acquaintances to try our solution, we had never pitched anyone to fund our company before. Pierre, Florian, and I faced a serious choice and challenge.

What did we do, and how did we fare?

We reached out to business angels and pitched numerous times while continuing to work on the product and starting to plan the first recruitments. We learned a lot during this initial period about how to structure a company and present it to investors. At the time, startups were less common in the region and using online content from YC and other accelerators was invaluable for us.

SESAMm’s headquarters is now in Metz, and we currently have offices in Paris, London, Tunis, New York, and Tokyo. We’ve also diversified our technology and clientele, particularly in ESG and corporate spaces. In other words, we faced the challenge, moved our headquarters, and grew and evolved a lot!

Signage at SESAMm’s first office
Our first sign, our first footprint, our first home office in Metz.

Series B2 funding round challenges

That brings us to the recent past. We’ve grown our company so much that it was time for another funding round, and the timing wasn’t the best. Our teamwork and ability to communicate effectively across regions and cultures were vital in tackling this latest fundraising round. We decided to run the entire process in-house as it was important to us to not only demonstrate our ability to do so but also to build our own VC relationships.

Moreover, we’ve had a unique previous fundraising experience. Having been funded by FinTech venture capital firm NewAlpha Asset Management and a large private equity firm like Carlyle Group Inc. in earlier rounds helped us improve our processes and standardize our communications. We felt ready for this challenge.

Frankly speaking, though, between the COVID-19 pandemic and the Russia-Ukraine war, startup funding had become a much bigger challenge. Things weren’t popping like they were a couple of years ago, so we needed to be cautious and conservative yet bold and compelling. Regardless, we dug deep and stuck with the plan, with a focus on our numbers and our vision.

To achieve our goal, we created an internal team, onboarded two advisors, and created a scalable process. The internal team included SESAMm’s C-suite: CEO (Sylvain Forté), COO (Pierre Rinaldi), CTO (Florian Aubry), CFO (Marie-Charlotte Deucher), CMO (Jorge Alvarez), and CDO (Eric Sionnet). Our advisors provided guidance around strategy, gave feedback, and supplied operational help when needed. And from our pitch deck variations to our storage and sharing policies, we established a fine-tuned communication and project management process to keep us on task and on time. It was a good process, and it really helped us manage things efficiently while maintaining control over day-to-day operations. I can’t say it enough: process is always the key.

Fundraising wasn’t easy, but the process was worthwhile. And all this to say that I couldn’t be happier to announce that SESAMm has wrapped up its Series B2 funding round. With this money, we plan to further expand into the U.S. and Asian markets and continue SESAMm’s exponential growth. We’ll also seek key talent to sustain this growth and support the development of our AI technology.

Pleased, grateful, and excited for what comes next

Today, SESAMm is a healthy and fast-growing company. And as COO, CTO, and CEO, respectively, Pierre, Florian, and I are now more than a scrappy group of students. We’re a crew with the support of many contributors, consisting of more than 100 employees across many offices and cultures. It’s because of the teamwork and collaboration that SESAMm is where it is today. And I’m immensely grateful for all our team’s effort and the tremendous support we get from clients, investors, and advisors every day.

SESAMmers gathered for a company retreat
Paris, France, September 2022: Many of the 100+ SESAMmers gathered for a company retreat.

Thank you for being a part of our journey, and see you at SESAMm’s next milestone. Cheers!

Read the press release: “SESAMm Raises €35M in Series B2 to Grow its ESG and Sentiment Analysis Business.”

AI | Text Analysis | Data Science

It’s 2023, AI Innovation is Here and Only Getting Better

January 12, 2023
5 mins read

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.

Illustration of a globe hovering above a robotic hand in a painted style
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.

ESG | NLP | AI

SESAMm’s Top 10 Blog Posts for 2022

December 21, 2022
5 mins read

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

SESAMm 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

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

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

Sylvain-Forte-presenting-at-Finovate-2022-resize

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

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

www.sesamm.comhubfsWhat 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

Illuminated Tesla sign at night

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

Introducing 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

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

Windmills on a dock with superimposing data points

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.

ESG | Risk Management | Sentiment Analysis

FTX’s Collapse a Warning to Investors? Can AI Technology Help Avoid Similar Loss?

December 14, 2022
5 mins read

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.

FTX and SBF mentions and sentiment over time chart
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?

FTX and SBF pre-bankruptcy mentions and sentiment chart
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.

FTX and SBF withdrawal analysis chart
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?

SBF co-mentions chart
Figure 4: Donations, SEC, and U.S. elections co-mentions with SBF.

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.

SBF governance risks over time chart
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.

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.

Crypto and FTX sentiment polarity chart
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.

FTX competitors sentiment polarity comparison chart
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.

FTX and competitors ESG risks by mention volume chart
Figure 8: FTX and competitors ESG risks by mention volume.

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).

Centralized vs decentralized exchanges mentions and sentiment over time chart
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.

Self-custody mention volume chart
Figure 10: Self-custody mention volume.

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.

Mt. Gox and crypto mentions and sentiment comparison chart
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.

ESG | AI

Walking the Talk: SESAMm’s ESG Manifesto

December 7, 2022
5 mins read

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.

ESG | Risk Management | Portfolio Monitoring

We Need to Talk About Climate Change, Greenwashing, and ESG

November 30, 2022
5 mins read

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.

Greenwashing mentions by industry chart

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.

Greenwashing mentions by sector and year chart

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.

ESG | Alternative Data | AI

How Successful Investors Are Using AI to Get ESG Data: A Quick Guide

November 16, 2022
5 mins read

Environmental, social, and governance (ESG) data.

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?

In the Journal of Applied Corporate Finance, Sakis Kotsantonis and George Serafeim share “Four Things No One Will Tell You About ESG Data.” Here’s a summary:

  1. ESG measuring, data, and how companies report them are inconsistent.
  2. Lack of benchmarking transparency undermines the reliability of peer performance ranking.
  3. ESG data providers deal with “data gaps” differently, and their gap-filling approaches could lead to significant discrepancies.
  4. 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.

Learn more about NLP.

What makes SESAMm better at extracting ESG data

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.

Learn more about data lakes.

2. SESAMm’s machine-learning process

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.

Learn more about knowledge graphs.

To learn how you can generate NLP-enhanced ESG data for your firm, or to request a demo, reach out today.

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