In a recent interview, CEO Sylvain Forté spoke with Fintech Global about how the SESAMm has evolved, the role of AI in transforming ESG risk detection, and what's next for the industry.

Can you tell us about SESAMm's origins and how the company has evolved since 2014?

SESAMm started with a focus on analyzing sentiment and reputational signals on social media to understand how narratives influenced financial performance. We began by working with asset managers and quickly realized that reputational risks were not only impacting public equities but were becoming critical in other asset classes.

As we evolved, we discovered a much broader blind spot in private markets. ESG and reputational data simply didn't exist for private companies, infrastructure projects, or suppliers, yet these made up a significant share of global exposure. This expansion was supported by Carlyle, a leading private equity firm and a SESAMm investor, which helped us deepen our expertise in private markets. Since then, we've expanded to supply chain and procurement use cases as well.

The name SESAMm stands for "open sesame." We were designed to unlock this missing layer of insight, opening access to critical ESG risks across public, private, and supply chain assets.

How is AI transforming the way financial institutions detect and understand ESG risks?

AI is a fundamental enabler of SESAMm's platform. We cover over 5 million companies, public and private, across more than 4 million sources, making it the largest private asset ESG coverage in the industry. This would be impossible to maintain with traditional manual workflows.

But our AI enables not just massive scale; it delivers quality. We aggregate documents, match events over multiple years, classify and score controversies properly, and do all of this in real time. There is no lag between detection and delivery.

We've gone further than data delivery. With AI agents, we've moved from surfacing insights to solving real problems. For example, for an insurance company, our AI agents don't just provide controversy data; they fully complete underwriting risk questionnaires. This is not just enhanced ESG; it's a workflow transformation.

Can you share an example where SESAMm caught an ESG signal that others missed?

One example is Loro Piana, an LVMH company, where we were able to surface early signals from regional news and filings before they became broadly visible.

For private markets, some of our clients, including major US private equity firms, have used our insights to stop due diligence processes early, saving millions of dollars by identifying major ESG risks at the pre-deal stage. These events were buried in local reports or niche sources and would have gone undetected using traditional tools. This is where SESAMm's multilingual, real-time coverage delivers immediate and material value.

How does SESAMm's global, multilingual scope lead to better ESG insights?

SESAMm's reach is a key differentiator. We process content from over 100 languages and 4 million data sources, including local news, NGO reports, legal documents, blogs, and more. For global private investment firms, this makes it possible to monitor hundreds of portfolio companies and thousands of credit lines every single day, without human intervention.

Due diligence becomes instant. Customers can type a company name into the platform and get immediate access to source data, scores, and an auto-generated report. This level of coverage and automation is only achievable with AI. Without multilingual processing, most of these risks would remain hidden in local languages and never surface in English datasets.

What role does Generative AI play in turning complex data into actionable intelligence?

GenAI allows us to go beyond raw data or filtered alerts. SESAMm now delivers fully automated ESG due diligence reports in under 30 minutes, complete with citations, severity levels, and multi-language source integration. These are dynamically built with real insights. The same applies to legal reports, private equity and M&A deal screening, secondary and credit due diligence, and more.

Teams no longer need to sift through events, tag them, write memos, or manually structure reports. We also accelerate regulatory workflows by automatically flagging potential significant harm, governance issues, and controversial activities, enabling clients to meet disclosure requirements more quickly and focus their time on high-impact actions.

SESAMm has announced partnerships with Clarity AI and Sayari. How do these enhance your offering?

These partnerships are both strategic and complementary. SESAMm offers comprehensive data coverage, particularly for private firms and complex events, which many partners utilize to enhance their own offerings. Sayari enhances the mapping of supply chains and tier-N suppliers. Clarity AI provides a full ESG analytics platform. SESAMm plugs into each of them, either as a source of controversy intelligence or to enrich end-user offerings.

In return, they help our clients go further, from data to action, whether through real-world audits, regulatory support, or deeper portfolio insights. The goal is to offer an ecosystem of ESG solutions that go beyond alerts and into implementation.

Why are financial institutions now treating ESG and reputational risk as strategic intelligence rather than just compliance?

The stakes are higher than regulation. Of course, frameworks like SFDR and PAI 10 are important, but firms come to us because they need actionable insight. Corporates monitor their suppliers and group entities. Investors track portfolio companies, screen for risk during deal flow, and respond to reputational events.

ESG controversies now have direct financial implications, influencing deal approvals, investment decisions, and valuations. This is especially true in private markets. We work with both LPs and GPs to integrate controversy detection into due diligence and ongoing monitoring. In many cases, ESG insights are the trigger for early engagement or the reason to walk away from a deal.

What do you see as the next frontier for AI in ESG and reputational risk?

The next frontier is clearly AI agents. SESAMm is already building and deploying agents that automate entire workflows, from detection and classification to full reporting. This includes ESG due diligence in 30 minutes, secondary screening, good governance screening, controversial activity screening, legal risk reports, and more.

For secondary funds, this means being able to screen hundreds of underlying assets in hours, not days. For compliance teams, it means automated risk reports with source evidence and scoring. For ESG professionals, this means saving time to focus on engagement, strategy, and impact, rather than data wrangling.

We believe AI agents will power the next generation of ESG intelligence, providing seamless, embedded, and action-ready solutions.

Click here to access the full ESGFintech100 2025 report.

Reach out to SESAMm

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

This is from SESAMm’s Deal Screening AI report. These reports are usually used by private equity deal teams and M&A teams to conduct pre-commercial due diligence on any company or project in minutes, and they contain insights and risks on the target company as well as a full competitive and market analysis.

ManTech International Corporation is a U.S.-based defense contractor specializing in cybersecurity, data analytics, and systems engineering solutions for national security and government agencies. Founded in 1968 and headquartered in Herndon, Virginia, the company operates globally. It primarily serves U.S. defense, intelligence, and homeland security clients, delivering technology services that support mission-critical operations such as cybersecurity protection, artificial intelligence and analytics, and intelligence, surveillance, and reconnaissance capabilities.

The broader defense and intelligence services market in which ManTech operates is expanding rapidly due to rising global defense spending, geopolitical tensions, and increasing demand for advanced digital and cyber capabilities. Within this context, ManTech has recently secured several growth signals, including a $200 million cybersecurity contract with the National Oceanic and Atmospheric Administration, partnerships to deploy secure AI technologies, and major analytics and systems contracts with the U.S. Army, reflecting continued demand for its technical expertise.

The report identifies several legal and reputational risks that could be relevant in a due diligence context. The most significant relate to labor and human rights issues, including allegations by former employees that the company confiscated passports and imposed hazardous working conditions under a U.S. Army contract in Kuwait, with a U.S. court allowing human-trafficking claims to proceed. Other previous issues include whistleblower retaliation claims linked to military contract billing practices and a civil fraud settlement involving misrepresentation of security clearance status. Environmental risks appear limited, with no major pollution or climate-related incidents identified.

In the competitive landscape, ManTech operates alongside firms such as Booz Allen Hamilton, Leidos, CACI International, SAIC, and General Dynamics Information Technology. While not the largest player in the sector, it is regarded as a capable provider of secure IT and cybersecurity solutions for classified government missions. Overall, the report concludes that ManTech benefits from strong demand for defense technology and cyber capabilities but faces reputational exposure primarily tied to labor practices and contract-related compliance risks.

Reach out to SESAMm

SESAMm’s AI Deal Screening Reports analyze web data across over five million public and private companies to help investors quickly identify legal, ESG, and reputational risks during due diligence. To learn more about how you can generate these reports or to request a demo, reach out to one of our representatives.

Financial and ESG insights begin with big data coupled with data science.

At SESAMm, our artificial intelligence (AI) and natural language processing (NLP) platform analyzes text in billions of web-based articles and messages. It generates investment insights and ESG analysis used in systematic trading, fundamental research, risk management, and sustainability analysis.

This technology enables a more quantitative approach to leveraging the value of web data that is less prone to human bias. It addresses a growing need in public and private investment sectors for robust, timely, and granular sentiment and environment, social, and governance (ESG) data.
This article will outline how the data is derived and illustrate its effectiveness and predictive value.

Content coverage and ESG data collection

The genesis of SESAMm’s process is the high-quality content that comprises its data lake, the source from which it draws its insights. SESAMm scans over four million data sources rigorously selected and curated to maximize coverage of both public and private companies. Three guiding criteria—quality, quantity, and frequency—ensure a consistently high input value.

Every day the system adds millions of articles to the 16 billion already in the data lake, going back to 2008. The coverage is global, with 40% of the sources in English (the U.S. and international) and 60% in multiple languages. The data lake, expanding every month, comprises over 4 million sources, including professional news sites, blogs, social media, and discussion forums.

The following tables illustrate SESAMm’s data lake distribution (Q1 2022):

Respect for personal privacy figures highly in the data gathering process. We don’t capture personal data, like personally identifiable information (PII), and respect all website terms of service and global data handling and privacy laws. SESAMm’s data also doesn’t contain any material non-public information (MNPI).

Deriving financial signals and ESG performance indicators

SESAMm’s new TextReveal® Streams platform applies NLP and AI expertise to process the premium quality content gathered in its data lake. This complex process involves named entity recognition (NER) and disambiguation (NED)—the process of identifying entities and distinguishing like-named entities using contextual analysis—and mapping the complex interrelationships between tens of thousands of public and private entities, connecting companies, products, and brands by supply chain, location, or competitive relationship.

Using SESAMm’s TextReveal Streams, this wealth of information is filtered to focus on four crucial contexts for systematic data processing, risk management, and alpha discovery:

• Sentiment covering major global indices: world equities (and Small Caps, Emerging), U.S. 3000, Europe 600, KOSPI 50, Japan 500, Japan 225
• Sentiment covering all assets and derivatives traded on the Euronext exchange
• Private company sentiment on more than 25,000 private companies
• ESG risks covering 90 major environmental, social, and governance risk categories for the entire company universe, which includes more than 10,000 public and more than 25,000 private companies with worldwide coverage

TextReveal Streams data sets and assessments are used by financial institutions, rating agencies, and the financial services sector, such as hedge funds (quantitative and fundamental) and asset managers, to optimize trade timing and identify new sustainable investment opportunities. Private equity deal and credit teams also use the data for deal sourcing and due diligence. Private equity ESG teams use it to manage initiatives like portfolio company environmental, social, and governance risk and reporting.

Methodology and technology for processing unstructured data

NLP workflow, from data extraction to granular insight aggregation

Data is continually extracted from an expanding universe of over four million sources daily. As it enters the system, it is time-stamped, tagged, indexed, and stored in our data lake to update a point-in-time history extending from 2008 to the present.
The source material is then transformed from raw, unstructured text data into conformed, interconnected, machine-readable data with a precise topic.

Mapping relationships between entities with the Knowledge Graph

At the heart of the text analytics process is SESAMm’s proprietary Knowledge Graph, a vast map connecting and integrating over 70 million related entities and their keywords. It’s essentially a cross-referenced dictionary of keywords, relating each organization to its brands, products, associated executives, names, nicknames, and their exchange identifiers in the case of public companies.

Entities within the Knowledge Graph are updated weekly and tagged to ensure changes are correctly tracked. The CEO of a company today, for example, may not be the CEO tomorrow, and brands may be bought and sold, changing the parent company with each sale. Weekly updates within the Knowledge Graph ensure the system is aware of these changes.

Named entity disambiguation (named entity recognition plus entity linking) is one of the NLP techniques used to identify named entities in text sources using the entities mapped within the Knowledge Graph universe.

At SESAMm, NED identifies named entities based on their context and usage. Text referencing “Elon,” for example, could refer indirectly to Tesla through its CEO or to a university in North Carolina. Only the context allows us to differentiate, and NED considers that context when classifying entities. This method is superior to simple pattern matching, limiting the number of possible matches, requiring frequent manual adjustments, and cannot distinguish homophones.

SESAMm uses three other NLP tools to identify entities and create actionable insights. These are lemmatization, embeddings, and similarity. Each is explained in more detail below.

Analyzing the morphology of words with lemmatization

News articles, blog posts, and social media discussions reference organizations and associated entities in various forms and functions. Lemmatization seeks to standardize these references so the system knows they mean the same thing.

For example, “Tesla,” “his firm,” “the company,” and “it” are all noun phrases that can appear in a single article and refer to a single entity. Even where the reference is apparent, it can take different forms. For example, “Tesla” and “Teslas” both refer to the same entity but have slightly different meanings (semantics) and shapes (morphology).

The lemmatization process standardizes reference shape (morphology) to facilitate identification and aggregation. Lemmatization is a more sophisticated process than stemming, which truncates words to their stem and sometimes deletes information.

Encoding context and meaning with word embedding

In NLP, embedding is a numerical representation of a word that enables its manifold contextual meanings to be calculated relationally. Embeddings are typically real-valued vectors with hundreds of dimensions that encode the contexts in which words appear and, thus, also encode their meanings.
Because they are vectors in a predefined vector space, they can be compared, scaled, added, and subtracted. An example of how this works is that the vector representations of king and queen bear the same relation to each other as the representations of man and woman once you subtract the vector that represents royal.

Using embedding is key to analyzing how words change meaning depending on context and understanding the subtle differences between words that refer to the same concept: synonyms. For example, the words business, company, enterprise, and firm can all refer to the same thing if the context is “organizations.” But they represent different things and even different parts of speech if the context changes.

In the phrase, “[Tesla] will be by far the largest firm by market value ever to join the S&P,” for example, one could replace the word firm with company or enterprise without affecting the meaning significantly. Contrast that with “a firm handshake,” where a similar substitution would render the phrase meaningless.

Also, words referring to the same concept can emphasize slightly different aspects of the concept or imply specific qualities. For example, an enterprise might be assumed to be larger or to have more components than a firm. Embeddings enable machines to make these subtle distinctions.

One advantage of using embedding is that it’s practical because it’s empirically testable. In other words, we can look at actual usage to determine what a word means.

Another advantage is that embeddings are computationally tractable. This understanding of a word’s definition allows us to transform words into computation objects to programmatically examine the contexts in which they appear and, thus, derive their meaning.

As lemmatization is an improvement on stemming, embeddings improve techniques such as one-hot encoding, which is close to the common conception of a definition as a single entry in a dictionary.

SESAMm uses the global vectors for word representation (GloVe) algorithm to generate embeddings. It’s an unsupervised learning algorithm that begins by examining how frequently each word in a text corpus co-occurs with other words in the same corpus. The result is an embedding that encapsulates the word and its context together, allowing SESAMm to identify specific words in a list and different forms of the listed words and unlisted synonyms.

GloVe is an extension of recent approaches to vector representation, combining the global statistics of matrix factorization techniques like latent semantic analysis (LSA) with the local context-based learning of word2vec. The result is an unsupervised algorithm that performs well at capturing meaning and demonstrating it on tasks like calculating analogies and identifying synonyms.

BERT is another algorithm used by SESAMm to generate embeddings. BERT produces word representations that are dynamically informed by the words around them. Google developed the technique, and it’s what’s known as a transformer-based machine learning technique, which means it doesn’t process an input sequence token by token but instead takes the entire sequence as input in one go. This technique is a significant improvement over sequential recurrent neural network (RNN) based models because it can be accelerated by graphics processing units (GPUs).

SESAMm uses BERT for multilingual NLP of its extensive foreign language text because it has been retained using an extensive library of unlabeled data extracted from Wikipedia in over 102 languages. BERT model was trained to predict words from context and next sentence prediction where it was trained to predict if a chosen following sentence was probable or not given the first sentence. As a result of this training process, BERT learned contextual embeddings for words. Due to this comprehensive pre-training, BERT can be finetuned with fewer resources on smaller datasets to optimize its performance on specific tasks.

Linking words, sentences, and topics with cosine similarity

Cosine similarity with centered means it’s identical to the correlation coefficient, which highlights another element of the computational tractability of the embeddings approach. It makes it easy to compare words and contexts for similarity.

Converting words to vector representations means we can quickly and easily compare word similarity by comparing the angle between two vectors. This angle is a function of the projection of one vector onto another. It can identify similar, opposite, or wholly unrelated vectors, which allows us to compute the similarity of the underlying word that the vector represents.

Two vectors aligned in the same orientation will have a similarity measurement of 1, while two orthogonal vectors have a similarity of 0. If two vectors are diametrically opposed, the similarity measurement is -1. In practice, negative similarities are rare, so we clip negative values to 0.

Cosine similarity measures whether two words, sentences, or corpora are close to one another in vector space or “about” the same thing in semantic space. To answer the question, “Is this sentence referencing company X?” we embed the sentence using the process described above and compute the cosine similarity between the sentence and the embedded company profile. Analogously, we compute similarities between sentences and the ESG topics SESAMm monitors by taking the maximum similarity between a sentence and each embedded keyword associated with an ESG topic.

These similarities allow us to identify whether a sentence references fraud, tax avoidance, pollution, or any other ESG risk topic among the more than 90 that SESAMm tracks across the web.

Similarities within ESG topics combine with word counts to resolve the recall and precision problem. Word counts are precise because if a word is identified within a context, then that context, by construction, references the topic.

The virtue of using these NLP techniques is that even if a given keyword list does not include every possible combination of words that a person might use to discuss a topic, relevant entities missed by the word-count process will be identified through vector similarity.

This is the power of SESAMm’s NLP expertise. We can scan many lifetimes’ worth of data in seconds to find the concepts you explicitly ask for and the concepts relevant to your search but that you did not think of yourself.

Sentiment analysis with deep learning and neural networks

Once we’ve identified the concepts and contexts of interest in all the forms they appear, we analyze the context to determine the speakers’ attitudes.

We use sentiment classification models to score a sentence with three possible outcomes: negative, neutral, or positive. The current classification models are based on deep learning AI technologies. Specifically, we stack convolutional neural networks with word embeddings and bayesian optimized hyperparameters—parameters not learned during training. This architecture improves the accuracy and enables fast shipping of production-ready models for a given language. We also produce state-of-the-art frameworks with architecture variations enabling multilingual capabilities, such as transformers and universal sentence encoders.

Condensing information and extracting insights with daily aggregation

Similarities, embedded word counts, and sentiment are state-of-the-art tools for processing unstructured text data. The same tools are effective cross-linguistically.

Once the information has been extracted from millions of data points, it’s aggregated and condensed into actionable insights.

All entities are referenced directly or indirectly within an article. Then, sentence-level references are aggregated to obtain an article-level perspective, and finally, all relevant articles are aggregated to gain an entity-level view of that day.

In this way, reams of data are compressed into several metrics to provide a daily aggregate view for each entity, highlighting trends at a sentence, article, and entity-level comparable over a multi-year history.

ESG analysis use cases

SESAMm’s TextReveal Streams is used in various investment domains, from asset selection to alpha generation and risk management. Systematic hedge funds track retail interest in real time to identify investment opportunities and protect their existing positions. In the Private Equity industry, equity and credit-deal teams use the data in various ways, from monitoring consumer perspectives via forums and customer reviews for evaluating deal prospects to estimating due diligence risks, all to help make investment decisions. Dedicated teams use our data for monitoring portfolio companies for ESG red flags that conventional ESG reporting might miss.

Below are two examples of how aggregated TextReveal Streams data can be used to help identify investment risk and opportunity.

LFIS CapitalL: ESG signals for equity trading

ESG controversies can significantly impact asset prices in the short term, and it’s now estimated that intangible assets, including a company’s ESG rating, account for 90% of its market value.

Working in partnership with LFIS Capital (LFIS), a quantitative asset manager and structured investment solutions provider, SESAMm developed machine learning and NLP algorithms that could analyze ESG keywords in articles, blogs, and social media, to generate a daily ESG score specific to each stock, which is part of the TextReveal Streams’ platform’s core functionality.

The results were promising when these scores were incorporated into a simulated strategy for trading stocks in the Stoxx600 ESG-X index.

A simulated long-only strategy running between 2015 and 2020, using the signals, delivered a 7.9% annualized return, 2.9% higher than the benchmark for similar annualized volatility (17.3% vs. 17.1%). The information ratio of the strategy was greater than 1, with a tracking error of 2.8%. Results for the previous three years were compelling, reflecting the growing interest and news flow around ESG themes.

Researchers also backtested a hypothetical long-short strategy for all stocks in the Stoxx600 ESG-X index with a market cap of over $7.5bn. This investment strategy delivered a Sharpe ratio of approximately 1 with annualized returns and volatility of 6.1% and 5.9%, respectively, between 2015 and 2020. Like the long-only strategy, returns were particularly robust over the three years up to 2020: +6.0% in 2018, +7.3% in 2019, and +11.3% in 2020.

Finally, a simulated “130/30” ESG strategy that combined 100% of the long-only ESG strategy and 30% of the long-short ESG strategy delivered a 10.8% annualized return, 5.8% higher than that of the Stoxx600 ESG-X index. Annualized volatility was similar at 16.9% vs. 17.1%. The strategy experienced a tracking error of 3.8% and an information ratio of over 1.5, with a consistent outperformance each year.

Disclaimer: Past performance is not an indicator of future results. Theoretical calculations are provided for illustrative purposes only. The investment theme illustrations presented herein do not represent transactions currently implemented in any fund or product managed by LFIS.

Read more: “How Alterntive Data Identifies Controversies Before Mainstream Sources With Examples”

Wirecard: ESG sentiment and volume as predictive indicators

The Wirecard scandal broke on June 21, 2020, when newswires carried the story that the major German payment processor had filed for bankruptcy after admitting that €1.9 billion ($2.3 billion) of purported escrow deposits did not exist.

Could SESAMm’s TextReveal Streams platform have provided investors with an early warning that the scandal was about to break?

The following chart derived from the platform shows how key ESG metrics, including ESG scores (volumes) and ESG scores (sentiment), reacted to the news.

An analysis of the charts pinpoints a shallow rise in the ESG scores (volumes) time series in the early part of June before the eruption on June 21.

The ESG scores (sentiment) metric also shows a steady increase in negative sentiment for governance, the most relevant of the three ESG factors regarding the scandal.

Additionally, before the crash, governance was the most negative of the three ESG factors most of the time. This was especially the case from late March to early April, and then before the scandal in early June, negative governance sentiment diverged higher from the other two.

The rate-of-change of negative governance sentiment as it rose and peaked in early June before the scandal broke was also extremely high, perhaps providing the basis for an early warning signal.

Portfolio managers who had been keeping an eye on the reputational slide in Governance for Wirecard may have decided the company was at high risk of a negative controversy emerging, giving them cause to drop the stock before the event.

In this way, it can be seen how while not providing a hard and fast early warning signal, SESAMm’s ESG scores can, nevertheless, be used as the basis for developing a data-driven, rules-based portfolio management approach that can help investors avoid high-risk candidates like Wirecard.

SESAMm takes on ESG data challenges

SESAMm’s NLP and AI tools analyze over four million data sources daily to identify thousands of public and private companies and their related products, brands, identifiers, and nicknames, turning reams of unstructured text into structured and actionable data.

SESAMm’s TextReveal Streams platform can be used in many quantitative, quantamental, and ESG investment use cases. TextReveal is a solution that allows you to fully leverage NLP-driven insights and receive high-quality results through data streams, modular API and dashboard visualization, and signals and alerts.

Learn how SESAMm can support you in your investment decision-making and request a demo today.

To request a demo or for access to the full SESAMm Wirecard or LFIS reports, contact us here: