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:

The European Union faces a significant internal rift as its largest economies take opposing stances on the bloc's Corporate Sustainability Reporting Directive (CSRD) and CSDDD (Corporate Sustainability Due Diligence Directive), highlighting the delicate balance between environmental ambition and economic competitiveness in today's regulatory landscape.

A Continental Divide

According to recent reports, Germany and France—two of the EU's economic powerhouses—are pushing for a two-year delay to the CSRD implementation. This stance contrasts sharply with Spain and Italy, who advocate for maintaining the current timeline while potentially offering concessions to smaller businesses.

On one hand, at the Choose France summit on May 19, 2025, French President Emmanuel Macron called for the European Union to abandon the CSDDD, citing concerns over its potential impact on European competitiveness. Macron's stance aligns with German Chancellor Friedrich Merz, who also advocates for the law's repeal, arguing that it imposes excessive burdens on businesses, especially amid global competition from the U.S. and China. While some EU member states and industry leaders support revising or delaying the directive, others, including left-wing politicians and NGOs, defend it as essential for upholding European values and sustainability goals.

Spanish Environment Minister Sara Aagesen and Economy Minister Carlos Cuerpo, on the other hand, emphasized in a letter to the European Commission that sustainability reporting "supports the values and the priorities of the EU even beyond our borders, setting an example of leadership." Meanwhile, Italy's finance minister Giancarlo Giorgetti specifically urged against delaying CSRD for the tens of thousands of companies already preparing to report this year.

Why France and Germany Are Pushing Back: The Competitive Concerns

The Franco-German resistance to the current CSRD and CSDDD timeline stems from several key economic and practical concerns:

Administrative Burden on Businesses

German Finance Minister Jörg Kukies highlighted that approximately 13,000 German companies would be subject to extensive reporting requirements, with each needing "to provide around 1,000 data points or at least explain why fewer are provided." This represents a massive administrative undertaking, particularly for smaller enterprises with limited resources.

Economic Fragility and Global Competition

France's pushback comes amid broader economic concerns. The French government described the CSRD rules as "hell for companies," reflecting anxiety about imposing additional costs during a period of economic vulnerability. Both countries fear that excessive regulatory requirements could further weaken their competitive position against less-regulated economies, particularly the United States under the Trump administration, which has shown hostility toward environmental regulations.

Overlapping Regulatory Frameworks

German officials have pointed to the problem of multiple, uncoordinated sustainability reporting regimes. Kukies noted that "every CFO could tell absurd stories about how the same data has to be reported multiple times,"  arguing for a more streamlined approach where "each data point only has to be reported once."

Specific Reform Proposals

The German government has proposed significant changes, including:

• Postponing the CSRD application deadline by two years (to 2027) for large companies
• Increasing company size thresholds for reporting from €50 million in turnover to €450 million, and from 250 employees to 1,000
• Eliminating sector-specific reporting standards that would create additional complexity
  

Implications for Businesses and Investors

This regulatory uncertainty creates significant challenges for businesses operating across the EU. Companies face difficult strategic decisions about whether to proceed with sustainability reporting preparations or wait for potential rule changes.

For investors, this division introduces several critical considerations:

1. Reporting Inconsistency: Different implementation timelines across EU countries could create a patchwork of disclosure standards, complicating investment analysis.
2. Competitive Impacts: Companies in countries maintaining stricter timelines may face higher short-term compliance costs than competitors in countries securing delays.
3. ESG Data Reliability: Delays could affect the quality and comparability of ESG data, potentially undermining investor confidence in sustainability metrics.
4. Strategic Positioning: Forward-thinking companies that continue sustainability reporting preparations regardless of potential delays may gain competitive advantages in attracting ESG-focused investment.

Looking Ahead

The European Commission plans to publish an "omnibus" proposal to simplify green rules for businesses, aiming to enhance competitiveness while responding to global regulatory pressures, including potential deregulation under a second Trump administration in the U.S.

This internal EU debate reflects a broader global tension between advancing sustainability standards and addressing immediate economic pressures. Navigating this evolving regulatory landscape will require flexibility, foresight, and a balanced approach to ESG integration for businesses and investors alike.

As this situation develops, stakeholders should closely monitor European Commission decisions and prepare for multiple regulatory scenarios across the EU's diverse economic landscape.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

PARIS, FRANCE, le 1er mars 2023 — SESAMm, leader du traitement automatique des langues, un domaine de pointe de l'intelligence artificielle, a annoncé aujourd'hui la clôture d'une levée de fonds en série B2 de 35 millions d'euros (37 millions de dollars US). L’objectif : accélérer sa croissance et son développement international.

Cette opération permettra à SESAMm de poursuivre son expansion notamment aux Etats-Unis et en Asie, et de soutenir ses développements technologiques en intelligence artificielle pour l’analyse ESG (critères Environnementaux, Sociaux et de Gouvernance des entreprises) et de sentiment. En complément, cette levée de capitaux permettra de recruter des talents clés en ESG, développement informatique et intelligence artificielle, ventes et marketing.

Ce nouveau tour de table a été mené conjointement par Elaia, une société de venture capital spécialisée dans la deep tech, et Opera Tech Ventures, fonds de capital-risque de BNP Paribas (BNPP). Cette levée de fonds bénéficie également de la participation du gestionnaire d'actifs Unigestion, de la banque Raiffeisen Bank International (RBI) à travers son entité de venture capital Elevator Ventures, d’AFG Partners, de CEGEE Capital ainsi que des investisseurs historiques de SESAMm, incluant Carlyle (CG) et New Alpha Asset Management, qui ont participé à la précédente série B1. Cette opération porte le total des fonds levés par SESAMm à 50 millions d'euros.

"Nous sommes ravis de soutenir les ambitions de SESAMm, qui exploite une technologie de pointe pour créer des données d’analyse et de suivi de tendances ESG. Nous sommes très impressionnés par la qualité et l’expertise de son équipe de direction, opérant d’ores et déjà  sur tous les continents et pour des clients institutionnels de premier rang au niveau mondial.

La vision d’apporter ces indicateurs ESG technologiques à l’industrie financière et aux grandes entreprises représente un réel changement de paradigme et nous sommes très heureux de faire partie de cette aventure aux côtés de l’équipe de SESAMm", a déclaré Pauline Roux, Partner chez Elaia.

"Dans un contexte où il est de plus en plus critique d’accompagner la prise de décision grâce à des sources de données pertinentes, nous avons trouvé le produit de SESAMm très efficace pour aider à identifier, filtrer et évaluer des informations clés, tant sur de petites sociétés privées que sur des plus grandes entreprises. Nous partageons pleinement la vision de son équipe et sommes très fiers de soutenir SESAMm dans son développement", a déclaré Thibaut Schlaeppi, Managing Director d'Opera Tech Ventures.

SESAMm est un fournisseur de données leader dans l’utilisation du traitement automatique des langues, et qui compte parmi ses clients les plus grandes sociétés de private equity, banques et gestionnaires d’actifs, ainsi que des entreprises de tous secteurs. Grâce à son data lake de plus de 20 milliards de documents, en croissance de 20 % chaque année, SESAMm fournit des données et technologies pour générer des analyses innovantes. Les cas d'utilisation sont multiples et incluent la détection de controverses, les scores réputationnels, les indicateurs ESG et ODD (Objectifs de Développement Durable), le due diligence d'investissement ou encore le suivi automatisé de fournisseurs.

"Depuis que nous avons commencé à travailler avec SESAMm en tant qu’investisseur et client il y a plus de deux ans, nous avons été impressionnés à la fois par la croissance de l'entreprise et par ses indicateurs de pointe qui ont appuyé nos processus de recherche de sociétés, de due diligence et de création de valeur pour les sociétés de notre portefeuille", a déclaré Matt Anderson, Chief Digital Officer de Carlyle. "Nous sommes ravis de renforcer notre partenariat avec SESAMm en participant à ce nouveau tour de table."

Le CEO de SESAMm Sylvain Forté, son COO Pierre Rinaldi et son CTO Florian Aubry ont cofondé SESAMm en 2014. Avec leur équipe de près de 100 experts de la donnée, ils travaillent sur de grandes quantités d'informations textuelles issues du web, des sites d'actualités aux rapports d’ONG en passant par les médias sociaux, pour les transformer en puissantes informations pertinentes et rapidement exploitables.

Sylvain Forté, CEO et cofondateur de SESAMm, a partagé: "Nous sommes heureux et reconnaissants d’avoir finalisé cette levée de fonds de 35 millions d'euros pour poursuivre notre croissance et nous étendre sur de nouveaux marchés internationaux tels que Singapour. Lever ce montant important dans des conditions de marché tendues est une validation supplémentaire de la pertinence des deux tendances clés qui sont au cœur de SESAMm : l'intelligence artificielle et l’ESG. Nos outils permettent ainsi à toutes les entreprises de prendre de meilleures décisions et de combler les manques de données, notamment dans l’ESG, sur les entreprises publiques et privées."

À propos de SESAMm

SESAMm est une société d'intelligence artificielle de premier plan, au service des sociétés d'investissement et des entreprises du monde entier. SESAMm analyse plus de 20 milliards de documents en temps réel afin notamment de détecter automatiquement les controverses sur les investissements, les clients et les fournisseurs, calculer des scores ESG et d'impact positif, améliorer le due diligence et le sourcing en private equity et analyser le sentiment sur les actifs financiers.

À propos d'Elaia

Elaia est une société de venture capital spécialisée dans le financement des entreprises technologiques en phase d'amorçage. La société se focalise sur les startups des secteurs du logiciel, du web et des médias numériques et a démontré sa capacité à soutenir des entreprises devenant performantes et rentables. L'équipe d'Elaia est composée d'investisseurs et d'entrepreneurs expérimentés qui apportent des conseils et un soutien précieux aux startups de leur portefeuille. En s'attachant à aider les entreprises à croître et se développer, Elaia est un partenaire de choix pour tout entrepreneur qui souhaite lancer une entreprise technologique.

À propos d'Opera Tech Ventures de BNP Paribas

Opera Tech Ventures est la branche VC de BNP Paribas, lancée en 2018 avec l'objectif d'investir dans des startups qui transforment l'industrie financière. Le fonds est géré par BNP Paribas Asset Management France, au sein de sa division Private Assets, dédiée à la gestion d'actifs privés. Avec une dimension globale, Opera Tech Ventures soutient les entrepreneurs qui construisent des entreprises ambitieuses, de la série A à la série C, avec des investissements allant de 3 à 15 millions d'euros.

À propos de Carlyle

Carlyle (NASDAQ : CG) est une société d'investissement mondiale possédant une expertise sectorielle approfondie et qui déploie des capitaux privés dans trois secteurs d'activité : Global Private Equity, Global Credit et Global Investment Solutions. Avec 373 milliards de dollars d'actifs sous gestion au 31 décembre 2022, l'objectif de Carlyle est d'investir judicieusement et de créer de la valeur au nom de ses investisseurs, des sociétés de son portefeuille et des communautés dans lesquelles nous vivons et investissons. Carlyle emploie plus de 2 100 personnes dans 29 bureaux répartis sur cinq continents. Pour plus d'informations, consultez le site www.carlyle.com. Suivez Carlyle sur Twitter @OneCarlyle.

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