Private markets are changing fast. From new ESG regulations to advances in AI, the forces shaping investment decisions are multiplying.
Join Clarity AI and SESAMm as we explore the biggest shifts redefining private-market investing in 2026 and how data and technology are transforming due diligence, risk management, and deal flow.
Watch this replay to explore:
What shaped private markets in 2025 - and what’s ahead in 2026
How AI is transforming due diligence, risk monitoring, and value creation
What these changes mean for deal-flow, risk, and competitive advantage
Private equity deal teams and M&A teams are under constant pressure to move faster, screen more opportunities, and avoid costly blind spots. To meet this challenge, SESAMm is expanding its suite of AI Reports with a new offering: the AI-powered Deal Screening Report.
This latest addition gives deal teams the ability to conduct pre-commercial due diligence on any company or project in minutes, not weeks. Not only does it surface insights and risks on the target company, it also provides a full competitive and market analysis.
Faster, Smarter Deal Screening
Built for high-volume, time-sensitive deal environments, SESAMm’s Deal Screening reports act as an early-stage radar: surfacing hidden risks, growth signals, and market dynamics before significant expenses are incurred.
Delivering weeks of manual research in just hours, these reports provide a structured view of target companies to guide where deeper diligence should focus.
Key benefits include:
Faster prioritization: Eliminate weak targets early and shrink the funnel.
Sharper focus: Direct consultants and expert networks to areas that matter most.
Greater confidence: Make earlier, more strategic go/no-go decisions.
Lower costs: Reduce wasted hours and consultant spend on low-value deals.
Supporting the Full Diligence Cycle
Deal Screening reports fit seamlessly into every stage of the diligence process:
Pre-CIM: Shape hypotheses and spot risks before significant time and budget are invested.
During CIM review: Use SESAMm insights to challenge claims and guide expert calls.
Post-CIM: Monitor ongoing sentiment and emerging risks as diligence deepens.
By acting as a bridge to deep diligence, SESAMm ensures consultants and expert calls are focused on what truly matters, helping deal teams allocate resources more effectively and move forward with sharper decision confidence.
A Growing Suite of AI Reports
These new Deal Screening reports are the latest addition to SESAMm’s expanding portfolio of AI-generated reports. From ESG assessments to supply chain and business exclusion screenings, SESAMm provides scalable, AI-powered insights that help firms make faster, smarter, and more confident decisions.
Get Started Today
SESAMm is trusted by 7 of the top 10 private equity firms worldwide, such as Carlyle and Warburg, to deliver AI-powered intelligence at the speed of deal flow. Request a free trial of the Deal Screening report and experience how SESAMm can transform your early diligence process.
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.
Researching and analyzing investment opportunities can be challenging for asset management—private equity and hedge fund portfolio managers, researchers, and analysts—because, of course, you want to make sure that you're a good steward of your client's investments.
And when you find and source data, such as traditional or alternative data, you also want to make sure it's reliable and that the methods used to gather it are tried and true.
This article aims to give you an inside look into SESAMm's knowledge graph—one of the key reasons SESAMm's NLP-derived alternative data is reliable and trusted. We'll explain what a knowledge graph is, why it's important, how it works, and what makes SESAMm's knowledge graph unique.
What is a knowledge graph?
A knowledge graph is a digital representation of a network of real-world entities, the foundation of a search engine or question-answering service. This structured data model puts the schema in context through linking and semantic metadata, providing a framework for data integration, analytics, unification, and sharing. In other words, it's like a map and legend, with the legend labeling the concepts, entities, and events and the map connecting and identifying their relationships. These details are stored in a graph database and visualized as a graph representation, hence the term knowledge graph.
Fun fact: The expression, knowledge graph, gained popularity after Google used it in 2012 to name their semantic network.
Two types of knowledge graphs
There are two general types of knowledge graphs: open and private. Open knowledge graphs are open to the public. They're created and made available by organizations such as Wikidata, DBpedia, and Yago. Private knowledge graphs are often only used by organizations that create them, like Google, WolframAlpha, Facebook, and SESAMm (of course). Some offer them up for a fee or subscription, such as Crunchbase and OpenCorporates.
Why a knowledge graph is important
Knowledge graphs are important because they equip us with a model to see how everything relates from a big-picture view, creating new knowledge. Its benefits include:
Incorporating disparate data sources, avoiding data silos
From a data science and artificial intelligence (AI) perspective, knowledge graphs provide machine-readable details, adding context and depth to data-driven AI techniques such as machine learning. Using knowledge graphs and machine learning models together improves system accuracy and extends the range of machine learning capabilities for better explainability and trustworthiness.
How a knowledge graph works
The core of a knowledge graph is its knowledge model, a collection of interconnected descriptions of concepts, entities, events, and relationships known as an ontology. This model provides a framework for statements or taxonomy. Each statement consists of a subject, predicate, and object (Figure 1)—known as a triple model—and each subject or object is represented only once in the context of the other subjects and their relationships. For example, in this simple sentence, "The boy kicks the ball," The boy is the subject, and kicker is the predicate because he kicks the ball, the object.
Figure1: Apple is the subject, chief executive officer is the predicate, and Tim Cook is the object.
Likewise, each statement consists of three components: nodes, edges, and labels. A node, or vertice, represents an entity, which can be anything existing in the real world, such as a person, company, or object. For instance, in this example (Figure 2), Barack Obama is the subject node, Malia and Sasha are object nodes, and the edges, or relationships, are labeled as father or sibling, respectively.
Figure 2: How the relationships between nodes can be labeled.
What makes SESAMm's knowledge graph unique?
SESAMm uses open and private datasets with custom, curated information to create our proprietary knowledge graph. As a result, the knowledge graph is a vast map connecting and integrating over 70 million related entities and their keywords, relating each organization to its brands, products, associated executives, names, nicknames, and exchange identifiers in the case of public companies from a data repository made up of more than 18 billion articles and messages and growing.
The knowledge graph is updated regularly
Entities within the knowledge graph are updated weekly and tagged to ensure we correctly track their changes. For instance, the CEO of a company today might not be its CEO tomorrow. And brands might be bought and sold, changing the parent company with each sale. So, weekly updates within the knowledge graph ensure the system is aware of these changes.
NLP-driven accuracy
At SESAMm, named entity disambiguation (NED), a natural language processing (NLP) technique, 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, which limits the number of possible matches, requires frequent manual adjustments, and can't distinguish homophones.
SESAMm uses three other NLP tools to identify entities and create actionable insights: lemmatization, embeddings, and similarity. The lemmatization process normalizes a word into its base form (morphology) to help identify and aggregate entities. Embedding assigns the entity a numerical value to help analyze how words change meaning depending on context and understand the subtle differences between words that refer to the same concept. Similarity measures whether two words, sentences, or objects are close to one another in meaning.
SESAMm tailored its knowledge graph to find, extract, and analyze data about public or private entities, which isn't readily available from the web or standard rating firms. This unique implementation of a knowledge graph provides insights to give you an edge when researching, analyzing, and submitting recommendations to the portfolio manager or clients.
SESAMm's premiere platform, TextReveal®, allows you to leverage NLP-driven insights fully and receive high-quality results through data streams, modular API and dashboard visualization, and signals and alerts. It's perfect for many quantitative, quantamental, and ESG investment use cases.
Learn how SESAMm can support you in your investment decision-making and request a demo today.
The insurance sector continues to face mounting ESG scrutiny amid rising climate losses, digital vulnerabilities, and complex regulatory environments. Over the past three years, leading firms such as UnitedHealth Group, Prudential Financial, and AIG have faced increasing challenges related to governance oversight, social accountability, and environmental exposure. Climate-related issues have driven significant financial impacts, while increased regulatory intervention, particularly in healthcare and claims management, has underscored the cost of weak internal controls. Data breaches, legal disputes, and reputational controversies have further intensified the spotlight on insurers’ operational resilience and ethical standards. Collectively, these developments illustrate how ESG risks in the insurance industry are shifting from mere concerns to central strategic priorities.
What are the most pressing ESG challenges currently facing the insurance sector? Read on to find out.
UnitedHealth Group (UNH): Governance and Regulatory Scrutiny
UnitedHealth’s ESG risks have intensified amid ongoing investigations and governance controversies. Its $3.3 billion acquisition of Amedisys has led to an antitrust lawsuit, while its Medicare Advantage business is under investigation for federal fraud. Social controversies include reports that UnitedHealth used algorithms to shorten patient rehabilitation care and paid nursing home bonuses to limit hospital transfers, prompting inquiries from U.S. senators. With its stock declining nearly 30% amid these challenges, the insurer’s case highlights the growing regulatory and ethical scrutiny of healthcare-linked financial services.
Prudential Financial Inc.: Compliance, Cybersecurity, and Consumer Protection Risks
Prudential’s ESG controversies over the past three years reflect systemic issues in data governance, workforce management, and regulatory oversight. The company announced layoffs in different regions, as well as a data breach affecting over 25 million individuals, for which a $4.75 million settlement is available to cover claims. Additionally, the U.S. Department of Labor found that Prudential had illegally denied over 200 life insurance claims, which has impacted investor confidence. These events highlight the company's vulnerabilities in compliance, cybersecurity, and consumer protection.
American International Group (AIG): Climate Risks and Reputational Challenges
Over the past three years, AIG has faced increased ESG scrutiny regarding climate risks and fossil fuel underwriting, reporting a 39% decline in profit and over $600 million in losses from Hurricane Ian. Activists pressure AIG to withdraw coverage for the East African Crude Oil Pipeline due to environmental concerns. On the social side, AIG has been struggling with reputational fallout from protests marking the 15th anniversary of its bailout and allegations of sexual assault involving a senior executive. On governance, AIG has been dealing with legal battles ranging from disputes over firearm-related claims and post-M&A settlements to trade secret litigation, underscoring persistent operational and compliance risks across its global portfolio.
The recent controversies across major insurers reinforce a broader trend: the convergence of financial performance, regulatory compliance, and ESG integrity. For AIG, physical climate risk and fossil fuel exposure remain defining challenges; for Prudential, consumer data protection and fair claims practices are under scrutiny; and for UnitedHealth, governance lapses tied to healthcare operations threaten long-term trust. As the sector evolves under increasing public and regulatory pressure, insurers that strengthen transparency, ethical oversight, and risk governance will be best positioned to sustain credibility and competitiveness in an ESG-driven market.
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.
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