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.

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.

U.S. banks have dramatically increased fossil fuel financing in a notable contradiction with the narrative established after COP26. According to the 2025 Banking on Climate Chaos report, compiled by the Rainforest Action Network and its partners, global banks significantly scaled up their support for the fossil fuel industry in 2024, with a staggering $162 billion increase, pushing total financing to $869 billion.

U.S. institutions are at the forefront of this backslide. JPMorgan Chase, Bank of America, Citigroup, and Wells Fargo accounted for one-third of global fossil fuel financing, approximately $289 billion. JPMorgan alone provided $53.5 billion, a 35% rise in funding that placed it at the top of the global list. Bank of America and Citi each contributed over $44 billion, while Barclays led among European banks, increasing its lending by 55% ($35.4 billion).

Why the Sudden Surge?

This resurgence coincides with the political shift in the U.S. following the Trump administration’s departure from the Paris Agreement and weakened climate policies. In parallel, several major banks have exited the Net-Zero Banking Alliance, prompting environmental groups to accuse them of “walking away from climate commitments.”

What This Means for Climate Risk

The spike in fossil fuel financing carries profound implications. First, it increases banks’ exposure to climate liability risk. A Financial Times analysis cites growing concerns that banks may face litigation due to their financing practices in relation to climate change. Second, funneling money back into carbon-intensive sectors undermines global efforts to limit warming to 1.5 °C; long-term goals rest on systemic transitions away from fossil fuels.

Public Relations vs. Funding Reality

Banks have defended their actions by emphasizing fossil fuels and clean energy investments. JPMorgan, for instance, claims it invested $1.29 in green energy for every dollar in fossil fuel financing. Nevertheless, critics argue that green financing claims ring hollow when fossil fuel funding is simultaneously ramping up.

Rebuilding Credibility in Sustainable Finance

The disconnect between words and actions is a challenge for the financial sector. With growing scrutiny on climate claims, stakeholders demand greater transparency and accountability. Greenwashing has evolved from a reputational issue to a regulatory one, impacting trust and market access. Banks that emphasize climate commitments while increasing fossil fuel investments risk losing credibility. To maintain stakeholder confidence, a genuine transition to clean energy financing is crucial. Trust now hinges on consistent actions rather than just marketing promises, allowing us to build a sustainable future together.

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.

A familiar debate has followed ESG data for years. One camp argues that the field generates too much information for any human team to handle, so the work should be left to machines. The other argues that ESG judgments are too consequential to automate, so humans should review everything. Both positions contain a real concern. Neither describes how a credible rating is actually produced.

With the publication of its full Controversy Exposure Score methodology, now public and free to access following the entry into force of the EU ESG Rating Regulation on 2 July 2026, SESAMm is making the answer explicit. A trustworthy rating is not a choice between artificial intelligence and human expertise. It is the disciplined combination of the two, with each doing the part of the work it does best.

The Scale Problem Is Real

Teams that monitor ESG controversies rarely suffer from too little information. They suffer from too much. A single incident can generate dozens of articles within days, in multiple languages, across outlets of very different quality. Multiply that by a global investment universe and the volume becomes impossible to track by hand.

This is the part of the problem that machines are built for. SESAMm's pipeline ingests more than 10 million documents a day, drawn from an input layer of over 30 billion documents that includes licensed global news, public web and media feeds, NGO publications, and public regulatory and judicial filings. It screens controversies for millions of public and private companies, alongside infrastructure projects, state-owned entities and sovereigns. No analyst team could read at that scale, and none should try. Asking people to do machine work is how important signals get missed.

So artificial intelligence carries the load. Natural language processing and machine learning models, including large language models, attribute documents to the right entity, filter for genuine ESG relevance, and group related articles into discrete events and events into continuous cases. This is what allows a controversy that unfolds over weeks to be tracked as one developing story rather than a hundred disconnected headlines.

Why Scale Alone Is Not Trust

A system that reads everything will also, inevitably, misread some of it. SESAMm is direct about this in its methodology, because pretending otherwise would be the opposite of transparency.

Probabilistic language models can misinterpret a historical or hypothetical reference as an active controversy. Automated clustering can occasionally merge two distinct incidents or split one prolonged crisis into fragments. Model accuracy varies across languages, and lower-resource languages or heavily idiomatic content raise the risk of misclassification. These are structural properties of statistical systems, not bugs to be wished away.

This is precisely where scale stops being enough and human expertise becomes indispensable. A number that informs how capital is allocated cannot rest on automation alone.

Where Human Judgment Enters

SESAMm operates a dual-layer human quality-assurance process, and it runs every day.

At the first layer, a dedicated Research and Analytics quality-assurance team reviews data accuracy, both reactively, when a question is raised about a case, a score or a classification, and proactively, by reviewing generated alerts. Where an issue is confirmed, the correction, whether a reattributed entity, a corrected sub-risk tag or the removal of an irrelevant event, is applied at the source, logged, and the affected scores are recomputed on the standard daily cycle.

At the second layer, complex cases and recurring structural issues are escalated to the Methodology Lead, who can update the underlying training corpus so that a category of error becomes less likely in future. This is the detail that matters most. Human review is not a final rubber stamp on top of the machine. It is a feedback loop that teaches the system, so that today's corrections improve tomorrow's automated output.

The same expertise sits at the front of the process, not only the end. Analysts define the 44 ESG sub-risks, design how severity is assessed, and fine-tune the models. The methodology is a human construction that machines then apply consistently at scale.

A Division of Labor, Not a Contest

Seen this way, the old debate dissolves. The question was never whether AI or people should produce ESG ratings. The question is which part of the work belongs to which.

Machines provide reach, consistency and speed. They apply the same rules to every entity, every day, without fatigue or favor.

People provide judgment, correction and improvement. They decide what the system should look for, they catch what it gets wrong, and they raise the standard of the model over time.

The result is a rating that is both broad enough to cover the real world and rigorous enough to be relied upon. Scale without rigor is noise. Rigor without scale never reaches most of the companies an investor actually holds. The value is in the combination.

What This Means Going Forward

The EU ESG Rating Regulation asks providers to disclose how their ratings are built. SESAMm has chosen to disclose the full pipeline, including the role of AI, the points where it can fail, and the human controls that contain it. The aim is not to claim the technology is flawless. It is to show, in detail, why the output can be trusted anyway.

As artificial intelligence becomes more capable, the temptation to remove the human layer will grow. SESAMm's position is the opposite. The more powerful the models become, the more valuable the people who direct them, check them and teach them become. That is the architecture of a rating worth trusting, and it is now open for anyone to read.

To explore the full methodology behind the Controversy Exposure Score, visit sesamm.com/methodology.