Sylvain Forté, CEO and co-founder of SESAMm, presented the following at Finovate 2022. In the presentation, Sylvain explains who SESAMm is, what SESAMm does, including examples, and how it benefits our financial clients.

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Hi, everyone. Thank you very much for the opportunity to be with you today. I’m very glad to introduce you to SESAMm. I’m Sylvain, CEO and co-founder of SESAMm.

We’re an artificial intelligence company specializing in analytics for investment professionals and [corporations]. We basically extract billions of articles and messages from the web and transform them into actionable insights to make better decisions. We’re a team of close to 100 people now, and we generate insights from more than 20 billion articles and messages.

Immediate access to daily insights

Let me jump straight to the demo and give you a practical example of what we do. So imagine you’re, for example, a bank looking to compute environmental, social, and governance risks on your portfolio on your clients or on your suppliers. Right now, you may have access to ratings, which are updated once per quarter or once per year. We can give you access immediately to timely daily data on all of your companies in order for you to better assess risks and raise early warnings.

Wirecard use case

In this specific example (Figure 1), we look at Wirecard, a company that went bankrupt due to a 2 billion fraud scandal in Germany.

We extracted dozens of thousands of articles and messages on the company, and we can immediately see that there is a huge anomaly in terms of governance risk. The company is basically exposed to fraud accusations, to lawsuits, and the like, things that you don’t really want to see in your clients or your own portfolio.

Furthermore, we can see on this chart that we can get that type of indicator every single day. And we can see that six months prior to the company’s bankruptcy, there were already huge alerts actually here in January 2020, indicating that the company was in a pretty bad situation from the perspective of web content and web data from news to social platforms, blogs, and forums.

We really have the ability to compute live insights for ESG risk, sustainability monitoring, credit, and similar topics. The advantage of the platform is that we can go very deep. You can see here (Figure 2) some of the underlying governance topics associated with Wirecard, such as fraud, embezzlement, and crime—the main accusation—but also things related to anti-competitive practices or corruption.

And furthermore, the platform enables full transparency. This is AI at scale, but the underlying content is actually text articles and messages that you can read in order to understand the situation and see why the company is in that risk position. So with our platform, with our text analysis engine (TextReveal®), you can immediately extract content on your portfolio, your clients, your suppliers, and for example, generate ESG insights, competitive insights, sentiment insights, or credit warnings, for example.

Trusted, reliable, and abundant insights

We are today trusted by major financial institutions, such as Nomura [Holdings] or Raiffeisen Bank in the banking sector, for example, or large private equity firms worldwide. The reason why they trust us is that we can provide data more quickly—so waiting one day instead of waiting three months—to get an indicator. In addition to that, we have better coverage. We’re the only company in the world that can provide information on five million different public and private companies, meaning all of your banking clients, for example, are covered. And finally, we have access to a large variety of sources, from social content to news and blogs.

Insights beyond companies

Another example that is very common—sadly right now—is clients asking us to follow the Ukraine Russia War and to understand the current situation, including by getting access to local content in local languages in Ukrainian, in Polish, in Russian, to really understand the news and social media out there.

You can see here that beyond companies, we actually track sectors, infrastructure projects, and concepts.

Here (Figure 3), Nord Stream, for example, in the context of Ukraine specifically—so as to understand how these two topics are associated on the web—we can see an explosion in terms of volumes of data over time, the news associating this concept more and more, with more than 40,000 pieces of content. And we can see that sentiment over time, as displayed on this curve (Figure 4), decreases very rapidly, so we see the shock on e-reputation, and we can observe that immediately. And, for example, as a bank or as an asset manager, we can use that to assess the potential risk to clients or portfolio companies.

The interesting thing here is that, beyond the graphs and the raw contents, we can look at where the information comes from. Here (Figure 5), you see a lot of information in German, for example, which is not surprising. And you can even follow the Russian propaganda directly from the platform, looking at Russia Today or Sputnik straight from the engine, as these are also sources that we monitor.

And as you can see, these contents are highly customizable and can be used in very specific situations. So this is really a platform as a service (PaaS) that we offer. This is an engine that tracks four million different sources of information, and we can track millions of companies but also even fuzzy concepts, countries, or topics of interest.

Generate analytics from big data with API

One last thought. A lot of our clients integrate with our API; it’s a technical solution. We work a lot with data science teams, data engineering teams, risk teams, quantitative analysts, and heads of innovation. All of these teams are looking to generate analytics from big data and from web content at scale, with solutions that are currently used by dozens of clients worldwide and for which we provide very relevant analytics.

I’ll leave you with three final calls to action.

1. The first one is come see us at our booth. We would be very happy to present the solution in a bit more detail.
2. The second is, please request a demo. You understand that these indicators can be tailored to your needs in real time. So we’ll be very happy to show you a demo at SESAMm.com.
3. And finally, come see us for a free proof-of-concept (POC). We would be very happy to show you how we incorporate these solutions in actual banking tools and in risk management tools.

So the web is now readily available as a system that you can use and that you can rely on in order to generate valuable insights. We’re very happy to provide the solution to the market and to help inform better decisions and to help monitor risks.

Thank you very much.

The first and second parts of this series discussed the differences between public and private companies from the Environmental, Social, and Governance (ESG) and the United Nations Sustainable Development Goals (UNSDG) perspectives. In this part, we’re using IKEA as a prime example; we can explore how its private ownership impacts its sustainability practices and governance. This analysis aims to reveal how IKEA's strategies align with broader ESG goals, shedding light on the implications of private versus public company frameworks.

We chose IKEA as an example for this use case for two main reasons. Firstly, as a private company, it provides a suitable basis for comparison with other private companies in the same industry. Secondly, IKEA is known for promoting sustainable practices, such as using renewable energy, responsibly sourced materials, and minimizing waste. However, despite the company's claims about the eco-friendliness of its products, our goal is to investigate whether these claims and products are perceived as environmentally friendly. We also aim to identify any issues affecting any of its stakeholders beyond the environment.

ESG Industry Benchmark

In our study, we focused on a detailed comparative analysis of IKEA's Environmental, Social, and Governance (ESG) risk mentions over the past three years, particularly in the context of the consumer discretionary sector. Our findings indicate a lower prevalence of environmental controversies both for IKEA and the sector overall. However, regarding governance risks, the consumer discretionary sector appears to encounter these issues more frequently than IKEA does.

On the other hand, IKEA stands out with a more significant presence of social risks than the sector average. This includes a notable number of product safety concerns, exemplified by instances of product recalls due to choking hazards, laceration risks, and even products infested with bugs. The analysis also brought to light several instances of human rights breaches at IKEA, particularly concerning privacy issues, such as data leaks and illegal filming incidents involving staff and customers. Labor rights violations are another area of concern, with instances ranging from union-busting activities to allegations of religious and gender discrimination within the company. Additionally, human capital risks are conspicuous, with mentions of strikes driven by dissatisfaction over wages and layoffs, as well as health and safety issues. Risks in customer relations have also been documented, including incidents of overcharging customers and discriminatory practices against certain customer groups.

Detecting ESG Risks Through the Industry SDG Lens

In our comparative analysis of IKEA's controversies against the average adverse behaviors in its sector concerning the Sustainable Development Goals (SDGs), we noticed both similarities and distinctions. A key finding is that Goal 1, "End poverty," features prominently for both IKEA and the sector, highlighting a common vulnerability to controversies under this goal.

These breaches predominantly pertain to issues around labor rights and human capital, aligning with the findings from our ESG controversy analysis. Additionally, a smaller yet significant portion of controversies is linked to internal control deficiencies within the company. This pattern suggests that both IKEA and its sector face similar challenges in addressing labor rights and human capital issues, contributing to breaches of Goal 1.
In examining the differences, Goal 3, "Health and well-being," stands out for IKEA, exceeding the sector norm. This is largely attributed to numerous product recalls, alongside health and safety concerns related to IKEA's workforce.
Moreover, in Goals 11 ("Sustainable Cities") and 12 ("Responsible Production and Consumption"), IKEA shows a higher-than-average proportion of controversies, mainly due to issues in human capital and customer relations. This highlights a specific focus on product safety and human capital challenges at IKEA, pointing to areas of heightened risk or difficulty compared to industry peers.
Additionally, our study reveals distinct variations in Goals 9 ("Industry, Innovation, and Infrastructure") and 16 ("Peace, Justice, & Strong Institutions"), where IKEA shows a lower proportion of issues compared to the sector average. This suggests that, unlike its industry counterparts, IKEA has been more effective in mitigating risks in these areas.

Detecting ESG Risks Through the Industry SDG Lens

Our methodology analyzes the controversies detected for IKEA and maps them to identify which ones constitute breaches of the United Nations Global Compact (UNGC) principles.

Consistent with the identified ESG risks, human rights breaches at IKEA are notably more prominent than the sector average. This is primarily due to multiple instances of privacy, security, and dignity violations, as well as issues in diversity & inclusion. Additionally, labor rights issues at IKEA, while exceeding the industry average, are not markedly higher.
Our study also reveals that IKEA has a slightly higher proportion of breaches in the environmental pillar compared to its sector. These include incidents like gas leaks, allegations of greenwashing, and cases of illegal logging.

Conclusion

ESG controversies and breaches of SDG goals vary notably between public and private sectors. Public companies frequently encounter more visible and consistent ESG risks, while private companies, although subject to less scrutiny, experience significant impacts when controversies do occur. The case study of IKEA particularly sheds light on the unique challenges faced in product safety and human capital. This highlights the critical need for rigorous and proactive risk management strategies to maintain sustainable corporate practices tailored to the specific nature and scale of the entity in question.

Download the full report to discover how different sectors navigate regulatory pressures and sustainability challenges with real-world examples to guide your strategy.

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.

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.