Navigating the finance sector requires technologies that offer precision and foresight. Watch Andrew Bernstein, Head of Global Sales, demonstrate SESAMm's ESG Alerts and Monitoring at the ESG Fintech Summit 2023 in London last June. This tool allows private equity firms and asset managers to stay ahead of emerging risks and opportunities.Watch the demo here:

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Welcome back to the second part of our thought leadership series on implementing generative AI solutions for finance. In the first part of this series, I discussed the impact, implications, and potential challenges of generative AI in the financial sector. Today, I'm excited to share SESAMm's journey, a leading AI fintech firm, as we integrate the latest generative AI technology into our products.

SESAMm's Generative AI Journey

At SESAMm, we've always been at the forefront of technological innovation, and our approach to AI is no exception. We've been actively studying the market and gathering user feedback on the potential applications of Generative AI, particularly those modeled after large language models like ChatGPT.

The feedback revealed a clear demand for more Generative AI implementations. Recognizing the potential, we began by integrating the technology into our own internal processes, automating data annotation, streamlining competitor identification, and even generating marketing content. The results have been profound, leading to operational efficiencies and rapid skill development within our team.

Now, we're ready to ramp up our efforts and embark on a more aggressive Generative AI initiative. We aim to further embed large language models into our tech stack to enhance internal productivity, provide improved functionalities to our clients, and introduce a client-facing conversational agent in our dashboards.

The Roadmap for Integrating Generative AI

Our roadmap for integrating generative AI is threefold. First, we are already embedding large language models into our tech stack, developing solutions for automating data annotation for ESG/SDG alerts, automating company portfolio requests, improving the handling of complex queries, and leveraging generative AI for more automated due diligence.

Secondly, we plan to provide a client-facing conversational agent in our dashboards. This agent will automate the extraction and summarization of important ESG/SDG events, generate competitors’ lists and analyses, and handle full due diligence and ESG reports.

Finally, we're focused on increasing the internal adoption of AI across all teams. We're equipping teams with ChatGPT Plus accounts for daily tasks, granting a group of developers access to Github Copilot for productivity gains, and organizing internal working groups and demo sessions.

Enhancing Functionality and Performance

Integrating generative AI into our products will significantly enhance their functionality and performance. Clients will find it much easier to interact with our data, and usage of our dashboards will become quicker and more intuitive. New functionalities will be added, such as the summarization of ESG/SDG events and automatic searches of competitors and comparables for any company.

Notably, the integration of generative AI has led to a significant increase in our rate of shipping AI features. We've seen a five-fold reduction in development time for many components, enabling us to deliver value to our clients much more rapidly.

Robust Risk Mitigation

Risk mitigation is a key concern in the financial sector, and Generative AI can play a vital role in this domain. By integrating generative AI into our products, we aim to provide more comprehensive solutions for detecting risk and ESG controversies for due diligence and portfolio monitoring.
Generative AI will help us better interpret these controversies, providing insights as if our clients had immediate and constant access to an entire team of ESG analysts and experts for each company. This capability can significantly enhance our clients' ability to navigate potential pitfalls, ensuring safer and more informed decision-making.

Positioning for the Future

At SESAMm, we're positioning ourselves for the future by taking a proactive, agile approach to integrate generative AI within our existing product suite.
We're deploying a dedicated task force of experts on this project, iterating quickly and slightly outside our traditional product development processes. Training our team is a crucial aspect of this endeavor, with full team training sessions on Generative AI, discussions related to OpenAI’s API use, and practical examples presented by our data science team.

Our management team is highly passionate about this initiative. We continuously monitor and share the latest developments in AI, ensuring we don't miss any technological shifts that could accelerate our innovation efforts.

The Future of SESAMm with Generative AI

With the integration of generative AI, we envision a future where SESAMm can provide even more value to our clients. Our products will become easier to use, faster, and more intuitive. The new functionalities we are adding will allow us to provide insights and analyses that were previously out of reach.

Moreover, we foresee an increase in our pace of innovation. As I mentioned earlier, the integration of generative AI has already allowed us to develop new features five times faster. This acceleration will enable us to stay ahead of the curve and continue to provide our clients with cutting-edge solutions.

Finally, integrating generative AI will facilitate the adoption of advanced technologies across our entire team, fostering a culture of innovation and continuous learning. This internal transformation will drive our ability to deliver superior solutions to our clients.

The integration of generative AI is an exciting step for SESAMm. It presents numerous opportunities to enhance our product offerings, improve our internal processes, and deliver greater value to our clients. As we embark on this journey, we are not only shaping the future of our company but also setting a precedent for the finance industry at large.

Are you excited about the potential of generative AI in finance? Want to learn more about SESAMm's new solution? Click here to explore how we at SESAMm leverage generative AI to revolutionize the finance industry. Also, make sure you read the third and final part of this series.

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Hello, and welcome to our ongoing series on Generative AI in Finance. I’m Sylvain Forté, CEO and co-founder of SESAMm, and in our first article of the series, we’ll explore how generative AI is reshaping the financial industry. At SESAMm, we’ve been fortunate to be at the forefront of this revolution, witnessing the transformational power of large language models like ChatGPT and its iterations.

Unprecedented Evolution in Generative AI

Let's begin with an overview of the current generative AI landscape. Over the last few years, we've seen an explosion in the capabilities of generative AI, particularly in text processing. From BERT to GPT4, large language models (LLMs) have demonstrated increasingly impressive capabilities. These models, performing at a human level for many tasks, are rapidly evolving, making the past six months feel like an exponential leap in the AI domain.
Generative AI is no longer a speculative idea but rather an early adoption phase of a powerful technology. At SESAMm, we've leveraged our partnerships with OpenAI and other organizations to gain high-level access to these AI models, allowing us to harness this potential and democratize access to intelligence. It’s an exciting shift that, while reshuffling business models and job roles, promises enormous productivity gains and increased overall value. The key, of course, is ensuring that these benefits extend to society as a whole.

The Disruption in Financial Sector

The finance sector, with its vast array of text-based tasks, stands to gain enormously from generative AI. Any repetitive yet intelligence-heavy tasks — think verification, document generation, or client communication — are ripe for automation.
Finance, despite being a highly intelligent sector, often sees that intelligence is misspent on routine tasks. Generative AI can realign this balance, reducing costs, enhancing service quality, and building trust. Whether private equity, asset management, or commercial banking, AI can streamline processes, delivering an efficiency boost that significantly enhances customer satisfaction.
In private equity, the automation possibilities could reshape the sector, bringing it closer to the public markets. In asset management and banking, cost reduction and service enhancement could lead to a dramatic rise in customer satisfaction.

Concrete Use Cases of Generative AI in Finance

So, how does this look in practice? Generative AI can automate numerous finance tasks, including creating reports, verifying information, summarizing news or earnings calls, and even making internal data searchable. Imagine a system that can help asset managers match various types of datasets based on a user query in natural language, thereby making data access and interpretation simpler. This could vastly improve the user experience with business software, reducing effort and time spent. While some applications, like a fully automated financial advisor or AI-led trading and hedging, might present more significant challenges, their potential benefits could revolutionize these sectors.

Overcoming Roadblocks

Of course, every transformation comes with challenges. The key is discerning which use cases are suited for full automation and which require human oversight. Data privacy concerns will also influence decisions about whether to use proprietary or open-source models.
There will inevitably be resistance to change within organizations, but the 'Google test' can help navigate data privacy issues: if an employee would conduct that search or share that data on Google, it's likely safe to share with a proprietary Generative AI solution.

Generative AI and Risk Mitigation

Risk mitigation strategies can greatly benefit from generative AI. From detecting and preventing fraud to managing market risks, generative AI can verify identities, cross-reference databases, and analyze vast amounts of data. For instance, at SESAMm, we're developing an ESG controversy detection solution that can be an invaluable tool for risk mitigation.

Improving Investment Decision-Making

Generative AI’s ability to process and analyze massive amounts of data accurately and quickly makes it a formidable tool for investment decision-making. By identifying patterns, trends, and correlations that humans might miss, generative AI can provide a more comprehensive, data-driven perspective, aiding portfolio optimization, asset allocation, and investment risk management.

Streamlining Operations for Efficiency

Generative AI's efficiency and accuracy promise to transform financial institutions. By automating time-consuming tasks like report generation and client communication, AI can free employees to focus on strategic tasks that require critical thinking.
Imagine being able to ask complex questions to your banking app in natural language and getting immediate, accurate responses. Such high-quality service was unthinkable a few years ago, but with generative AI, it's within our reach.
The future of the financial sector is undeniably tied to the successful implementation of generative AI solutions. The potential is vast, the challenges are surmountable, and the rewards are great. In my view, generative AI is the key to a more efficient, cost-effective, and customer-centric financial sector.

To learn more about SESAMm’s innovative solutions and how we’re pushing the boundaries with generative AI, read the second part of this series here.

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In recent years, the field of natural language processing (NLP) has seen significant advancements that have enabled more effective processing and analysis of large volumes of textual data. This has had major and disrupting implications for many industries, including finance and investment.

One area where NLP has been particularly useful is in the design of baskets and indices on all asset classes. One of them, where we can definitely observe key value-added, is related to digital assets and cryptocurrencies. A crypto basket is a group of cryptocurrencies that are bundled together and traded as a single unit, while a crypto index is a measurement of the overall performance of a group of cryptocurrencies.

Traditionally, the process of designing a crypto basket or index involved manually selecting a group of cryptocurrencies based on various criteria such as market capitalization, trading volume, and price history. However, this process is time-consuming and can be subject to bias. Moreover, it can be difficult for investors to invest in the crypto market as a whole. NLP technology has allowed it to analyze vast amounts of data from various sources such as news, articles, social media posts, and blogs to identify trends and sentiment around specific cryptocurrencies. This information can then be used to inform the design of crypto baskets and indices, making them more accurate and reflective of market sentiment.

By leveraging sentiment analysis through NLP-based indicators, robust indices can be created to serve as market benchmarks and investment vehicles. These indices can provide relevant performance measurement tools, allowing investors to understand the performance of their investments better and make more informed decisions. Furthermore, using NLP-based indicators to design crypto baskets and indices can also help generate alpha compared to a single basket of tokens. By tracking sentiment and emerging trends over time, investment professionals can gain valuable insights into which cryptocurrencies will likely perform well in the future and which may be less favorable.

Overall, using NLP in the design of crypto baskets and indices has significant potential to improve the accuracy and reliability of these investment products. By leveraging the power of NLP to analyze large volumes of text data, investment professionals can gain valuable insights into market sentiment and emerging trends, allowing them to make more informed investment decisions and potentially generate alpha. This is why we have entered a collaboration with Compass FT to design the first AI & NLP crypto sentiment index.

Index objective

The Compass SESAMm Crypto Sentiment Index aims to give investors exposure to the crypto market with a sentiment tilt to determine the selection and weights of underlying tokens. The index selects tokens based on financial filters such as average trading volume and market capitalization. Using NLP-based sentiment scores in the weighting mechanism allows the index to rebalance towards the coins with the best sentiment scores efficiently and, therefore, those with the highest expected relative returns.

Key features

• Provides smart and dynamic exposure to the cryptocurrency market
  
• Monthly review to adapt to the fast-moving crypto ecosystem and capture up-to-date/representative sentiment for each coin
• Unique quantitative weightings mechanism based on liquidity filters and sentiment scores
• Invests in a basket composed of the 20 main crypto coins
• Constituents are selected based on rigorous criteria considering liquidity, tokenomics, sentiment, custody, and security
• Methodology and governance in line with the most constraining financial indices regulation, the European Benchmark Regulation (EU BMR)

Index mechanisms

The Compass SESAMm Crypto-Sentiment Index is a diversified digital asset index designed to offer broad exposure to the market’s top crypto assets (all sectors included) while capping each component exposure at 30%. Weightings are based on sentiment scores, liquidity, and market capitalization constraints.

SESAMm’s NLP technology carries out a granular and transparent analysis of publicly available articles. More than 20 billion articles from over 4 million international and local sources are analyzed to identify each coin’s associated mentions. For each source, indicators of sentiment and volume of mentions are determined. These indicators are then aggregated daily to create a historical time series per cryptocurrency, which acts as the basis for the overall score used by Compass Financial Technologies. For each day and each coin, SESAMm calculates crypto sentiment scores based on several indicators, such as polarity, volume, and memory functions, to provide up-to-date and representative scores. SESAMm’s Crypto sentiment scores are based on the sentiment scores (negative, positive, and neutral) computed on articles related to the 50 digital assets universe.

Analytics

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In recent years, the concept of Environmental, Social, and corporate Governance (ESG) investing has gained tremendous traction. Not only does it offer opportunities to generate sustainable returns, but it also enables investors to make a positive impact on society and the environment. However, to truly understand the value of ESG, we need to shift our perspective and consider the 'new' stakeholders that are becoming increasingly crucial in this space. In this blog post, we’ll also delve into the challenges of the current ESG rating systems and discuss how AI is transforming the ESG landscape.

Broadening the ESG landscape: Emergence of new stakeholders

Historically, financial analysis has primarily focused on the impact of a company’s actions on its shareholders. Today, however, this view is expanding to include a more diverse array of stakeholders, thanks to ESG analysis - groups that are vital for a company's long-term prosperity. The environment, local communities, government authorities, regulators, NGOs, and journalists now take center stage as new stakeholders in the ESG dialogue.

The environment, for instance, is a stakeholder that companies can no longer afford to ignore. Overexploitation and neglect have led to climate change, thus, the depletion of vital resources and biodiversity, jeopardizing the long-term viability of many businesses. The recognition of the environment as a stakeholder underscores the necessity to balance economic growth with sustainable practices.

Similarly, local communities provide the workforce that companies rely on and need to respect their social environments and fundamental human rights. Governments, often viewed solely as tax collectors, are also stakeholders, providing key services like infrastructure, safety, and the rule of law. Finally, NGOs and journalists, tasked with safeguarding the general interest, ensure transparency and accountability, holding companies to their ESG commitments.

The problem with current ESG ratings

As companies grapple with these complex and interconnected issues, ESG ratings have emerged as a tool to gauge their sustainability efforts. However, these ratings aren't without their flaws.

Firstly, there is a notable divergence of opinion between rating providers, which can lead to confusion and inconsistency. Different providers may emphasize different aspects of ESG, leading to disparate ratings for the same company.

Secondly, most ESG ratings are based on self-reported data, creating an inherent risk of bias or selective reporting. It’s like allowing students to write and grade their own exams, which isn’t ideal for a system aiming to bring transparency and objectivity.

The power of AI in ESG risk assessment

To overcome these challenges, a new player is emerging in the field: Artificial Intelligence (AI). Through Natural Language Processing (NLP) algorithms, AI can analyze billions of documents from a wide range of sources to provide a more objective and comprehensive view of a company's ESG performance.

These AI-driven tools, like those developed by SESAMm, can scan a plethora of information, from press articles and social media posts to reports from NGOs, local press, and governmental bodies. They can detect ESG controversies, positive events, and sentiments linked to various ESG issues. This results in a more detailed and accurate picture of a company's ESG framework that surpasses what current ratings offer.

By bridging the gap between traditional ESG ratings and actual on-the-ground impact, AI provides a novel and powerful tool for investors and companies alike. It fosters a more holistic approach to sustainability, one that takes into account the increasingly complex web of direct and indirect stakeholders.

The future of ESG

In the grand scheme of things, the integration of AI into ESG analysis marks a significant leap forward. By acknowledging the role of new stakeholders and addressing the shortcomings of current ESG ratings, AI is reshaping our understanding of sustainable investing. The road ahead is exciting and promising, and there's no better time than now to harness the power of AI for a more sustainable and inclusive future.

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Client: Raiffeisen Bank International

Industry: Corporate Banking and Finance

Location: Austria

Use case: ESG alerts and monitoring

SESAMm solution: TextReveal^® API and Dashboards

Introduction

Raiffeisen Bank International’s (RBI) Advanced Analytics and AI Tribe is crucial to the bank’s operations. The team delivers, maintains, and operates AA&AI (digital) solutions allowing Retail and Whole-Sale Banking to increase revenues (and to fulfill their role as the first line of defense). They are pioneers in using cloud-based infrastructure. With more than 50 data scientists, data engineers, machine learning engineers, and cloud engineers, they play a crucial role in transforming RBI into a data-driven company.

The AA&AI tribe at RBI recognized a significant opportunity in SESAMm, a leading AI-powered analytics, and data solutions provider. SESAMm’s solutions offer access to an extensive range of web-based information, which is otherwise challenging to obtain. This data is critical for RBI’s operations, enabling the bank to stay ahead of the curve regarding market trends, consumer preferences, and industry insights.

Key successes for RBI after working with SESAMm include:

• Generated analytics on clients to specifically monitor companies exposed to the Ukraine war, enabling the bank to proactively identify potential risks and minimize its exposure to geopolitical events.
• Integrated specific languages within RBI’s core market, including Russian, Romanian, Slovak, Czech, and Polish, improving the bank’s ability to analyze and understand regional data.
• Integrated SESAMm’s data with RBI’s internal visualization dashboard, allowing the bank to leverage the insights generated by SESAMm’s AI-powered analytics to improve decision-making and drive business growth.

Why RBI chose SESAMm: Coverage, early warning signals, and customizability

Raiffeisen Bank International decided to partner with SESAMm due to several key factors:

1. SESAMm’s excellent coverage, including that of the CEE market, is a crucial need for RBI. This coverage enables RBI to obtain critical data and insights that help inform the bank’s decision-making process.
2. SESAMm’s product, TextReveal API, provides data and the underlying natural language processing (NLP) capabilities, enabling RBI to analyze data at a deeper level. This capability is significant for the bank’s operations in the CEE region, where multiple languages are spoken.
3. The relationship built between SESAMm and the RBI team during the proofs-of-concept (PoCs) brought confidence in the quality of SESAMm’s products and the potential value they could bring to the bank.
   
   Overall, the combination of SESAMm’s excellent coverage of the CEE market, NLP capabilities, and positive relationship with the RBI team made them the ideal partner for the bank’s data and analytics needs.

The Collaboration

After SESAMm and Raiffeisen Bank International agreed to collaborate, SESAMm began working with David Eschwé, the Head of Group Advanced Analytics at RBI. SESAMm onboarded the RBI team on TextReveal API and opened dashboards and API access to RBI. This access allowed RBI to generate historical datasets. SESAMm worked with the RBI team to define the roadmap and key milestones, particularly for integrating Central and Eastern European languages. This enabled RBI to access critical information efficiently that could help their internal teams generate early warning signals to better mitigate potential risks that can harm the bank. By working closely together, SESAMm and RBI achieved key milestones, demonstrating the value of the collaboration to both parties.

The results

By leveraging SESAMm’s solutions, RBI was able to monitor more than 1,000 clients, generating analytics on companies exposed to the Ukraine war and creating early warning signals to mitigate better potential risks that could harm the bank. Additionally, SESAMm’s solutions provide substantial yearly savings in raw-data-related costs, allowing RBI to allocate resources more efficiently and effectively. Through this collaboration, SESAMm helped RBI achieve more significant insights into their data, improve their risk management processes, and achieve considerable cost savings.

"Our partnership has been a great success. Thanks to SESAMm, we can now answer business-relevant questions within days, including those related to the critical topic of ESG” —David Eschwé, Head of Group Advanced Analytics in RBI.

About Raiffeisen Bank International

Raiffeisen Bank International AG (RBI) is a leading Austrian banking group that operates across Central and Eastern Europe. It is headquartered in Vienna, Austria. RBI offers a wide range of banking and financial services, including corporate and investment banking, retail banking, leasing, and asset management. With a focus on sustainability and social responsibility, RBI is committed to providing high-quality banking services while supporting the communities in which it operates.

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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.

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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.

CEO Sylvain Forté demonstrates TextReveal’s® latest features applied to supply chain and risk analysis on the main stage of FinovateEurope 2023 in London. He also presents a quick use case on the latest Silicon Valley Bank fallout.

Discover how TextReveal can help automatically analyze millions of companies based on web content at SESAMm.

Watch the full recording:

SESAMm has secured Series B2 funding, and I want to share my excitement about this milestone and reflect on the journey.

After months of hard work from our team, we closed on a €35 million Series B2 funding round led by Elaia and Opera Tech Ventures (BNPP), which followed a €7.5M B1 round led by New Alpha and Carlyle in January 2021. That means we can now pursue the next phase of SESAMm’s growth.

We want to accomplish many things with this investment, and I’m incredibly grateful to all of our investors, partners, and clients who helped us during this process. But before I talk about them and SESAMm’s future, I can’t help but think about how the company started.

Academic beginnings

It’s still hard to believe that SESAMm sprouted from an idea, an academic project, and three college students. I still remember Pierre Rinaldi asking me to develop the first algorithms that used social media to analyze financial markets while in school. Pierre worked at a bank trading desk while finishing his studies and doing a research internship at a behavioral finance lab, where the idea came up for the project. I studied engineering and was passionate about AI already, so I started coding, and the first project was born.

Eventually, this project outgrew my capabilities. The volume of data involved needed hardcore algorithms to process them, the kind of algorithms Florian Aubry could build. Florian was the best developer in my engineering school, and lucky for us, he was also my friend.

Before long, we started renting servers, optimizing code, and building signals based chiefly on Twitter data, reproducing research papers that also used text data to generate sentiment indicators for financial markets. You could say that this project had become more than academic at this point. But it really only became a company once we won our first startup award.

The award that sparked the launch of a company

We participated in a competition organized by local incubator SEMIA with support from Société Générale as a way to gain credibility for the project, and would you know, we won. The award was monetary, and we could receive it on one condition: that we launch an actual company.

You probably already know that starting a business is a big decision, at least it was for Pierre, Florian, and me. After all, we were still in school, trying to finish our degrees and looking to start new careers. Our choices could be life-altering. Pierre, for instance, planned to take a full-time job at the bank he was working at.

In the end, we chose SESAMm. Pierre didn’t take the job at the bank. I did my end-of-study internship at SESAMm, which allowed me to focus on the company 100% while finishing my degree. And Florian dedicated all his time and energy to the startup, too. In other words, we were all in, and our school project had officially become a business.

Pressure to build

Setting up a company was relatively easy. Building a business, as it turns out, is way harder. How would we take SESAMm’s product and put it to work? Pierre’s contacts, that’s how.

One by one, Pierre called on everyone he knew from school that worked in trading, offering them a chance to be the first to test the latest AI innovation: using social media for financial analysis. Back then, Pierre had more of a sales role. He’s now COO and head of HR, in case you were wondering.

Our first client was a trader from London. He put $50,000 into a strategy based on SESAMm’s signals. With our signals, the trader grew his portfolio with such performance that he began to raise capital, including for a hedge fund. Ultimately, he was trading $50 million from the strategy. It was the kind of success and track record we needed in the finance industry to take SESAMm to the next level.

Compelled to move…literally

Having demonstrated success, an investment firm, a venture firm called Fonds Venture Numérique Lorrain, reached out to us with interest in investing in SESAMm’s first round of funding. But they had two conditions:

1. SESAMm must move to Metz, a startup-friendly hub in the Lorraine region in France.
2. SESAMm must seek additional funding from other investors.

At the time, SESAMm was based in Strasbourg because that’s where Florian and I studied. And for all intents and purposes, we hadn’t planned on moving.

Also, aside from Pierre reaching out to school acquaintances to try our solution, we had never pitched anyone to fund our company before. Pierre, Florian, and I faced a serious choice and challenge.

What did we do, and how did we fare?

We reached out to business angels and pitched numerous times while continuing to work on the product and starting to plan the first recruitments. We learned a lot during this initial period about how to structure a company and present it to investors. At the time, startups were less common in the region and using online content from YC and other accelerators was invaluable for us.

SESAMm’s headquarters is now in Metz, and we currently have offices in Paris, London, Tunis, New York, and Tokyo. We’ve also diversified our technology and clientele, particularly in ESG and corporate spaces. In other words, we faced the challenge, moved our headquarters, and grew and evolved a lot!

Series B2 funding round challenges

That brings us to the recent past. We’ve grown our company so much that it was time for another funding round, and the timing wasn’t the best. Our teamwork and ability to communicate effectively across regions and cultures were vital in tackling this latest fundraising round. We decided to run the entire process in-house as it was important to us to not only demonstrate our ability to do so but also to build our own VC relationships.

Moreover, we’ve had a unique previous fundraising experience. Having been funded by FinTech venture capital firm NewAlpha Asset Management and a large private equity firm like Carlyle Group Inc. in earlier rounds helped us improve our processes and standardize our communications. We felt ready for this challenge.

Frankly speaking, though, between the COVID-19 pandemic and the Russia-Ukraine war, startup funding had become a much bigger challenge. Things weren’t popping like they were a couple of years ago, so we needed to be cautious and conservative yet bold and compelling. Regardless, we dug deep and stuck with the plan, with a focus on our numbers and our vision.

To achieve our goal, we created an internal team, onboarded two advisors, and created a scalable process. The internal team included SESAMm’s C-suite: CEO (Sylvain Forté), COO (Pierre Rinaldi), CTO (Florian Aubry), CFO (Marie-Charlotte Deucher), CMO (Jorge Alvarez), and CDO (Eric Sionnet). Our advisors provided guidance around strategy, gave feedback, and supplied operational help when needed. And from our pitch deck variations to our storage and sharing policies, we established a fine-tuned communication and project management process to keep us on task and on time. It was a good process, and it really helped us manage things efficiently while maintaining control over day-to-day operations. I can’t say it enough: process is always the key.

Fundraising wasn’t easy, but the process was worthwhile. And all this to say that I couldn’t be happier to announce that SESAMm has wrapped up its Series B2 funding round. With this money, we plan to further expand into the U.S. and Asian markets and continue SESAMm’s exponential growth. We’ll also seek key talent to sustain this growth and support the development of our AI technology.

Pleased, grateful, and excited for what comes next

Today, SESAMm is a healthy and fast-growing company. And as COO, CTO, and CEO, respectively, Pierre, Florian, and I are now more than a scrappy group of students. We’re a crew with the support of many contributors, consisting of more than 100 employees across many offices and cultures. It’s because of the teamwork and collaboration that SESAMm is where it is today. And I’m immensely grateful for all our team’s effort and the tremendous support we get from clients, investors, and advisors every day.

Thank you for being a part of our journey, and see you at SESAMm’s next milestone. Cheers!

Read the press release: “SESAMm Raises €35M in Series B2 to Grow its ESG and Sentiment Analysis Business.”