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The Secondaries Market in 2026: Record Growth, Emerging Challenges, and What Lies Ahead

April 29, 2026
5 mins read
The secondaries market has tripled since 2019. We examine what's driving growth, how deal terms are evolving, and the role of AI in due diligence.

The private markets secondaries space has entered a new chapter. What was once a niche corner of alternative investments, used primarily by limited partners (LPs) seeking early exits from fund commitments, has grown into one of the most dynamic segments of global private capital. The market has tripled in size since 2019 and grown by approximately 50% between 2024 and 2025 alone, reaching an estimated $230 billion in annual transaction volume and now representing around 5% of all global private equity assets under management. 

This piece examines the forces behind that expansion, the structural shifts redefining the market, and the operational and regulatory challenges participants will need to navigate as the asset class continues to scale.

Market Growth and Shifting Deal Dynamics

Several converging factors have driven the secondaries market to its current size. A prolonged slowdown in IPO activity and traditional exits has created a liquidity bottleneck across private markets, leaving many LPs over-allocated to alternatives and constrained in their ability to make new commitments. The secondary market has become a primary mechanism for these investors to rebalance portfolios and free up capital.

Deal structuring has grown more sophisticated in step with market volumes. Ropes & Gray has observed a continued expansion in the use of purchase price deferrals and earnouts, and more recently, the introduction of deal-specific funding caps, limits on how much capital a buyer can be called to deploy before a specified date. These mechanisms allow sellers to achieve higher reference-date pricing while enabling buyers to manage capital deployment pacing and portfolio composition. In Q1 2026 alone, institutions initiated new secondary sales processes totaling north of $20 billion, some linked to denominator effect concerns as declines in public market portfolios pushed private allocations above target levels. Whether this proves a sustained driver of supply will depend on how institutional portfolios weather current market conditions.

The Three Transaction Types

Secondary transactions fall into three main categories: 

  • LP-led transactions, the original form, involve an LP selling existing fund interests, sometimes across a broad portfolio of hundreds of positions, typically through competitive auction processes with tight timelines. 
  • GP-led continuation funds, the fastest-growing segment, involve a sponsor transferring select assets into a new vehicle, giving existing LPs the option to cash out or roll forward. As of 2025, GP-led and LP-led volumes are roughly evenly split at around $115 billion each. GP-led buyout fund volume grew 39% year-over-year, while private credit secondaries saw nearly 300% year-over-year growth in GP-led activity. 
  • The third category, structured solutions, provides capital to a GP collateralized by existing fund assets and can take a wide variety of bespoke forms.

What Are the Operational and ESG Challenges in the Market?

One of the defining challenges in secondaries is the speed and scale of due diligence required, particularly in LP-led transactions. Buyers may need to evaluate hundreds, or in private credit secondaries, over a thousand, underlying positions with limited information and within windows of 24 to 48 hours. As Jessica Huang, Managing Director and ESG lead for private equity and secondaries at Ares Management, noted in a recent webinar:

Against this backdrop, LP expectations around ESG integration have risen sharply. LPs are now holding secondaries to a standard closer to that applied to direct investments, with requests for Article 8-classified funds, look-through exclusion lists, and UN Global Compact compliance screening becoming more common. Main exclusion categories include fossil fuels, controversial weapons, tobacco, and gambling, though definitions and revenue thresholds vary significantly across mandates. SFDR 2.0, currently in draft form, may introduce additional mandatory exclusion categories that managers are monitoring closely. In LP-led deals where buyers are inheriting a broad portfolio of assets, highly granular opt-outs can mean missing certain large transactions, a trade-off that must be clearly communicated to LPs.

The Role of Technology and AI

Technology has become central to the scaling of secondaries operations. AI tools are now applied across controversy screening, ESG data analysis, and emissions estimation, where direct disclosures are unavailable. A particular challenge in the asset class is coverage: many underlying companies are small or mid-market private businesses not captured in conventional databases.

Market participants consistently emphasize that AI outputs serve as inputs to human judgment, not as replacements for it. At Ares, screening results are reviewed by ESG specialists before being passed to deal teams for final decisions.

What the Future Holds

Transaction volumes are forecast to continue rising as both the seller and buyer universes expand. Private credit, infrastructure, and structured secondaries all represent areas of growing specialization and regional expansion, particularly in Asia, where secondary activity has been limited but is expected to grow as investment programs mature, broadening the market further. Capital supply dynamics bear watching: while dry powder remains substantial, deal volume growth has outpaced fundraising since 2023, which could create pricing or capital constraints. The entry of retail investors through evergreen vehicles adds a meaningful new source of capital but brings different liquidity expectations and regulatory considerations.

On the operational side, the sophistication of deal terms, the complexity of ESG compliance, and the volume of data processed per transaction are all increasing. Firms that can integrate technology into their diligence and monitoring workflows, while preserving the human judgment layer, will be best positioned to manage market growth. Secondaries are no longer a supplementary liquidity tool; they have become a structural feature of how private markets operate.

Read More

The chemicals industry, often perceived as the backbone of modern economies, is undergoing a notable shift. With the world's focus now fixed on environmental, social, and governance (ESG) initiatives, this sector finds itself at the crossroads of risk and opportunity. In this “ESG Data Trends,” we dive deeper into the chemicals’ market ESG performance, studying the example of Ineos.

The chemicals industry: riding the ESG wave

Post-2020, the chemical market has seen an increase in web mentions. Several factors—from gas shortages rattling this energy-intensive market to escalating environmental concerns—have ushered in a new era of sustainability discussions. But which chemicals are stealing the limelight?
Chlorine, Ammonia, and Base Chemicals like Ethylene and Propylene account for over half of the chemical web mentions. And it's not just about their volume. The narrative is changing too. The industry is leaning towards eco-conscious production, championing innovations like recycled propylene, Renewable-Benzene, and Green ammonia.

Chemical market volume of mentions graph
Figure 1: Chemical market volume of mentions.

What's interesting about this is the emphasis on ESG initiatives over ESG risks. It's a clear signal that the industry is taking action toward sustainability and is making tangible strides. When looking at the industry’s ESG risks mentions, we found that Arkema has the highest percentage of ESG Risks driven mainly by environmental incidents and impact on biodiversity due to a chemical plant explosion in 2017, followed by UOP LLC, which displays the highest proportion of Social related risks as a consequence of layoffs.

ESG risks by company chart
Figure 2: ESG risks by company.

Conversely, across the industry, the volume of ESG initiatives indicates a significant commitment to sustainable related practices. Environmental-related practices are the most mentioned initiatives in the chemicals industry; precisely, two pillars stand out in ESG initiatives: climate change reduction and circular economy strategies. LyondellBasell displays the highest percentage of ESG initiatives mentions due to its climate change reduction and circular economy strategies, where the company is working towards greenhouse gas reductions and advancing plastic waste recycling. Despite having the highest environmental risk mentions, Arkema has the highest social-related initiatives with corporate social responsibility.

ESG initiatives by company chart
Figure 3: ESG initiatives by company.

Case study: Ineos

The TextReveal Dashboard detected another chemicals company with an increasing number of mentions, the British multinational Ineos. After the announcement of Ineos Grenadier's off-roader in 2020, the number of mentions more than doubled, increasing Ineos' overall volume. Later on, the company’s mentions have been relatively increasing after cooling down from the announcement, with a significant increase in 2022 following M&A and collaboration announcements, sustainability actions, and controversies around its CEO, Jim Ratcliffe.

Ineos volume of mentions and relative volumes chart
Figure 4: Ineos volume of mentions and relative volumes.

We also detected a geographical shift in mentions. Once dominant in the US, Ineos mentions dropped from 65% in 2015 to roughly 30% in 2022. Europe, on the other hand, has seen a spike from 25% to over 65%. Sentiment analysis offers another layer of insight.

Geographical distribution over time chart
Figure 5: Geographical distribution over time.

While the sentiment has largely remained steady, there have been dips, especially during periods associated with fracking controversies and environmental incidents, including a toxic chemical spill. Digging deeper into Ineos’ ESG risks, there has been a decrease over the recent years; nonetheless, before 2019, we captured a relatively higher number of risks, mainly environmental–related controversies, coming from mentions about overexploitation of resources, namely fracking. Social-related risks display a significant proportion of data driven by social dialogue controversies as we capture multiple mentions of protests, particularly in 2017.

Ineos ESG risks over time chart
Figure 6: Ineos ESG risks over time.

While Ineos ESG risks mentions represent 2.46% of its overall data share, its ESG initiatives mentions represent 5.91% of its web presence, signaling a more positive outlook for the firm, at least from a perception point of view. Furthermore, we detected that environmental–related initiatives are the main focus for Ineos, particularly climate change, while social initiatives arise, particularly in 2018, due to product safety mentions.

Ineos ESG initiatives over time chart
Figure 7: Ineos ESG initiatives over time.

Data sources

To produce this analysis, we combined natural language processing with billions of textual web data related to the chemicals market. Using NLP-powered models gives us an edge as we can extract ESG, SDG, and financial insights that aren’t necessarily obvious or easy to detect. These insights help investors make better investment decisions. SESAMm leverages artificial intelligence and machine learning technologies to help you decipher and understand timely sentiment, trends, and ESG metrics on a wide range of public and private companies.

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.

ESG | Video

ESG Fintech Summit 2023: ESG Alerts and Monitoring

August 1, 2023
5 mins read

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:

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.

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.

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.

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.

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.

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

analytics performance and key indicators chart
Figure 1: Performance and key indicators

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.

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.

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.

Case Study

Transforming Businesses with AI-powered Analytics

June 1, 2023
5 mins read
Raiffeisen_Bank_International_Logo

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.

Reach out to SESAMm

Whether you’re a financial institution, an asset manager, or a data-driven company looking to gain insights into your data, SESAMm’s technology and team of experts can help you achieve your goals.

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.

BioNTech

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.

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.

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