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Hydropower's ESG Paradox: Why the "Green" Asset Class Tops the Controversy Charts

August 20, 2026
5 mins read
Hydropower tops ESG controversy volume across 250,000+ projects, outranking coal. Why the greenest label in energy hides the heaviest social risk.

An analysis of over 250,000 infrastructure projects reveals that the sector most often filed under "clean energy" carries the heaviest environmental and social controversy footprint of any asset type assessed.

In the taxonomy of energy infrastructure, hydropower occupies a comfortable position. It is renewable, dispatchable, and long-lived, and it enters transition frameworks, green bond eligibility criteria, and net-zero roadmaps with minimal friction. Where coal is a legacy liability to be managed down and nuclear invites a specialized debate, hydropower is largely treated as settled. What these projects have actually done does not support that treatment.

Belo Monte, an 11,233 MW complex on the Xingu River in Pará, Brazil, is the sharpest test of the point, because it was built to answer this exact objection. Approved after decades of opposition to a far larger design, it was engineered as a run-of-river plant to minimize flooding, and its reservoirs cover 478 km², of which 274 km² was already river channel at high water, a 61% reduction compared with the 1980s proposal, according to the operator's own regulatory filing. The mitigation was designed from the start, and everything that follows happened regardless.

Biodiversity: the cost of a physical footprint

Environmental controversy across infrastructure concentrates on industrial accidents, water pollution, and biodiversity, and hydropower leads the third, outright, because dams require the permanent conversion of river systems and the land around them. Mexico's Federal Electricity Commission won environmental approval in September 2014 for the Las Cruces dam on the San Pedro Mezquital, upstream of Marismas Nacionales, a Ramsar-protected wetland, even though the project's own impact statement conceded that the damage to Indigenous ceremonial sites could not be mitigated. Along the Mekong River, river health and fish populations fell as dam construction spread through the basin. In Brazil, the Doce River carried a mass release of toxic material after an upstream failure. Elsewhere, the record includes violations of the Endangered Species Act and documented disruption to rainfall patterns.

At Belo Monte, the consequences have been measured rather than projected. The plant diverts water into a canal that bypasses a 130-kilometer stretch of the Xingu known as the Volta Grande, which has received less than 30% of its natural annual discharge since 2019, and some 86% of the stretch's seasonally flooded vegetation, 30,748 of 35,600 hectares, can no longer be inundated at all. The gap lies in the regulator's own file: IBAMA's technical staff called for 10,900 cubic meters per second in February, the historic peak month, compared with the 1,600 that the operating regime actually releases. Seven years of underwater video survey data published in Scientific Reports recorded total fish species richness falling from 62 to a post-operation average of 51, with the steepest losses near the dam and in the rocky rapids, which hold roughly 2.6 times as many species as sandy reaches. The zebra pleco, whose entire known range lies inside the dewatered stretch, now sits on Brazil's national list of threatened species as critically endangered.

None of this is an accident or a failure of operation. It is a structural consequence of the asset. A well-run dam still floods a valley, and a dam engineered specifically not to flood one still dewater the river below it.

When engineering fails: hydropower's physical risk profile

Coal mining leads infrastructure on industrial accidents, where the record is dominated by human tragedy and safety negligence: explosions, collapses, fires, and repeated, incremental failures. Hydropower ranks second, but its accidents take a different form, because in this sector, industrial failure means catastrophic engineering failure at scale. The record includes pipe ruptures causing severe land erosion, oil leaks, and dam collapses that killed and displaced people across whole regions, while PG&E's settlement over damages to the Middle Fork American River Hydroelectric Project and the litigation still running in Brazil after dam collapses give a sense of the exposure a single event can generate. For anyone underwriting these assets, the distinction is financial as much as physical: a coal mine's safety record is a rising cost curve, while a dam's structural integrity is a low-probability, near-unbounded loss.

At Belo Monte, that exposure has so far been financial. The project was budgeted at R$28.9 billion when Brazil's development bank approved a then-record R$22.5 billion loan in November 2012, and by late 2017, actual investment had reached R$38.6 billion, roughly 34% over. The operator owed R$28.3 billion to lenders and debenture holders at the end of 2024. Aliança Norte Energia Participações, the Vale and Cemig vehicle holding a stake in the project, discloses a possible loss of R$3.05 billion from a single construction-delay claim and describes the operator's liquidity as its principal point of attention and a source of investor alert. Neoenergia wrote off its own 10% holding by R$482 million in the fourth quarter of 2021.

The physical risk has been closer than the absence of a collapse suggests. In October 2019, the operator wrote to the national water regulator declaring an emergency, because reservoir levels had fallen far enough to expose an unprotected section of the Pimental dam's earthfill base to wind-driven wave erosion and, in the company's own words, structural damage. It cut outflow below the level agreed with the environmental regulator to protect the structure, and the letter surfaced only through investigative reporting.

Beyond the environment: displacement, water, and chronic corruption

Right to property

Hydropower ranks first among infrastructure sectors for property disputes, a direct function of the footprint a dam and reservoir require. The record shows land seizures, forced displacement, compensation that arrives short or not at all, communities never consulted before ground was broken, and blasting that cracked the foundations of nearby homes. Those affected are frequently the least equipped to hold an operator to account.

Fifteen years after Belo Monte broke ground there is still no audited count of who lost their homes. Estimates run from 20,000 to 40,000 depending on the definition used, against the operator's account of rehousing some 6,000 urban families. Landowners say expropriations are priced at unadjusted 2013 values while the project's own construction boom inflated the market, and as of 2025 none of the land required for the riverine resettlement program had been bought. A petition filed with the Inter-American Commission in 2011 still has no ruling.

Community health and safety

Hydropower sits alongside coal and nuclear as a leading source of community health disputes, but it arrives by a different route. Coal delivers PM2.5, nuclear delivers radioactive anxiety, and hydropower delivers water mismanagement: overconsumption that strips farmers of a livelihood, contaminated water reaching local crops. The grievance is agricultural rather than industrial, which widens the affected population considerably.

On the Volta Grande, catch per fisher fell from 11.1 kilograms a day between 2001 and 2008 to 4.53 kilograms between 2020 and 2023. A randomized household survey found 38.5% of residents in Belo Monte's resettlement neighborhoods living with moderate or severe food insecurity, against 28.3% across the surrounding city. In June 2026, federal prosecutors sought as interim relief for 635 families along the reduced-flow stretch the emergency delivery of three and a half to five liters of drinking water per person per day.

Corruption and bribery

Corruption and bribery accounts for close to 30% of governance controversy across infrastructure. What separates hydropower is the pattern. In airports, nuclear, and coal, corruption surfaces as discrete scandals: a probe opens, executives are charged, attention fades. In hydropower it keeps returning, tied repeatedly to falsified records and payments to local officials to secure land and water rights. Isolated scandals point to isolated actors. A pattern that recurs points to how these projects get permitted.

Brazilian prosecutors alleged that Belo Monte's construction contracts carried bribes worth 1% of their value, and three contractors admitted cartel conduct and kickbacks under leniency agreements that carried immunity. Everything after that was procedural closure rather than a finding of liability: the principal defendants were acquitted and the acquittal upheld on appeal in 2024, the competition authority archived its bid-rigging case in 2025, and no individual has been convicted in connection with the project. An investor screening for enforcement outcomes would have found a closed file. The costs landed elsewhere, in permitting delay, financing conditions, and a minority stake that has been for sale since 2022 without a buyer.

Hydropower's risk concentration: what this means

Hydropower's classification as clean energy is accurate on the metric it was designed to measure, because generation is low-carbon. But carbon intensity is one dimension of sustainability, and it is not the dimension that produces operational friction, legal exposure, or the loss of a social license.

What drew sustained opposition to these projects was water rights, displaced communities, cracked foundations, converted wetlands, and permits secured through local payments. None of it appears in a carbon accounting framework.

For investors, insurers, and lenders seeking transition-aligned infrastructure exposure, that is a material blind spot: an asset class that screens well on the primary criterion while carrying the heaviest social burden in the dataset, and carrying it on behalf of people who have no employment relationship with it. Belo Monte was engineered to avoid precisely that outcome and produced it regardless, which suggests the exposure is not a function of how a dam is built but of what a dam is.

The label is not wrong. It is simply measuring something other than risk.

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In the early 2020s, sustainable investing was booming. Trillions of dollars were flowing into funds labeled "green," "sustainable," or "ESG-focused." But behind the marketing, a troubling reality emerged: many of these products weren't as sustainable as they claimed. Some funds marketed as environmentally friendly held stakes in fossil fuel companies. Others promoting social responsibility had questionable labor practices in their portfolios. Investors were confused, regulators were concerned, and the term "greenwashing" became unavoidable.

In response, the European Union introduced the Sustainable Finance Disclosure Regulation (SFDR) in March 2021. This wasn't just another piece of bureaucratic paperwork; it was the EU's ambitious attempt to bring order to the Wild West of sustainable investing. The regulation aimed to create a common language, establish clear standards, and ultimately answer a simple question that had become surprisingly complicated: "Is this investment actually sustainable?"

Today, SFDR has become one of the most influential regulations in global finance, reshaping how asset managers operate and how investors evaluate their options. But what exactly does it require, and how does it work?

What Is SFDR and Why Does It Matter

SFDR was introduced as part of the European Commission’s Action Plan on Sustainable Finance, together with the EU Taxonomy Regulation and the Low Carbon Benchmarks Regulation. As a result, the regulation became a central pillar of the EU sustainable finance framework and was designed to improve transparency in financial markets and reduce greenwashing. Adopted in 2019 and applicable since March 2021, SFDR became fully operational in January 2023 when the European Commission’s Regulatory Technical Standards came into force. These technical standards outline the requirements for reporting sustainability information, the indicators that must be disclosed, and the presentation of sustainability data.

To understand its importance, it is essential to recall the problem it sought to solve. There was no consistent or comparable way to verify sustainability claims. ESG-labeled funds had multiplied across Europe, but marketing materials often lacked meaningful evidence. This inconsistency created a significant gap in investor protection. The SFDR addressed this by introducing structured disclosure requirements that compel financial institutions to substantiate their sustainability statements with documentation, metrics, and details of their investment strategy.

Because of its broad scope, SFDR applies not only to asset managers but also to insurers, pension funds, private equity firms, investment firms, and financial advisors operating in the EU or selling investment products to EU investors. The intention is not to dictate investment choices but to give investors the information needed to make informed decisions.

How SFDR Works in Practice

To achieve this transparency, SFDR uses a classification system that groups financial products into three categories reflecting different levels of sustainability ambition. Article 6 applies to products that do not promote environmental or social characteristics. These products must still describe how sustainability risks may affect financial returns. Article 8 applies to products that promote environmental or social attributes as part of their investment strategy. These funds may integrate ESG factors, apply exclusions, or prioritise companies with strong sustainability practices. Article 8 has become the most widely used category, although the range of practices within it varies significantly. Article 9 applies to products with a specific sustainable investment objective. These funds must demonstrate how their investments contribute to environmental or social goals such as climate change mitigation, biodiversity protection, or social equity. Because expectations for Article 9 are demanding, many funds originally classified in this category were reclassified once firms better understood the requirements.

Beyond labeling, SFDR requires disclosures at both the entity and product level.

  • At the entity level, organizations must describe how they integrate sustainability risks into investment decisions, how they assess adverse impacts of their investments, and how remuneration structures support sustainability objectives. These disclosures help investors understand the firm's overall sustainability approach.
  • At the product level, SFDR requires more detailed information about each investment offering, including the sustainability characteristics promoted by the product, the investment strategy used to pursue these characteristics, the data sources and methodologies used to evaluate performance, and the limitations of the approach.

These disclosures appear in pre-contractual documents as well as in periodic reports that allow investors to monitor progress over time.

As a further layer of transparency, Principal Adverse Impact (PAI) reporting is one of the most complex elements of SFDR. PAI indicators measure the negative environmental and social impacts of investment decisions. They cover areas such as greenhouse gas emissions, biodiversity loss, water use, waste generation, labor standards, gender pay gaps, and exposure to controversial sectors. Firms with more than 500 employees must publish a PAI statement each year. Smaller firms may choose not to report, but must explain why. This represents a shift from highlighting only positive sustainability contributions to addressing potential harm as well.

Impact of SFDR on the Investment Industry

Because of its ambition, SFDR has had significant effects on European financial markets. The most visible impact was a wave of fund reclassifications in late 2022 when many asset managers downgraded Article 9 products to Article 8 after reassessing their ability to meet the requirements. This raised questions about whether some funds had overstated their sustainability ambitions.

Alongside this reassessment, the regulation increased demand for reliable ESG data, analytics, and reporting infrastructure. Asset managers expanded sustainability teams and adopted new tools to meet SFDR disclosure requirements. Private equity firms also incorporated SFDR into their due diligence processes to assess sustainability risks in portfolio companies. The influence of SFDR has extended beyond Europe, as non-EU managers serving European clients have adopted the framework, effectively exporting EU sustainability standards internationally.

Ongoing Challenges and Criticisms

Despite its progress, SFDR remains difficult to implement. Data availability is a major obstacle. Many companies, particularly those outside Europe or in private markets, do not publish the information required to calculate PAI indicators. This forces asset managers to rely on estimates or incomplete datasets.

A related challenge is the ambiguity of key terms. Concepts such as promoting environmental characteristics or defining sustainable investment are not fully standardised and have led to inconsistent interpretations. Smaller firms face disproportionate costs because the systems needed for SFDR compliance are resource-intensive. Some managers have responded by engaging in greenhushing, choosing to classify products more conservatively to avoid regulatory scrutiny. This behaviour goes against SFDR’s core objective of transparency.

There is also debate about whether SFDR measures real sustainability impact or only the quality of disclosures. Because SFDR does not require funds to achieve specific environmental or social outcomes, a fund can meet the disclosure requirements without delivering significant sustainability results. This question remains central to ongoing discussions about the future of sustainable finance regulation.

Looking Ahead: The Future of SFDR and Sustainable Finance

Looking forward, SFDR marks an important shift toward measurable and transparent sustainable finance. It encourages financial institutions to support sustainability claims with data rather than marketing language. As companies improve their ESG reporting and as data quality increases, SFDR is expected to become more effective at identifying genuine sustainable investments and reducing greenwashing.

The framework is already influencing new regulatory developments, including the United Kingdom’s Sustainability Disclosure Requirements and initiatives across Asia. As a result, SFDR may ultimately serve as a global reference point for sustainability disclosures.

For investors, the Article 6, 8, and 9 structure provides a clearer way to assess the sustainability ambition of investment products. While the system is not perfect, it offers a foundation for better comparisons. As methodologies evolve and guidance becomes clearer, SFDR will continue to shape how sustainability is evaluated and communicated across financial markets.
Ultimately, SFDR has laid the groundwork for a more transparent and accountable investment ecosystem. Its evolution will continue to influence investment strategies, due diligence processes, and the role of finance in supporting the transition to a more sustainable economy.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

In an era where information increases at an unprecedented pace, the necessity for intelligent and efficient methods to filter and analyze large datasets is more critical than ever. This need is particularly emphasized in the finance industry, where private equity firms and asset managers require real-time, AI-powered ESG monitoring to make informed investment decisions.

Harnessing the power of AI for ESG monitoring

As Tyler Cowan noted, even if one could read an article in a second, it would take a lifetime to consume the volume of data available. At SESAMm, we analyze over 20 billion records, representing 250 terabytes of dense information. The challenge is, how can professionals navigate this ocean of data in a reasonable timeframe to make critical decisions?
Natural language processing and AI-powered techniques provide the solution. These technologies enable us to comprehend and navigate a multitude of documents, from newspapers to niche blogs, in mere seconds.

The need for AI-powered ESG monitoring

For private equity firms and asset managers, AI-powered ESG monitoring is not just a trendy concept but a necessity. Identifying potential ESG controversies and understanding the impact of various ESG factors on investment portfolios is crucial for risk management and investment strategies.
At SESAMm, our approach is similar to a "machete, then sandpaper" method. We first eliminate the unnecessary information and then gradually refine the data. We construct a knowledge graph that includes a broad range of entities, from companies and executives to brands and products. By employing custom indices and advanced algorithms, we focus on the most relevant data points. And in the last year, generative AI has been helping us to refine this process even further, achieving a high level of accuracy in our results.

Leveraging AI for ESG insights

Using AI and algorithms like DistilBERT and the Universal Sentence Encoder allows us to process vast amounts of information swiftly. By utilizing a hybrid model that combines on-premises servers with cloud-based solutions, we ensure speed without compromising cost-efficiency.
Our specific workflows for identifying ESG controversies leverage this technological prowess. We understand the importance of not sending our clients on wild goose chases with false positives. Our AI-powered ESG monitoring system is designed to identify only the most relevant and likely material risks. This approach saves time and ensures our clients have the insights they need without being overwhelmed.

From vast data to actionable insights

Our journey begins with over 20 billion records, but the destination is concise, actionable insights tailored to your industry and needs. We focus on what truly matters, employing AI, NLP, and strategic data processing techniques to transform a deluge of information into a manageable stream.
For private equity and asset managers, our AI-powered ESG monitoring provides the critical insights needed to make informed decisions. By prioritizing precision and reducing noise, we ensure that the information we present is not just accurate but also relevant.

SESAMm's approach

The age of information has called for intelligent, systematic detection of ESG controversies. Through AI-powered ESG monitoring and careful consideration of unique requirements, SESAMm delivers unparalleled insights tailored to the world of finance.
If your firm is engaged in private equity or asset management and is keen on leveraging data to identify potential ESG risks and controversies, SESAMm's offerings are designed to meet your exact needs.

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.

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.

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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.

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

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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.

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.

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