SFDR 2.0: A Reset for Sustainable Fund Classification
01/20/2026
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5 mins read
The Sustainable Finance Disclosure Regulation (SFDR) is the EU’s framework for governing how sustainability considerations are disclosed in investment products. While designed to improve transparency and reduce greenwashing, SFDR gradually evolved into a de facto labelling system, with Articles 8 and 9 shaping how funds were marketed and perceived.
In late 2025, the European Commission put together an SFDR 2.0 proposal. It’s intended to acknowledge that this approach has created complexity, inconsistency, and confusion for investors. By shifting toward clearer product categories and simpler disclosures, the reform aims to restore credibility and usability.
A New Category-Based Framework
SFDR 2.0 introduces three product categories: Sustainable, Transition, and ESG Basics. While categorization is voluntary, funds choosing a category must meet mandatory criteria for that classification. Each category is tied to a minimum 70% portfolio alignment with the stated strategy, alongside mandatory exclusions for activities such as those involving controversial weapons, tobacco, hard coal, and severe breaches of international norms. Products outside these categories face tighter limits on ESG-related naming and marketing claims.
Sustainable products are reserved for funds investing primarily in sustainable activities or assets, including taxonomy-aligned strategies and Paris-aligned benchmarks. These products are subject to the strictest fossil fuel exclusions, including a ban on new coal, oil, and gas development.
Transition products are designed to capture strategies financing the shift toward sustainability. They rely on credible transition plans, science-based targets, and structured engagement, with tighter restrictions on fossil fuels than ESG Basics products and a clear focus on forward-looking change.
ESG Basics products integrate ESG approaches in the investment strategy but do not qualify as Sustainable or Transition. While still subject to baseline exclusions and the 70% alignment rule, this category has drawn early criticism for its relatively lenient treatment of fossil fuels.
Less Complexity, Tighter Guardrails
SFDR 2.0 removes entity-level PAI disclosures and simplifies product templates. Rather than relying on the current sustainable investment definition and DNSH mechanics in SFDR 1.0, the proposal operationalizes ‘no harm’ and safeguards through a common exclusion baseline plus product-level disclosure of principal adverse impacts, with DNSH and good governance reflected via category criteria. Disclosures are significantly shortened, with pre-contractual and periodic reports capped at two pages, and marketing rules tightened to limit sustainability claims to qualifying products.
The intent is clear. SFDR 2.0 shifts from dense, technical disclosures toward clearer categories supported by exclusions and simpler safeguards.
Timeline and Market Impact
The legislative process is expected to conclude in late 2026 or early 2027, followed by an 18-month implementation period. Until then, asset managers must continue complying with SFDR 1.0 while preparing for a full reclassification of their product ranges.
For the market, this likely means a smaller but more clearly defined universe of labelled funds. Many current Article 8 and 9 products are expected to reclassify, while Sustainable products under the new regime may be fewer but broader in scope. Asset managers face near-term transition costs and communication challenges, but also the prospect of greater long-term clarity and reduced compliance complexity.
A Reform Still Under Scrutiny
Initial reactions have been mixed. Industry groups broadly welcome the simplification and stronger fossil fuel exclusions for Sustainable and Transition products. At the same time, concerns persist regarding the scope of the ESG Basics category, the lack of a level playing field for unclassified funds, and the absence of more stringent engagement requirements for transition strategies. Organizations such as Eurosif and Morningstar have described the proposal as a step forward that still leaves room for improvement, particularly in preventing greenwashing at the lower end of the spectrum. Triodos Investment Management has also voiced similar caution.
What SFDR 2.0 Signals
SFDR 2.0 reflects a broader recalibration in EU sustainable finance policy. After years of expanding disclosure requirements, the focus is shifting toward usability, clarity, and enforceable standards. For asset managers, the message is straightforward: Sustainability claims will be more tightly defined, product positioning will matter more, and the margin for ambiguity is narrowing as SFDR enters its next phase.
If I told you that I had a crystal ball and could predict the future, you’d probably laugh in my face. But what if I told you that this crystal ball could give you seemingly invisible data indicating what the future is likely to be, helping you make better investment decisions? Did your ears perk up? I bet they did.
Alternative data, specifically natural language processing (NLP)-generated alternative data, is like a crystal ball. It can help portfolio managers, analysts, and public equity investment managers make better decisions by identifying controversies about a company or potential investment before mainstream data providers and ESG rating firms can. That means you can take data-informed actions before a possible change in your investment value occurs.
That was a lot, so before we go further, let’s cover a quick basic as a refresher.
What is alternative data?
Alternative data is non-traditional information extracted from non-traditional data sources, such as internet social media communities and deeper-level article data. This subset of big data is often nonfinancial and unstructured.
Why use alternative data for finance?
In financial services, alternative data sets give investors insight into the investment process and guide their investment strategies. For example, quant hedge fund managers, asset managers, and private equity firms use alternative data to augment conventional data like those that come from quarterly financial statements and SEC filings. This unconventional data can reveal insights such as metrics on environmental, social, and corporate governance (ESG) information, sentiment analysis, and consumer behavior.
Where does alternative data come from?
Firms, such as data vendors or alternative data providers, find raw data from various sources, depending on the details you need. For instance, they can pull data from transaction data, like credit card transactions, text data from social media platforms and obscure media publishers. They can also extract information from technologies like satellite imagery and geolocation data, IoT sensors, web traffic, app usage, and new data sources yet to exist. All to say, alternative-data sources are found anywhere unconventional, valuable data live.
How does NLP-generated alternative data differ?
NLP-generated alternative data is more than raw data collection and presentation. Instead, it reveals the hard-to-see data and interprets it so you can make better decisions. At SESAMm, for example, we generate alternative data from text using NLP algorithms on a massive, ready-to-use data lake to identify noteworthy trends. Our developers and data scientists then use their machine learning technology to analyze these trends and build investment strategies for our clients.
How can alternative data identify controversies before mainstream providers and ESG rating firms?
There are two main ways alternative data identifies controversies before mainstream providers and ESG rating firms:
First, NLP-generated alternative data’s inherent quality is that it can reveal trends that mainstream providers and ESG firms can’t. And because of this quality—the ability to identify and analyze trends—you can use it to see warnings before a major controversy hits the mainstream.
Second, rating providers can be inconsistent and inaccurate, according to Andrew McLaughlin, a contributor to The Globe and Mail. He states that many ESG rating providers, for instance, are “popping up like dandelions,” and “each uses its own methodologies to rank and score publicly traded companies based on their purported environmental, social and governance risk and performance.” Further, “[their] reports produced are at times rife with inaccuracies,” McLaughlin says. While we at SESAMm might not agree with McLaughlin completely, we believe that alternative data helps bridge the gap between possible shortcomings and a more comprehensive view of an investment’s risks and opportunities.
2 NLP-generated alternative data use cases as examples:
Ericsson (ERIC) analysis
Event: On February 16, 2022, Ericsson investigates an in-house bribery scandal tied to ISIS. According to FIERCE Wireless, “investors reacted to reports that Ericsson may have made payments to the ISIS terror organization to gain access to certain transport routes in Iraq.”
Results: Ericsson’s share value dropped by at least 15% that day as news broke and investors reacted. “It was its biggest share drop in a day since July 2017,” per FIERCE Wireless.
What did NLP-generated alternative data see?
In Ericsson’s case, we analyzed three areas from January 2016 to the event on February 16, 2022:
Name-mention volume
Sentiment polarity
ESG Initiatives Score
Figure 1: Volume over time chart for Ericsson
In Figure 1, we chart our analysis of data volumes, indicating spikes to help detect significant positive or negative events. For instance, the payment scandal similarly affected mention volume as a controversy in 2020. Mentions related to the more recent events continue to increase, making it potentially Ericsson’s most controversial issue so far.
Figure 2: Polarity over time chart for Ericsson
In Figure 2, we analyze Ericsson’s polarity over time. Polarity represents the aggregate of positive and negative sentiment (opinions, reviews) on a company. It can range from -1 to 1. A 0 score means that as much positive as negative sentiment is expressed. High e-reputation brands can have polarity scores over 0.7, based on SESAMm’s research and findings.
Ericsson’s overall polarity sits in the average range for the most part. However, we found that Ericsson’s sentiment suffered significant negative drops caused by controversial news. In other words, the company’s reputation has been affected several times over the years, with the most recent controversies going viral and perceived as very negative.
Figure 3: ESG Score over time for Ericsson
In Figure 3, SESAMm used the analyzed areas and comparisons to compute an ESG Score based on proprietary ESG initiatives data. The scale ranges from 0 to 1, with zero indicating a low and undesirable value and one having a higher and desirable value. We score Ericsson in the 0.05–0.10 range, which we think is relatively low for this company. Despite Ericsson increasing its ESG initiatives over the past year, recent controversies have affected its score negatively.
Figure 4: Ericsson’s ESG risks over time compared to its stock price
Figure 4 charts Ericsson’s ESG risk, which is based on SESAMm’s web data. The range varies from 0 to 1, zero indicating the lowest risk and one as the highest. Ericsson’s score from its latest scandal is a 1. Compared to Ericsson’s stock prices, several spikes in ESG risk anticipated market movements.
Orpea SA (ORP:FP) analysis
Event: On January 24, 2022, Le Monde published an article about the book “Les Fossoyeurs”. According to Le Monde, the book concentrates most of its attacks on Orpéa, a top nursing homes and clinics company, employing “65,000 employees in 1,100 establishments across the planet; 220 nursing homes in France alone.” The book’s author attacks the “Orpea system” and reveals reported elderly abuse and deaths possibly caused by it or negligence.
The media begins to question the limits of ESG rating because of Orpea’s scandal.
Results: Two things occurred after the news broke. One, Orpea’s stock price sustained a 44-point drop. Two, the media begins to question the limits of ESG rating, given Orpea’s rating at the time.
What did NLP-generated alternative data see?
In Orpea’s case, we analyzed three areas from January 2016 to the event on February 16, 2022:
Name-mention volume
Sentiment polarity
ESG Initiatives Score
Figure 5: Volume over time chart for Orpea
In Figure 5, we analyzed volumes of data and compared them with significant events detected. Volume spikes detect clear, negative events in Orpea’s case. For instance, on January 24, 2022, the breaking news had the highest effect since 2016. It’s worthy to note that an upward mention trend becomes visible before the scandal emerges, with volumes reaching levels higher than average.
ESG scores, which range from 0 to 1, are relatively low for Orpea on average. Its controversies have strongly affected its scores in 2018 and 2022 in particular. But the trend to see in the chart is that Orpea’s ESG score had been trending downward for several months before Le Monde’s breaking story.
Figure 8:Orpea’s ESG risks over time compared to its stock price
Figure 8 charts Orpea’s ESG risk, which is based on SESAMm’s web data. The range varies from 0 to 1, zero indicating the lowest risk and one as the highest. Ericsson’s score from its latest scandal is a 1. Compared to Orpea’s stock prices, several spikes in ESG risk anticipated market movements. The current controversy, while very viral, represents a risk equivalent to the 2018 revelations.
Summarizing SESAMm’s Ericsson and Orpea findings
NLP-generated alternative data was able to see trends and events that mainstream ESG rating firms didn’t in the Ericsson and Orpea cases. In both cases, SESAMm would’ve flagged controversies in at least three key areas, name-mention volume, sentiment polarity, and ESG Initiatives Score. And these three areas, with additional proprietary analysis from SESAMm, would’ve provided much-needed insight to investors before their respective market-moving events had occurred.
How SESAMm’s NLP-generated alternative data can help you
Whether for fundamental, quantitative, or quantamental investment use cases, to monitor your corporate risks, or to conduct advanced due diligence on private companies for investment opportunities, explore limitless possibilities using SESAMm’s industry-leading data lake. Our data lake consists of nearly 20 billion articles today, and it’s growing by 20% every year. And if our data lake is our crystal ball, then TextReveal® is what fuels its magic. The data, in conjunction with TextReveal’s NLP algorithms, can reveal alternative data, such as emotion and sentiment data and ESG and risk metrics, on more than 70 million entities like:
Assets
Brands
Product reviews
C-level people
And more
And you can easily access valuable alerts and predictive insights—from live daily or historical data—through dashboards, APIs, or flat files delivered in usable formats. Are you ready to uncover the invisible data about your investments? Request a demo today.
As the year closes, it's time to reflect on some of your favorite pieces of content, so we've compiled a list of the top 8 blog posts highlighting the most popular and insightful content we've published. From investor guides to detecting greenwashing practices, these posts have resonated with you, our readers, and hopefully, continue to provide valuable information and inspiration. Join us as we look back at the top 8 posts of the year and see what made them stand out.
This piece explains the pivotal role of knowledge graphs in enhancing text analysis for investors. It clarifies how these graphs contribute to a more nuanced understanding of data, aiding in investment decisions. Readers appreciated the clear, practical insights into how knowledge graphs strengthen SESAMm’s cutting-edge AI technology.
In this article, Sylvain Forté presents an optimistic view of the future of AI in the financial sector, focusing on the rapid advancements in AI. He discusses how these technologies are evolving and what this means for investors and companies alike. Readers were captivated by the forward-looking perspective and practical implications of these innovations.
This article serves as a guide on the relationship between Sustainable Development Goals (SDGs) and AI. It explains how AI facilitates the identification and tracking of SDG-aligned investment opportunities. Our audience found the straightforward approach and practical examples particularly enlightening for understanding the intersection of sustainability and technology.
Highlighting SESAMm's innovative approach, this piece details the incorporation of generative AI into ESG risk mitigation. It underscores the significant improvements in process efficiency and accuracy, which resonated well with our audience, particularly those keen on technological advancements in finance.
This guide offers a practical look at how AI is revolutionizing and simplifying ESG data for investors. It provides real-world examples and strategies, making it a favorite among readers for its direct application in their investment processes.
This article addresses the increasingly relevant issue of greenwashing, showcasing how AI tools are employed to detect and mitigate it. The relevance of this topic in today’s sustainability-focused investment landscape made this article highly popular among our readers.
This comprehensive ebook gained popularity for its detailed exploration of AI’s role in distinguishing between genuine and deceptive sustainability initiatives. It provided readers with a deeper understanding of the intricacies involved in evaluating sustainability claims, making it a valuable resource. Download the ebook.
Topping our list is the announcement of SESAMm’s Series B2 funding. This milestone article not only signifies SESAMm's growth and success but also reflects the increasing importance of ESG and sentiment analysis in the financial world. The article’s blend of business success and industry relevance made it the year’s highlight for our readers.
Thank you for reading through this year's eight most popular blog posts. Which is your favorite, and how would you rate them?
At the RBI Innovation Summit in November 2023, SESAMm's CEO, Sylvain Forté, and Suleiman Arabiat, Senior Investment Manager at Elevator Ventures, shared an interview about the intersection of artificial intelligence and ESG data analytics. This conversation highlighted SESAMm's commitment to revolutionizing how ESG data is analyzed and utilized in the financial sector.
Sylvain Forté, SESAMm's CEO and co-founder, illustrated the company's impact in detecting ESG controversies using advanced AI. By processing billions of documents, SESAMm offers a unique capability to identify environmental, social, and governance issues that influence companies. This cutting-edge approach is particularly important for private equity firms, asset managers, banks, and corporations, providing them with critical data for informed decision-making.
The interview dove into the essence of ESG – encompassing environmental, social, and governance topics – and its growing importance in regulatory frameworks worldwide. SESAMm’s AI-driven technology scans online content in over 100 languages, from major media publications to niche NGO websites, to detect and alert clients about potential controversies.
Forté shared the birth of SESAMm, tracing back to 2014 when the initial idea burgeoned from a passion for AI and its application in text analysis. This nascent idea evolved into a specialized focus on ESG controversy analysis, aligning with the increasing regulatory emphasis on sustainable investment strategies.
One of the major challenges SESAMm faced was maintaining focus while leveraging its complex technology platform for the right use cases. This journey led us to tailor our technology for end business users, aligning with the company's growth and scalability goals. As we continue to expand, particularly in the US market and private equity sector, we remain committed to enhancing our offerings in asset management and exploring partnerships in the fintech space. This journey reflects a fusion of technological innovation and dedication to sustainable investment practices, signaling a transformative era in ESG data analytics powered by AI.
To gain deeper insights into how SESAMm is shaping the future of ESG data analytics with AI, watch the full interview between SESAMm's CEO, Sylvain Forté, and Suleiman Arabiat at the RBI Innovation Summit.
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