In theory, a portfolio with no ESG controversies signals low risk. In practice, experienced analysts treat it as a warning sign. The absence of alerts often reflects not resilience, but limited coverage, fragmented data, or incomplete aggregation. What looks like reassurance may instead point to a gap in visibility.

This dynamic came up repeatedly during our recent webinar, Private Markets in 2026: Macro Trends, ESG Shifts, AI Innovation and What It Means for Deal-Flow. The discussion highlighted how gaps in coverage and aggregation can shape investor perception, particularly when portfolios appear “quiet,” not because risks are absent, but because relevant information is not being captured.

This dynamic matters more than ever as private market due diligence intensifies. With fewer deals, longer holding periods, and higher selectivity, investors are spending more time scrutinizing assets before acquisition and monitoring them for longer after entry. Yet the informational foundation behind many ESG assessments has not caught up with these expectations.

When "No Data" Becomes "No Risk"

Private assets operate under persistent disclosure constraints. Unlike public companies, most private firms do not produce standardized, recurring ESG disclosures, nor do they benefit from consistent analyst coverage. These gaps are structural and unlikely to disappear in the near term.

In this context, silence is ambiguous. A clean ESG screen may indicate the absence of material issues, but it may just as easily signal that no relevant information was captured. Language limitations, fragmented sources, and uneven coverage across geographies and asset types all contribute to this uncertainty.

This dynamic is particularly visible in secondary transactions. Deal teams often need to assess large portfolios under tight time pressure, with limited access to management and incomplete identifiers. In such cases, relying on the absence of signals can create false confidence rather than reduce risk.

How Weak Coverage and Duplicated Signals Create Blind Spots

Even when information exists, it is not always immediately actionable. Adverse media has become a valuable substitute where structured ESG data is limited, offering outside-in visibility into private assets. However, it is not without challenges. Without robust aggregation and cross-language consolidation, the same issue can appear repeatedly across multiple articles, jurisdictions, and languages, creating duplication rather than clarity. At the same time, gaps in coverage or weak filtering can allow other material risks to go undetected.

At the same time, some portfolios appear unusually quiet simply because the underlying assets fall outside the scope of traditional datasets. ESG and reputational expectations in private markets remain fragmented, with bespoke workflows driven by LP-specific requirements. This lack of convergence makes it difficult to distinguish between genuinely low exposure and analytical gaps.

More data does not automatically resolve this problem. Without traceability, source quality, and a way to assess financial, legal, or operational materiality, increased volume can add noise without improving decisions. In that environment, silence can be just as misleading as signal overload.

What Meaningful ESG Visibility Looks Like Under Disclosure Constraints

A core takeaway from the webinar was that point-in-time ESG assessments are no longer fit for purpose in private markets. A single diligence exercise conducted at entry cannot capture emerging governance failures, litigation, reputational issues, or supply chain risks over multi-year holding periods.

Instead, meaningful ESG visibility combines three elements:

• Broad coverage, to avoid portfolios appearing "low risk" simply because assets are not captured.
• Aggregation and severity assessment, to separate isolated news from controversies with real financial or operational implications.
• Continuous monitoring, so the original risk thesis evolves as new information emerges rather than remaining static.

This approach reframes ESG from a compliance exercise into a source of informational advantage. Rather than concluding that no alerts mean no risk, investors use ESG signals to guide follow-up questions, prioritize deeper diligence, and identify issues that were not visible at entry.

Replacing False Comfort with Informed Uncertainty

Private markets will continue to operate with imperfect information. Disclosure gaps, opaque supply chains, and bespoke reporting demands are inherent to the asset class.

Treating “no issues detected” as a conclusion creates false comfort. Treating it as a hypothesis, contingent on coverage quality and monitoring depth, aligns ESG analysis with how risk actually emerges in private assets.

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.

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.

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

Unprecedented Evolution in Generative AI

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

The Disruption in Financial Sector

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

Concrete Use Cases of Generative AI in Finance

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

Overcoming Roadblocks

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

Generative AI and Risk Mitigation

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

Improving Investment Decision-Making

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

Streamlining Operations for Efficiency

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

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

Reach out to SESAMm

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