On 3 April 2025, the European Parliament voted to postpone the implementation deadlines of two major EU sustainability laws: the Corporate Sustainability Reporting Directive (CSRD) and the Corporate Sustainability Due Diligence Directive (CSDDD). The motion passed with an overwhelming majority of 531 votes in favor, 69 against, and 17 abstentions, supporting the European Commission’s “stop-the-clock” proposal. This vote, conducted under an urgent procedure, is part of a broader effort to streamline corporate sustainability requirements and reduce compliance burdens on companies. The Council of the EU had already endorsed the delay on 26 March 2025, citing the need to provide businesses with additional time to adapt to the directives. Final formal approval by the Council is expected shortly, after which the adjusted timelines will take effect.

Extended Deadline for Sustainability Reporting (CSRD)

The Corporate Sustainability Reporting Directive (CSRD) mandates companies to make extensive ESG disclosures. The approved delay affects the implementation timeline as follows:

• Large companies' reports delayed by 2 years: Companies defined as “large” under CSRD will now begin reporting on the financial year 2027, with the first sustainability reports published in 2028. Previously, these companies were expected to commence reporting for the financial year 2025, with reports published in 2026.
• Listed SMEs granted additional time: Listed small and medium-sized enterprises (SMEs) and other qualifying small companies will commence CSRD reporting one year later than initially scheduled, covering their financial year 2028 data in reports published in 2029. Under the original plan, these SMEs were to begin reporting for the financial year 2027, with an option to opt out until 2028.

Companies already within the scope of EU sustainability reporting (large public-interest entities under the previous Non-Financial Reporting Directive) are largely unaffected by this delay and have begun reporting for the financial year 2024 as planned. For the rest of the corporate sector, the CSRD’s effective start is deferred, providing additional time to build reporting systems and comply with the European Sustainability Reporting Standards (ESRS). The European Commission has tasked the European Financial Reporting Advisory Group (EFRAG) with simplifying and streamlining the reporting standards by late October 2025, enabling companies to adopt a more manageable set of disclosures when reporting begins.

One-Year Postponement for Due Diligence Rules (CSDDD)

The Parliament’s vote also extends the timeline for the Corporate Sustainability Due Diligence Directive (CSDDD), an EU law requiring companies to identify and mitigate human rights and environmental impacts in their operations and supply chains. The adopted delay includes:

• Transportation deadline extended: EU Member States now have until 26 July 2027 to transpose the CSDDD into national law, a one-year extension from the original July 2026 deadline. This extension allows governments to pass national legislation implementing the due diligence requirements.
• First corporate compliance phase delayed to 2028: The initial wave of companies subject to the CSDDD will have an additional year before the rules apply. Large EU firms with over 5,000 employees and €1.5 billion+ in turnover (and non-EU companies with equivalent EU turnover) must begin complying in July 2028 rather than 2027. Notably, this July 2028 phase will also cover companies with over 3,000 employees and €900 million turnover, effectively merging the directive’s first two implementation waves into one timeline.
• Subsequent phase in 2029: The next set of in-scope companies, including those with ≥1,000 employees and €450 million in turnover, are expected to come under the CSDDD by July 2029 as previously scheduled. The overall phase-in period is compressed into two stages (2028 and 2029) rather than spanning 2027–2029. This compressed rollout means the largest companies gain a one-year reprieve, while the smaller large companies will enter only slightly later than initially planned.
  

Next Steps

While this vote confirms a delay in implementation, negotiations regarding bigger changes to the laws (updating the reporting standards and the scope of companies affected) are still in their early stages. Those negotiations include exempting an estimated 80% of the companies initially covered by only applying these regulations only to firms with more than 1,000 employees. We delve deeper into these developments in our recent summary of the Omnibus initiative.

About SESAMm

SESAMm is a global leader in ESG controversy data, using advanced Generative AI. We automate monitoring and due diligence on public and private assets, providing coverage of more than 5 million companies. Our clients include companies like Carlyle, Warburg, Natixis, RBI, Fitch, Oddo, and more. SESAMm has raised $50M from renowned investors and operates across 4 continents.
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Sources

• European Parliament Press Release:  Parlement Européen
• Reuters Article on EU Parliament Vote:  Reuters
• EFRAG Announcement on ESRS Simplification:  EFRAG

SESAMm’s AI Technology Reveals ESG Insights

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Big data.

It’s a phrase that’s been thrown around for the last two or three decades—maybe too much in some cases. But it’s a short, catchy phrase. It sums up how we want to describe the amount of data we produce and have to deal with today.

To be clear, when we say “big data,” we mean big data analytics. It’s so much data that we can’t possibly grasp it in any human way, at least not reasonably. It’s coming from everywhere, growing exponentially, and coming at us faster and faster every day. In other words, the person-power it would take to process and analyze big data wouldn’t be feasible or affordable. So, we need help. We need data science. And we need a different type of intelligence: artificial intelligence. But more on that later.

Obviously, the use of big data comes with challenges. But big data initiatives are worth the cost and effort because what we can extract and analyze from it helps us understand the world and how it works at a macro-level. It also helps us dig into details and understand what’s happening at a micro-level. For example, businesses create lots of data in the Finance and Insurance industry. So extracting and analyzing big data can provide insights for investors when making investment decisions.

What is big data in finance?

Big data in finance is the immense amounts of diverse and complex data that banks, financial institutions, and investors use to understand consumer behavior, gain insight into possible investments, and create investment strategies. In other words, this data is primarily used by and for the financial services sector.

How big is big data anyway?

How big big data is depends on the amount of data being sourced, also known as data mining. If we were to consider how much data volume the world produces, it’s “at least 2.5 quintillion bytes of data” daily, according to  CloudTweaks. That’s 2,500,000,000,000,000,000 bytes.

We usually measure big data—structured and unstructured data—in petabytes (PB) and terabytes (TB). A petabyte is 1024TB or a million gigabytes (GB). To put this amount of data into perspective, let’s use the newest iPhone as an example. Today’s iPhone can store up to 1TB of data. That means 1PB would equal the amount of data 1024 iPhones can store.

Other big-data challenges

Managing big data’s size is an obvious challenge, but big data comes with even more challenges. For example, any origin that produces or stores data can be a big data source, including social media. Thus, we often gather data from disparate sources.

Big data is also ever-growing. So in dealing with an ever-growing amount of data, we must ensure proper data processing, data management, and data integrity. Our data scientists, for instance, spend a good chunk of their time curating and preparing the data to make sure it’s valuable and clean.

Finally, after we’ve ensured data quality, we need AI to help us make sense of the data we’ve curated. In our case, we use natural language processing (NLP) to read more than 20 billion articles, messages, and forums to make sense of the textual data to enable our clients with multiple use cases, including signals for investment strategies, due diligences on private companies, and ESG controversy monitoring, among others.

How big data is used in the finance industry

Big data is used in many sectors and industries, and in some cases, it’s changing financial business models. However, big data technology has been used in the financial services industry in three key ways: to gain stock market insights, to detect and prevent fraud, and accurately analyze risk.

For instance, through machine learning—using computer algorithms to find patterns in massive amounts of data—data scientists can conduct a deeper data analysis in the financial markets beyond stock market data like stock prices, considering factors such as social and political trends. In some cases, this big data analysis can be provided in real time.

Machine learning also helps with fraud detection. It helps mitigate security risks through monitoring and analyzing customer data like buying patterns around credit cards, for example.

Further, machine learning helps with risk management. Investors can rely on machine learning’s unbiased output from alternative and financial data to predictive analytics, helping identify potential risks or great investment opportunities. Banks use these strategies to analyze business borrowers’ potential defaults, for example.

Other areas big data can provide a competitive advantage in the fintech industry:

• Algorithmic trading
• Chatbots and robotic process automation
• Customer segmentation
• Customer satisfaction

SESAMm leverages AI and big data for better investment decisions

SESAMm is a leading NLP technology company, and we serve global financial organizations, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms. We provide datasets or NLP capabilities to enable our clients to generate their own alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporation studies, and more. With access to SESAMm’s massive data lake, made up of more than 20 billion articles, forums, and messages, our clients can improve their decision-making process.

Request a TextReveal® demo to see how you can leverage big data for your investment decisions today.

By Magnus Billing, SESAMm advisor, with insights from Sylvain Forté, CEO of SESAMm

Investors have faced so-called “black swan” events throughout history: unexpected crises with severe consequences, often rationalized only in hindsight. Yet in an era defined by generative AI and vast, real-time data lakes, the question arises: could such events be understood and acted upon before they unfold?

The 2023 U.S. regional banking crisis offers a striking case study. The rapid collapses of Silicon Valley Bank and Signature Bank revealed how quickly stress can spread and how difficult it remains to connect early warning signs across sources.

While traditional financial analysis focuses on fundamentals such as capital ratios, liquidity positions, governance, and earnings, a new class of tools is expanding the lens. AI-driven controversy data aggregates and analyzes millions of public sources, from regulatory statements to media and industry discussions, to detect emerging issues as they surface. It does not replace quantitative and fundamental analysis; it complements it by tracking the visibility of risk as it enters public conversation.

This combination of approaches may offer investors a fuller picture: the structural risks visible in balance sheets, and the narrative risks revealed through public dialogue. To test this idea, we revisited the 2023 crisis through both perspectives, starting with what traditional analysis could have shown and what it missed.

Traditional Analysis and Its Blind Spots

In hindsight, the vulnerabilities of regional banks such as Silicon Valley Bank and Signature Bank were visible before the start of 2023. Unrealized losses on long-term securities, heavy reliance on uninsured deposits, and exposure to interest-rate risk pointed to potential liquidity stress. Yet these indicators were neither fully recognized nor connected in the market.

Traditional analysis has a tendency to evaluate banks based on their specific niches: Silicon Valley Bank focused on technology and venture financing, while Signature Bank served commercial real estate and digital asset clients. However, this approach risks overlooking the common and shared structural factors: concentrated depositor bases, high sensitivity to interest rate changes, rapid growth, and weaknesses in governance. Few, if any, observers recognized how rapidly these vulnerabilities could interact and escalate in a modern, digitalized banking environment.

While financial reports contained the data, there was little discussion connecting these risks in the public domain. But what about controversy data? Would it have caught the impending crisis? To find out, I asked Sylvain Forté, CEO of SESAMm, to provide an AI perspective.

What the Data Showed: Signature Bank

Signature Bank displayed a gradual pattern of emerging risk visible through public discussion. From mid-2022 onward, controversy data showed a rise in coverage related to governance practices, management oversight, and deposit concentration risks, often in the context of its ties to the digital-asset industry.

Importantly, it was not the crypto exposure itself that led to the bank’s collapse. The bank even announced in December 2022 that it would reduce its crypto-related business. Instead, the FDIC’s Supervision of Signature Bank report concluded that, “the root cause of SBNY’s failure was poor management. SBNY’s board of directors and management pursued rapid, unrestrained growth without developing and maintaining adequate risk management practices and controls.”

From a controversy perspective, those signals were publicly visible but fragmented. As shown in the chart above, AI-powered monitoring could have aggregated them into a clear view of a sustained drift in governance-related discussions, offering an early indication that oversight and internal controls were under pressure and risk was increasing.

What the Data Missed: Silicon Valley Bank

In contrast, Silicon Valley Bank presented a markedly different pattern. While controversy data registered some activity in late 2022,  including investor reactions to financial forecasts and coverage of routine business operations, these signals were fundamentally different in character from Signature Bank's governance-related warnings.

The September 2022 increase reflected market disappointment with financial guidance rather than operational or governance concerns. The subsequent activity captured normal business news, such as arranging syndicated loans. Critically, there was minimal public discussion of the bank's balance-sheet structure, unrealized losses, or depositor concentration risk until the crisis was already unfolding in March 2023.

This example underscores a key distinction: AI controversy monitoring excels at capturing reputational, governance, and operational risks as they enter public dialogue, but may not surface structural financial risks that remain confined to regulatory filings and analyst reports.

Lessons from Both Cases

The contrast between these two banks illustrates the complementary roles of quantitative and fundamental financial analysis vs AI-driven controversy monitoring.

In Signature Bank’s case, controversy data captured a steady accumulation of governance-related warnings, a slow build-up of risk visible through public discussion.

In Silicon Valley Bank’s case, the risks were structural but not yet discussed, leaving little for AI-powered controversy data to detect.

As Sylvain explains, “AI controversy monitoring helps investors understand how and when risks start to emerge in public dialogue. It does not replace fundamental analysis. It complements it by showing when the conversation begins to shift.”

Conclusion

Black swan events are often rationalized only in hindsight, but the 2023 regional banking crisis suggests a more nuanced reality. Some signals existed. What remained difficult was connecting them across sources before stress became contagion.

AI-driven controversy monitoring proved effective at surfacing governance and operational risks as they entered public dialogue, as Signature Bank demonstrated. Yet structural financial vulnerabilities like those at Silicon Valley Bank may not generate discussion until crisis forces the conversation, underscoring that no single lens captures all risk.

The advantage lies not in prediction, but in preparation: combining the structural risks visible in balance sheets with the narrative risks revealed through public discourse. In an era of real-time data and generative AI, the question is no longer whether information exists, but whether investors can connect it before it becomes consensus.

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.