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

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.

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.

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 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

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.

Orpea’s polarity is average, but it shows significant negative sentiment linked to scandals. One of those drops in opinion dates back to 2018 when a documentary highlighted abuses in private retirement homes.

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 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.

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.

The Securities and Exchange Commission (SEC) has voted to cease defending its climate disclosure regulations in court, marking a significant shift in U.S. corporate sustainability reporting requirements. This decision, announced on March 27, 2025, under Acting Chairman Mark Uyeda's leadership, has substantial implications for the ESG landscape.

The Decision

The SEC’s withdrawal from defending its climate disclosure rules comes amidst ongoing litigation before the U.S. Court of Appeals for the Eighth Circuit. Originally adopted in 2024, the rules were intended to provide investors with standardized information about companies' climate-related risks, emissions, and the financial impact of those risks. Uyeda justified the withdrawal by stating, “The goal of today’s Commission action is to cease the Commission’s involvement in the defense of the costly and unnecessarily intrusive climate change disclosure rules.”
The regulations faced swift opposition from industry trade groups and Republican state attorneys general, who argued the SEC had overstepped its authority. The legal challenge quickly gained momentum, and with the change in SEC leadership, the agency opted not to continue defending the rules.
Caroline Crenshaw, the lone Democratic commissioner, sharply criticized the move. She described it as an attempt to “unlawfully undo valid regulations” and accused her colleagues of “watching the rule’s demise while eating popcorn on the sidelines.”

Market Implications

The decision reintroduces regulatory uncertainty for companies. Many had already begun preparing internal systems and compliance structures based on the 2024 rules. Now, in the absence of a federal standard, they may be forced to rely on voluntary reporting frameworks or navigate a fragmented set of expectations from investors, states, and international markets. This lack of uniformity is likely to lead to inconsistent reporting practices and difficulties in cross-company comparisons.
Investors, meanwhile, will face greater challenges in accessing reliable and comparable data on climate-related risks. Without SEC-mandated disclosures, much of the burden of transparency shifts to individual companies and third-party ESG data providers. Investors will likely need to increase due diligence efforts, adopt varied methodologies, and potentially absorb higher costs to obtain the data needed to manage climate risk effectively.

The Broader Context

This decision does not exist in isolation—it aligns with a broader trend of regulatory rollback on climate issues in the U.S. and signals a widening divergence between American and international disclosure approaches.

The divergence creates complexity for multinational corporations that must now navigate different expectations in different jurisdictions. This fragmentation may also create competitive disadvantages for U.S.-listed firms, especially those competing for capital in more disclosure-forward markets.

SEC Leaves the ISSB

In a related move that further isolates the U.S. from international sustainability efforts, the SEC recently withdrew from two key ISSB governance groups: the IFRS Sustainability Jurisdictional Working Group and the Sustainability Standards Advisory Forum. These groups are central to building alignment on global ESG disclosure standards.

The SEC’s exit from these forums signals a significant retreat from coordinated climate disclosure initiatives and weakens the U.S. role in shaping global ESG norms.

Market Response

Despite the rollback, some companies may continue voluntary climate-related disclosures. Those that have already invested in reporting infrastructure may opt to maintain transparency to meet investor expectations, mitigate reputational risk, and support long-term sustainability goals.

Simultaneously, ESG data providers and rating agencies are expected to play a more prominent role in filling the information gap. Financial institutions may also develop their own internal frameworks to evaluate climate risks, further privatizing what was once a public regulatory function.

Looking Forward

The path ahead remains uncertain. State-level legislation may introduce a patchwork of new rules. Global investors—particularly those with mandates in the EU or UK—may continue demanding robust disclosures from U.S. firms. And future federal administrations could choose to reintroduce or reshape mandatory disclosure regimes.
In the interim, companies and investors will need to adapt by maintaining flexible reporting systems, monitoring evolving voluntary frameworks, and diversifying their sources of ESG data. While federal requirements may have receded, the underlying investor interest in climate-related financial risk is not going away. Climate disclosure, in one form or another, remains firmly on the radar.

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.