Steven Carroll Joins SESAMm's Advisory Board, Bringing Deep Financial Information Services Expertise
05/04/2026
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5 mins read
SESAMm is pleased to announce the appointment of Steven Carroll to its Advisory Board. Over a career spanning more than 25 years, Steven has built a uniquely broad perspective on financial data, having operated at a senior level across every major corner of the industry, from quant analytics and content to AI-powered research tools and global data platforms.
A Career at the Heart of Financial Data
Steven has seen financial data from every angle: quant analytics at StarMine, content and indices at Thomson Reuters, and AI-powered search at AlphaSense. At Refinitiv and then LSEG, he took on progressively broader remits, culminating in his role as Head of Customer Strategy and Execution, where he was responsible for go-to-market across Workspace, Data and Feeds, FTSE Russell, and Risk Intelligence and Analytics. Throughout, Steven's roles have sat at the intersection of product, marketing, and sales, spanning multiple geographies, including Australia, Singapore, the UK, and the United States.
Deep Roots in the Institutions and Workflows SESAMm Serves
Steven is a subject-matter expert on the content sets and workflows that underpin institutional investment and risk management, including fundamental data, estimates, broker research, ESG, sentiment, and credit analytics. He has also worked closely with the firms that consume this data, from private equity and asset managers to commercial banks and insurers, giving him a first-hand understanding of how they evaluate, adopt, and integrate new data and analytics tools into their processes.
Expanding SESAMm's Reach Across Global Financial Markets
Steven's appointment comes as SESAMm continues to expand its AI-powered risk intelligence platform and deepen its relationships with private equity firms, asset managers, commercial banks, insurers, and financial institutions globally. His perspective will provide valuable insight, bringing a practitioner's understanding of how financial data businesses grow and scale.
Steven is also the founder of CCAS (Carroll Consulting and Advisory Services), a London-based advisory practice supporting startups and established vendors across the information services ecosystem. He is a Fellow of the Chartered Management Institute and the Institute of Consulting, a member of the Institute of Directors and the CFA Institute, and serves on the Board of Governors at Greenwich Waldorf School.
We're thrilled to welcome Steven to SESAMm's Advisory Board and look forward to working together as we continue advancing AI-powered risk intelligence for investment firms and corporations worldwide.
As sustainability expectations rise, so does scrutiny. This ebook explores how industries are performing against the UNGC’s Ten Principles—and where risks are being overlooked. Backed by SESAMm’s AI-powered UNGC violations screening, it offers a data-backed view into ESG alignment and accountability.
What You'll Learn:
Commitment doesn’t always mean compliance: Public alignment with the UNGC is there, but our data reveals persistent ESG risks that often go unaddressed.
Certain sectors face heightened exposure: The technology, finance, and automotive industries consistently rank among those most frequently linked to potential breaches.
The enforcement gap is widening: A clear disconnect exists between alleged violations and actual accountability, emphasizing the need for real-time monitoring and stronger ESG oversight.
Teams that monitor ESG controversies usually have the opposite of an information shortage. A single incident can generate dozens of articles within a few days, each covering the same underlying event, often repeating the same facts with a few new details. At a certain point, the sheer number of articles makes it hard to tell which developments are material and which are just the same story told again.
The volume is the part that breaks traditional approaches. Millions of articles are written every day across hundreds of languages, more than any team of analysts could read, let alone reconcile into a clear timeline of an evolving controversy. This is not a problem you solve by adding more people; the scale is on a different order of magnitude from human reading speed.
What changed is that language models can now read, categorize, and evaluate. They cover that volume in every language, judging whether two articles describe the same incident, whether one marks a new development, and how incidents link into a single controversy over time. Leveraging the latest AI models is the only way to structure this much material and generate daily updates.
SESAMm runs this across the ten million documents it ingests each day, from more than four million sources in over 100 languages, including premium news wires, NGO bulletins, company communications, and discussion forums. The result is ESG controversies organized into three layers: articles, events, and cases.
From Articles to Events to Cases
Each layer builds on the one below it, and each answers a different question an analyst needs answered.
Articles are individual news articles or documents: the raw material.
Events group the articles that describe the same specific incident or development. When forty outlets cover the same supplier labor issue, those forty articles become a single event, with the underlying coverage attached. Articles published close together in time and describing the same development are grouped; an article describing a genuinely new development, even on the same broader topic, forms a separate event. A strike in 2022 and a similar strike in 2024 at the same supplier are recorded as two events, because they are distinct incidents rather than a continuation of one.
Cases sit above events. A case ties together the events that belong to the same underlying controversy as it unfolds, with no fixed time limit. An oil spill, the regulatory investigation that follows it, and the settlement that closes it months or years later are three separate events but one case.
Articles tell you what was written, events tell you what happened, and cases tell you how a controversy is developing. All three sit in the same view: one entry per controversy, with the chronology of events nested inside it and the source articles a click below that.
Why the Underlying Data Matters
A three-layer structure is only as good as the data underneath it. To capture a controversy from start to finish, that data has to include the early signals that appear in regional press, NGO bulletins, or non-English sources before larger outlets report them, sometimes days later.
SESAMm's coverage spans more than 100 languages and extends well beyond mainstream news wires, so its cases are built on a wider base than most monitoring platforms screen. A controversy that starts in a local-language outlet, moves through regional media, and reaches the international press is captured as a single continuous case, rather than surfacing as disconnected alerts or being missed altogether in its early stages.
What Does This Change in Practice?
Three things change in day-to-day work.
The count starts to mean something. A rise in the number of cases reflects new controversies emerging, not an old one being picked up by more outlets.
Trajectories become visible. As a case accumulates new events over the months, the progression from complaint to investigation to hearing to settlement is easy to follow, rather than being buried in hundreds or even thousands of articles.
Analysts spend their time differently. Less of it goes to clearing duplicate headlines, and more to the important judgment calls.
What This Looks Like in the SESAMm Dashboard
In the dashboard, a company appears as a single entity with its related cases listed beneath it. Each case includes a controversy summary, an ESG risk classification, and an intensity score, with related events nested underneath and the original source articles just a click away. A case that draws on hundreds of articles becomes a short, readable list instead of hundreds of separate incidents.
Every case is fully traceable. Analysts can drill from a case down to its events, and from any event to the articles that produced it. The time period is set from the top of the dashboard, so older incidents do not crowd the view when the focus is on recent activity.
Reducing Noise in Adverse Media Monitoring
In practice, those forty articles collapse into one event, and that event sits inside a single case that is still developing, caught early and drawn from sources most platforms never see.
Grouping articles into events removes duplication caused when many outlets cover the same incident. Grouping events into cases keeps a controversy intact as it develops, rather than scattering it across months of separate alerts. Because this runs across ten million documents a day in more than a hundred languages, it holds up even for controversies that start far from the mainstream press.
The result is a view where the numbers carry meaning, the direction of an issue is clear, and the underlying articles stay one click away for full validation.
As the year closes, it's time to reflect on some of our favorite moments, so we've compiled a list of the top 10 blog posts highlighting the most popular and insightful content we've published. From alternative data trends to NLP and ESG topics, 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 10 posts of the year and see what made them stand out.
It's truly humbling that this blog post, celebrating our eighth anniversary, made the top 10 list. It reflects on the progress we've made and the milestones we achieved over the past eight years. We also discuss our plans for the future and our commitment to continuing to innovate and provide high-quality solutions for our clients.
#9 3 Remarkable Trends NLP Text Mining Exposes About Used Cars & U.S. Inflation
Back in May, we revealed inflation trends in the used car market and identified specific trends and patterns that can help predict changes in the market and inform investment decisions.
Suppose you're interested in learning how AI technology can improve the efficiency and accuracy of financial analysis. In that case, you won't want to miss this blog post. In it, we discuss a case study involving Tokio Marine Nichido, one of the largest insurance companies in Japan. By partnering with SESAMm, they were able to implement our AI-powered solutions and achieve impressive results, including increased productivity and a more comprehensive understanding of their data. This post provides valuable insight into the potential of AI technology and its real-world applications in the financial industry.
Are you curious about how we use AI technology to gain valuable financial insights from news and social media? This blog post is a must-read. We discuss how SESAMm's AI-powered solutions can analyze a vast amount of data from these sources and extract valuable information that can inform investment decisions. From predicting market movements to identifying trends and patterns, our technology provides a unique and powerful way to stay ahead of the game in the financial industry.
Learn more about the role of alternative data in the financial industry. In this blog post, we discuss how SESAMm's AI technology can analyze a wide range of data sources, such as news and social media, to identify controversies and potential risks in the market. By providing a more comprehensive view of a company or investment, our solutions can help investors make informed and strategic decisions. Overall, this post offers valuable insights into the potential of alternative data and its role in the financial industry.
Read this quick guide to natural language processing, a subfield of AI technology that focuses on the interaction between computers and human language. We discuss the basics of NLP, including its history and applications, as well as the challenges and opportunities it presents. It also provides an overview of SESAMm's NLP technology and its role in the financial industry. This post offers a valuable introduction to NLP for those interested in learning more about this exciting field of AI technology.
One of the year's biggest news events was the exclusion of Tesla from the S&P 500 ESG index, a huge hit for the electric car manufacturer and a sign of the growing importance of ESG criteria in the financial industry. This blog post discusses the event's significance and provides insight into the potential of ESG data and its role in informing investment decisions.
This blog post discusses how knowledge graphs are at the core of text analysis and provide valuable insights for investors. We explain how these graphs, representing real-world entities and their relationships, can analyze large amounts of data and extract valuable information. We also discuss how SESAMm's AI technology can generate and use knowledge graphs to provide a more comprehensive view of a company or investment and support decision-making in the financial industry.
Learn how organizations use NLP technology to detect greenwashing, the practice of making false or misleading claims about a company's environmental practices. We explain how SESAMm's AI technology can analyze large amounts of data, including news and social media, to identify inconsistencies and potential risks in a company's ESG claims. This post also offers valuable insight into the potential of NLP technology to support responsible investment and combat greenwashing in the financial industry.
At number one, your favorite post is about the potential of AI technology to provide valuable insights into ESG data. Here, we discuss how SESAMm's AI-powered solutions can analyze a wide range of alternative data sources, including news and social media, to provide a more comprehensive view of a company or investment's ESG performance. This post offers valuable insight into the role of AI in responsible investment and the potential of alternative data in informing ESG analysis.
Thank you for reading through this year's 10 most-popular blog posts. Which is your favorite, and how would you rate them?
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