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.
Summer is almost over for us in the northern hemisphere. (We know. It's sad for us, too.) And with this seasonal shift comes back-to-school and back-to-work activities, including taking a last-minute vacation. And vacations mean time for reading, right?
While they may not be beach reads, we think we have some great choices. These are the posts that have been most popular on SESAMm's blog in the past five months. Let's get started with SESAMm's most-read blog posts since this spring, starting with number 10.
Read this quick guide about what natural language processing is, how it’s used, why it's important to uncover financial alternative data. Bonus: Get an overview of how NLP works at SESAMm.
Review SESAMm's analysis based on its ready-to-use data streams, revealing red flags that support the decision to oust Tesla, Inc. from the S&P 500 ESG Index.
Watch CEO Sylvain Forté at Japan Investor Forum, discussing ESG data, its challenges, and how to use AI and NLP to generate insights on millions of companies.
See how we apply our NLP capabilities to identify companies likely to engage in greenwashing practices by analyzing text in billions of web-based articles.
Based on alternative data, discover how Elon Musk’s personal and related brands measure up to public sentiment following his failed acquisition of Twitter.
Discover why SESAMm’s data lake is ideal for investment research and other basics like what a data lake is, why it’s important, what it does, and how it works.
Learn how SESAMm’s AI and NLP platform is used to gain financial and ESG insights from alternative data for systematic trading, fundamental research, and more.
Learn what SESAMm’s Knowledge Graph is, what it does, and how it’s used in text analysis for financial research, such as in private equity and hedge funds.
Tokio Marine & Nichido Fire Insurance Company and SESAMm work together to predict stock price movements using NLP-generated data from news and social media.
Thank you for reading through our Summer Roundup: the 10 most-read blog posts this year.
Which is your favorite? How would you rate these posts? Let us know what you think on Twitter or LinkedIn.
SESAMm is delighted to welcome Magnus Billing to its Advisory Board. With more than 30 years of experience at the intersection of finance, technology, and sustainability, Magnus brings a wealth of knowledge and global perspective to support SESAMm’s mission of advancing AI-powered ESG and reputational risk analysis.
“Magnus has been a driving force in the evolution of sustainable finance, combining deep regulatory insight with a strong understanding of how data and technology can accelerate change,” said Sylvain Forté, CEO and Co-Founder of SESAMm. “His experience and perspective will be invaluable as we continue to scale globally and strengthen our partnerships with leading financial institutions in the Nordics and beyond.”
Magnus has held several senior leadership roles throughout his career, including CEO of Alecta, Sweden’s largest pension fund with approximately USD 100 billion in assets under management, and CEO of Nasdaq Nordics and Baltic Markets. Earlier in his career, he served as Chief Legal Counsel for Nasdaq Europe, overseeing market regulation and governance across multiple jurisdictions.
Beyond his corporate leadership, Magnus has played an influential role in shaping the global sustainable finance agenda. As a member of the EU High-Level Expert Group (HLEG) on Sustainable Finance, he contributed to the development of the EU Sustainable Finance Action Plan, which continues to guide regulatory frameworks across Europe.
Today, Magnus serves as a non-executive director and senior advisor to organizations advancing sustainability and development finance. He was recently appointed Independent Chair of the Future of Sustainable Data Alliance (FoSDA), further expanding his contribution to advancing sustainability data and analytics worldwide.
“SESAMm’s work at the intersection of AI and sustainable finance is both innovative and impactful. I look forward to supporting the company’s mission to help financial institutions better understand and act on ESG and reputational risks,” Magnus stated.
We’re thrilled to welcome Magnus to SESAMm’s Advisory Board and look forward to working together as we continue to advance AI-powered ESG and reputational risk analysis worldwide.
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.
In this issue of the "what investors ought to know about…" series, we'll cover natural language processing (NLP), a tool that draws from the computer science and computational linguistics disciplines. In the last topic, we discussed knowledge graphs as the core of text analysis. And if knowledge graphs are the core of the data’s context, NLP is the transition to understanding the data.
What is natural language processing?
Natural language processing is an artificial intelligence (AI) technology that automates the data analysis of mined textual, unstructured data to include natural language understanding and natural language generation to simulate a human's ability to create language. It combines computational linguistics with machine learning and deep learning models, performing a special linguistic analysis by algorithms so a machine can "read" text.
Where is natural language processing used?
Today, various industries use NLP, from email filters to virtual assistants and search engines to chatbots. Here's a list of common ways natural language processing is used:
Chatbots: Chatbots are computer programs that use NLP. They simulate human conversation by identifying a sentence's intent, determining suitable topics, keywords, and emotions, and calculating the best response based on the data's interpretation.
Email filters: Email filters apply machine learning using many data samples to sort emails into the right inbox.
Machine translation: Translation software like Google Translate or Microsoft Translator use NLP to translate text from one language to another, such as English to French.
Natural language generation (NLG): NLG, a subfield of NLP, builds applications or computer systems that can automatically produce natural language texts of various types by using a semantic representation as input. Applications of NLG include question answering and text summarization.
Predicting and autocorrecting text: Predictive text and autocorrect use NLP to recognize and recall commonly used words and names to make text suggestions and correct common errors.
Search engines: Search engines like Google search use NLP machine learning to interpret a searcher's intent and provide relevant results. It can even suggest subjects and topics related to the query the searcher might be interested in.
Virtual and voice assistants: Virtual assistants like Apple's Siri or Amazon's Alexa use NLP technology to understand and respond to voice requests. Speech-to-text can dictate messages and notes, and speech recognition can control everything from smartphone apps and smart speakers to thermostats and home security systems.
Web sentiment analysis: Sentiment analysis automates classifying opinions in a text as positive, negative, or neutral. It's a method companies like SESAMm use to monitor sentiments like a brand's sentiment on the web and social media.
Why natural language processing is important to uncover financial-related alternative data
NLP is important because it helps resolve human language ambiguity in big datasets (big data). Languages are complex, diverse, and expressed in unlimited ways, from speaking hundreds of languages and dialects to having a unique set of grammar and syntax rules, slang, and terms for each. In text form, these variables are unstructured text. But with NLP, we can transform unstructured data into structured data and make sense of it.
Because of NLP's power, investors can research and analyze unstructured data from the web to gain insights into financial and ESG data. You can use this wealth of information to focus on systematic data processing, risk management, and alpha discovery through contexts, such as:
Major global indices sentiment
Euronext exchange sentiment
Private company sentiment
ESG risks for public and private companies worldwide
A quick overview of how natural language processing works at SESAMm
At SESAMm, we use named entity recognition (NER), which extracts the names of people, places, and other entities from text, and then named entity disambiguation (NED) to identify named entities based on their context and usage. For example, text referencing "Elon" could refer indirectly to Tesla through its CEO or a university in North Carolina. NED considers the context when classifying entities for an accurate match. Compared to simple pattern matching, which limits the number of possible matches, requires frequent manual adjustments, and can't distinguish homophones, NED is superior.
Process representation for NER and NED.
When identifying entities and creating actionable insights, SESAMm uses three other NLP tools: lemmatization and stemming, embeddings, and similarity. The lemmatization process normalizes a word into its base form (morphology) to help identify and aggregate entities. Embedding assigns the entity a numerical value to help analyze how words change meaning depending on context and understand the subtle differences between words that refer to the same concept—similarity measures whether two words, sentences, or objects are close to one another in meaning.
Representation of nodes in a knowledge graph.
Of course, NLP couldn't function without the core of the text analytics process: knowledge graphs. A knowledge graph is a digital representation of a network of real-world entities, the foundation of a search engine or question-answering service. This structured data model puts the schema in context through semantic metadata and linking, providing a framework for analytics, data integration, sharing, and unification. In other words, it's like a map and legend, with the legend labeling the concepts, entities, and events and the map connecting and identifying their relationships. These details are stored in a graph database and visualized as a graph representation, hence the term knowledge graph.
SESAMm's natural language processing platform for investment research and analysis
SESAMm is the leading provider of natural language processing and machine learning solutions and analytics for investment firms and corporations.
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