Discussions around nuclear weapons and defense have recently highlighted how differently investors interpret weapons exclusions. In particular, Russia’s invasion of Ukraine has brought security considerations back into focus across Europe and prompted some investors to revisit long-standing exclusion policies.

At the same time, regulatory frameworks such as the Sustainable Finance Disclosure Regulation (SFDR) encourage investors to screen portfolios for controversial activities and disclose how those risks are managed. However, these frameworks do not impose a single global definition of weapons exposure. As a result, policies can vary widely between institutions.

Controversial Weapons

What Do We Mean by Controversial Weapons?

In responsible investment policies, the term “controversial weapons” has a relatively clear meaning. It refers to weapons that are prohibited or heavily restricted under international conventions because of their indiscriminate or humanitarian impacts.
Typical examples include:

• cluster munitions
• anti-personnel landmines
• chemical weapons
• biological weapons
• nuclear warheads

Because these weapons are banned or widely condemned under international treaties, investors usually apply strict zero-tolerance exclusions. Any company involved in producing these weapons, or supplying critical components for them, is typically excluded from ESG-focused portfolios.

What Counts as Weapons Exposure?

The broader category of weapons exposure is more complex and is where investor interpretations often begin to diverge. Recent discussions across Europe’s sustainable finance community have focused on whether defense companies should remain excluded from ESG portfolios, particularly in light of renewed security concerns following Russia’s invasion of Ukraine.

Many exclusion frameworks distinguish between controversial weapons and other forms of military-related activity. Companies may be involved in conventional weapons manufacturing, such as firearms, missiles, bombs, or military electronics. Others produce defense systems and equipment, including radar, communications technology, or aircraft components. Civilian firearms are also frequently treated as a separate category within exclusion policies.

In these cases, investors often rely on revenue thresholds rather than absolute bans. A company may be excluded if more than five to ten percent of its revenue comes from weapons manufacturing, while smaller or indirect exposure may still be permitted depending on the investor’s mandate.

A key challenge in these screenings is that a company’s weapons exposure is not always obvious from its core business description. A firm may supply components, software, or materials used in weapons systems or operate as part of a broader defense supply chain. This is particularly difficult to identify in private markets, where companies are not required to disclose detailed segment revenues or defense-related contracts.

As a result, defining and detecting weapons exposure requires clear policy definitions and structured screening logic. What counts as weapons involvement, and where the exclusion threshold lies, ultimately depends on each investor’s mandate and risk tolerance.

What's Different Now?

These questions are no longer abstract. Since Russia's invasion of Ukraine, major asset managers have visibly shifted their positions. Allianz Global Investors, for instance, updated its Article 8 fund policies in 2025 to allow defense companies. Global Trading UBS and Franklin Templeton made similar moves, each removing revenue-based weapons thresholds that had been standard practice for years. At the regulatory level, Hortense Bioy, Head of Sustainable Investing Research at Morningstar Sustainalytics, noted that "since the start of the war in Ukraine in 2022, it has become increasingly clear that geopolitics plays a more significant role in shaping the boundaries of sustainable investing than ethics." What these shifts share is a common thread: the thresholds and definitions that once felt settled are now being redrawn, which is precisely why screening frameworks need to be flexible enough to reflect each investor's current policy, whatever that may be.

Customizable Screening

Because exclusion policies are defined at different levels, it’s rarely as simple as establishing a generic exclusion list. Limited partners often impose their own restrictions or revenue thresholds, which general partners must apply alongside the fund’s internal ESG policy and regulatory restraints. In some cases, LP requirements may further restrict or override the fund’s baseline approach.

As a result, acceptable levels of exposure to activities such as conventional weapons can vary significantly across portfolios.

Screening frameworks, therefore, need to adapt to the investor’s policy rather than forcing the policy to adapt to the tool.

In this case, SESAMm’s AI-generated exclusion screening report can be customized to match each investor’s requirements. Threshold-based classifications help identify different levels of involvement, allowing investors to distinguish between companies with no exposure, limited exposure, or significant involvement in controversial activities. Each classification is supported by underlying evidence and source documentation, allowing analysts to verify the reasoning behind the flag.

This approach makes it possible to apply a consistent methodology across both public and private companies while remaining aligned with the investor’s specific exclusion framework.

Discussions around defense and responsible investment will continue to evolve as geopolitical and regulatory contexts shift. Recent debates around nuclear deterrence and defense participation illustrate how differently investors can interpret weapons exclusions, even when they operate under the same regulatory frameworks.

For investors, the challenge is therefore not only defining exclusion policies but ensuring that those policies can be applied consistently and transparently across portfolios. As definitions of weapons exposure vary, and as supply chains and private market structures add further complexity, screening frameworks must be capable of translating policy into clear, operational rules.

Sylvain Forté, CEO and co-founder of SESAMm, presented the following at Finovate 2022. In the presentation, Sylvain explains who SESAMm is, what SESAMm does, including examples, and how it benefits our financial clients.

Below is an approximation of this video’s audio content. Watch the video for a better view of graphs, charts, graphics, images, and quotes to which the presenter might be referring to in context.

Hi, everyone. Thank you very much for the opportunity to be with you today. I’m very glad to introduce you to SESAMm. I’m Sylvain, CEO and co-founder of SESAMm.

We’re an artificial intelligence company specializing in analytics for investment professionals and [corporations]. We basically extract billions of articles and messages from the web and transform them into actionable insights to make better decisions. We’re a team of close to 100 people now, and we generate insights from more than 20 billion articles and messages.

Immediate access to daily insights

Let me jump straight to the demo and give you a practical example of what we do. So imagine you’re, for example, a bank looking to compute environmental, social, and governance risks on your portfolio on your clients or on your suppliers. Right now, you may have access to ratings, which are updated once per quarter or once per year. We can give you access immediately to timely daily data on all of your companies in order for you to better assess risks and raise early warnings.

Wirecard use case

In this specific example (Figure 1), we look at Wirecard, a company that went bankrupt due to a 2 billion fraud scandal in Germany.

We extracted dozens of thousands of articles and messages on the company, and we can immediately see that there is a huge anomaly in terms of governance risk. The company is basically exposed to fraud accusations, to lawsuits, and the like, things that you don’t really want to see in your clients or your own portfolio.

Furthermore, we can see on this chart that we can get that type of indicator every single day. And we can see that six months prior to the company’s bankruptcy, there were already huge alerts actually here in January 2020, indicating that the company was in a pretty bad situation from the perspective of web content and web data from news to social platforms, blogs, and forums.

We really have the ability to compute live insights for ESG risk, sustainability monitoring, credit, and similar topics. The advantage of the platform is that we can go very deep. You can see here (Figure 2) some of the underlying governance topics associated with Wirecard, such as fraud, embezzlement, and crime—the main accusation—but also things related to anti-competitive practices or corruption.

And furthermore, the platform enables full transparency. This is AI at scale, but the underlying content is actually text articles and messages that you can read in order to understand the situation and see why the company is in that risk position. So with our platform, with our text analysis engine (TextReveal®), you can immediately extract content on your portfolio, your clients, your suppliers, and for example, generate ESG insights, competitive insights, sentiment insights, or credit warnings, for example.

Trusted, reliable, and abundant insights

We are today trusted by major financial institutions, such as Nomura [Holdings] or Raiffeisen Bank in the banking sector, for example, or large private equity firms worldwide. The reason why they trust us is that we can provide data more quickly—so waiting one day instead of waiting three months—to get an indicator. In addition to that, we have better coverage. We’re the only company in the world that can provide information on five million different public and private companies, meaning all of your banking clients, for example, are covered. And finally, we have access to a large variety of sources, from social content to news and blogs.

Insights beyond companies

Another example that is very common—sadly right now—is clients asking us to follow the Ukraine Russia War and to understand the current situation, including by getting access to local content in local languages in Ukrainian, in Polish, in Russian, to really understand the news and social media out there.

You can see here that beyond companies, we actually track sectors, infrastructure projects, and concepts.

Here (Figure 3), Nord Stream, for example, in the context of Ukraine specifically—so as to understand how these two topics are associated on the web—we can see an explosion in terms of volumes of data over time, the news associating this concept more and more, with more than 40,000 pieces of content. And we can see that sentiment over time, as displayed on this curve (Figure 4), decreases very rapidly, so we see the shock on e-reputation, and we can observe that immediately. And, for example, as a bank or as an asset manager, we can use that to assess the potential risk to clients or portfolio companies.

The interesting thing here is that, beyond the graphs and the raw contents, we can look at where the information comes from. Here (Figure 5), you see a lot of information in German, for example, which is not surprising. And you can even follow the Russian propaganda directly from the platform, looking at Russia Today or Sputnik straight from the engine, as these are also sources that we monitor.

And as you can see, these contents are highly customizable and can be used in very specific situations. So this is really a platform as a service (PaaS) that we offer. This is an engine that tracks four million different sources of information, and we can track millions of companies but also even fuzzy concepts, countries, or topics of interest.

Generate analytics from big data with API

One last thought. A lot of our clients integrate with our API; it’s a technical solution. We work a lot with data science teams, data engineering teams, risk teams, quantitative analysts, and heads of innovation. All of these teams are looking to generate analytics from big data and from web content at scale, with solutions that are currently used by dozens of clients worldwide and for which we provide very relevant analytics.

I’ll leave you with three final calls to action.

1. The first one is come see us at our booth. We would be very happy to present the solution in a bit more detail.
2. The second is, please request a demo. You understand that these indicators can be tailored to your needs in real time. So we’ll be very happy to show you a demo at SESAMm.com.
3. And finally, come see us for a free proof-of-concept (POC). We would be very happy to show you how we incorporate these solutions in actual banking tools and in risk management tools.

So the web is now readily available as a system that you can use and that you can rely on in order to generate valuable insights. We’re very happy to provide the solution to the market and to help inform better decisions and to help monitor risks.

Thank you very much.

In this final part of our series on AI in finance, we look at how new technological advancements will change the finance world. Over the next ten years, using data and AI for financial decisions will become common practice.

What is Generative AI and Why It Matters in Finance

New AI technologies, such as GPT-3.5 and GPT-4, are becoming part of everyday business tools. Although we're just scratching the surface of what they can do, these technologies will soon help us with tasks like writing emails, creating presentations, and making financial reports.

Take ESG (Environmental, Social, and Governance) indicators, for example. Right now, analysts often manually collect this data from financial reports. But soon, advanced AI models will handle this work, leading to more interactive and intelligent business tools.

What's Next for AI Technology

The following versions of these AI models will be even better than the ones we have today. Given that current models perform some tasks better than humans, it's exciting to think about their future capabilities. We expect these new models to excel in many different tasks.
In the future, we'll see machines handle most tasks, which could be good for the world if we use this technology wisely in our everyday work.

How Generative AI Will Change Finance

Just like the internet and smartphones did, generative AI will change how businesses operate. Companies that adapt will do well, while others might struggle. One significant change will be in jobs, especially for analysts. As data becomes easier to collect and understand, analysts will shift to roles where they guide and interact with AI-based business systems.

How SESAMm Uses Generative AI

At SESAMm, AI is already making our work more efficient. It's changing both our internal processes and the features we offer our clients. For example, we use advanced AI models to automate data annotation for ESG and SDG (Sustainable Development Goals) alerts. This has saved our analysts 30% of their time.
We're also creating a client-friendly interactive tool that will be a part of our dashboard. Our aim is to start with a demo and then fully automate the extraction and summary of key ESG and SDG events.

SESAMm’s Future with AI

In the long term, AI will play a big role in improving our services. We plan to use AI to automatically create reports, including detailed ESG or competitive analyses for private equity firms.
AI is central to our innovation plans. We see it as a way to speed up our growth and establish SESAMm as a key player in the industry.

Our Long-term Objectives with AI

Our main goal is to make it easy for users to find accurate and timely data and ESG insights. The power of AI comes from its ability to quickly sort through a lot of information and pull out what’s important.

Another key aim is to help direct investments toward truly beneficial companies by improving our ESG measurement capabilities.

Staying Competitive in an AI World

To stay ahead, we are committed to raising internal awareness about AI and encouraging its active use across all teams. We also understand that a culture of innovation and transparency is crucial for success, particularly in ESG matters.

Final Thoughts

AI will change the way we work, but it's not just a tool—it's a vital part of our business strategy. It will help us improve our processes, services, and client relationships. Ultimately, AI is about much more than efficiency. It’s about unlocking new opportunities, empowering our team, and driving sector-wide innovation.

In case you missed it, please check out the previous parts of the series:

• Part 1.
• Part 2.

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 request a demo, contact one of our representatives.