Here are some key quotes from my latest podcast with Sylvain Forté, Co-Founder and CEO of SESΛMm, speaking about the use of big data and artificial intelligence for investing:

“Our specialty is natural language processing, which is a subfield of artificial intelligence. What we do is we basically extract billions of articles and messages from the web, and then read them automatically using algorithms in order to derive relevant insights for investors.”

“I ended up in finance kind of at random to some extent. It was one of the fields where we could apply the data in order to generate valuable insights. But my first passion is really artificial intelligence.”

“There were a lot of startup initiatives starting at the same time in France. So [AI and FinTech] was becoming a topic that was relevant.”

“Historically, I would say that the French startup scene was less active in terms of venture capital investment. But I do believe that is less true since 2020. We’ve seen very big funding rounds with it. We’re seeing more and more unicorn companies actually here in France, especially in the field of FinTech.”

“Our first client was actually a prop shop in London that started leveraging these signals for its own use, and grew its strategy from something like 50k to 25 million based on leveraging these signals and raising capital and trading on them.”

“I try to help [clients] understand how they can go from our technology, which is pretty modular, to an actual use case. So I remain pretty close to the product itself, and to the technology in general.”

“We’re in between these two words. We provide datasets for hedge funds to trade systematically on sentiment, data, risk data, environmental, social governance data. And we also provide the platform so that people can build their own investment use cases and their own custom aspects.”

“Hedge funds are very advanced. They’re looking for one specific thing. And you can’t really deviate from what they’re looking for in a data set to test. They want 10 to 20 years of data. They want that in the format of historical data.”

“To be very blunt, our data set is huge. It’s one of the biggest in the world and contains more than 15 billion pieces of contents. So articles from mainstream news websites, financial and non-financial, super local news, like an obscure newspaper in Texas, we’ll have access to that.”

“Our main objective is to have everything already here so that we can create information on millions and millions of companies, public and private ones, with the lowest possible level of effort.”

“We can [also] leverage our technology to understand how companies are aligned with the UN Sustainable Development Goals in the context of climate change actions or global health, for example, or education. So [our technology can help] make sure that a portfolio is in line and has a positive impact on the world.”

“[Since we started,] we went from the use case of a hedge fund consuming data to generate alpha to a dozen different use cases where we need to make sure that our technology is compatible with all of that.”

“One of the key questions that we have – and even for new players in that field – as fundamental firms and private equity firms is how can we assess the value of this? How can we make sure that there is alpha contained in the data.”

“There is notion of alpha decay in the hedge fund industry, where people are looking at very specific datasets, and are looking for these data sets to not be over-exploited over time. This can happen, but it usually happens with data sources that are pretty narrow.”

“We think that one of the big differentiators beyond AI is data. We don’t sell just AI platforms, we sell AI platforms with data in them. And that makes it very, very big difference.”