Historically, investment research involved human analysts spending days reading and evaluating massive financial reports. Now, AI-driven financial analysis is covering all of that work in just a few minutes.

The catalyst was a 2020 academic paper called Lazy Prices, published by Harvard professor Lauren Cohen, which found that changes in the risk sections of SEC 10-K and 10-Q filings reliably predicted negative stock performance. According to Liam Hynes (pictured, right), global head of new product development for public markets at S&P Global Market Intelligence LLC., this was true even without reading the text. The result was a research framework capable of identifying precisely what changed, whether a new risk was material, and which companies in a short portfolio warranted the position — with large language models doing the reading.

“We went and rebuilt that research with the help of Snowflake,” Hynes said. “With all the Snowflake Cortex and Snowflake CoWork tools, we were able to augment the alpha in that short book by removing the companies that didn’t have any risk and just concentrating on the companies that did have the risk.”

Hynes and John Heisler (left), head of AI and financial services at Snowflake Inc., spoke with theCUBE’s Dave Vellante and Rebecca Knight  at the Snowflake Summit 2026 during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how agentic workflows are compressing the rift between financial research and production deployment, and what it will take for financial services firms to build AI systems that are deterministic and enterprise-wide in scope. (* Disclosure below.)

AI-driven financial analysis demands determinism, semantics and a business-first strategy

The framework derived from Lazy Prices illustrates a broader shift in financial services, where AI-driven financial analysis is democratizing tools that once required quant hedge fund infrastructure. What once required analysts like Michael Burry to lock themselves in a room for weeks reading millions of words of SEC filings can now be done in minutes. The key insight, Hynes explained, is that the market itself is slow to price in changes buried deep in regulatory documents.

“If I go in and I add two or three sentences to a document that has 10,000 words in it, the prices are very lazy to catch up with that new information,” Hynes said. “There isn’t a press release. There isn’t a big song and dance about this has changed. It’s kind of hidden in the document.”

For Heisler, AI-driven financial analysis only works at enterprise scale when every agent, analyst and workflow draws from the same governed source. The question of enterprise-wide consistency comes down to one foundational principle, he explained.

“Governing data is governing AI,” Heisler added. “Whether I’m using an agent or querying data directly, I’m John. I have permissions to certain data sets, and I don’t have permissions to others. That’s a big mechanical piece of this.”

On the question of what separates organizations that succeed with AI-driven financial analysis from those that fall behind, both guests pointed to successful institutions starting with strategy, not technology. Heisler framed it as a “nesting doll” approach. In short, business strategy drives data strategy, data strategy drives AI strategy and AI strategy drives semantic architecture. He noted that skipping any layer is like painting on a compromised canvas.

“The companies that sit down with their clients and literally say, ‘What is your process? What is your brain telling your fingers to type?’ Those are the ones that are going to succeed,” Hynes said. “The ones that put their clients and their processes first.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Snowflake Summit 2026:

(* Disclosure: TheCUBE is a paid media partner for Snowflake Summit 2026. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE