Established data providers such as LSEG, Bloomberg, PitchBook, S&P Capital IQ, FactSet, and Moody’s are supplying data to AI labs like OpenAI and Anthropic, enabling banks and asset managers to build AI-powered tools.

The line between data layers and user interfaces is blurring as LSEG integrates data into Model ML and collaborates with Daloopa, Model ML, and Fitch Solutions.

AI in finance is coalescing around three primary business models: data provision, analytics and automation, and the creation of proprietary content or interface products.

Banks are adopting AI agents and proprietary data to streamline analyst workflows, with Rogo reporting ARR above $50 million and Hebbia nearing that level, including Morgan Stanley headcount contracts.

AI agents from labs like Anthropic and OpenAI are being deployed for finance use cases, while startups emphasize in-house versus lab-provided capabilities; OpenAI and Anthropic have released models targeted at financial services.

Bloomberg and AlphaSense are building AI interfaces in-house, with Bloomberg launching ASKB on the Bloomberg Terminal and AlphaSense offering an AI-powered market intelligence platform, both leveraging large language models.

The standout deal is LSEG’s $3.5 billion acquisition of Octus for credit data, highlighting the premium on data assets as a foundation for AI in finance.

Daloopa and PitchBook supply data to labs and are expanding access; PitchBook’s private market data is integrated into ChatGPT, Claude, Perplexity, and Copilot Studio, while Daloopa covers data on over 6,000 companies.

Rogo and Hebbia turn data into finished research products—models, memos, and pitch decks—running on Anthropic, OpenAI, and Google models, with growing client rosters and significant ARR, including major banks as customers.