Wall Street’s next generation of artificial intelligence companies is taking shape around three businesses, including those that own financial data, those that turn it into research and analysis, and those that control the proprietary content investors rely on to make decisions.
The emerging market includes established providers such as London Stock Exchange Group (LSEG), Bloomberg and PitchBook alongside startups including AlphaSense, Rogo, Hebbia, Daloopa, Model ML, Samaya AI and Farsight.
Each is competing for a share of the technology budgets of investment banks, asset managers and other financial institutions. However, their business models differ, raising a question about where the economic value of AI in financial services will ultimately accrue.
The competition is intensifying as AI systems move beyond answering questions toward preparing financial models, analyzing transactions and producing investment research. Financial institutions increasingly need access to reliable data, software capable of completing complex tasks and proprietary information that cannot be readily reproduced by general-purpose AI models.
LSEG, Daloopa and PitchBook Supply Data to the AI Labs
Data sits at the base of the stack. LSEG, Daloopa, PitchBook, S&P Capital IQ, FactSet and Moody’s all feed financial data into the banking tools OpenAI and Anthropic have built, PYMNTS reported Sept. 11.
LSEG gave banks a way to build AI agents on its data through Microsoft in October 2025, and ChatGPT added it in December, PYMNTS reported.
Daloopa and PitchBook supply both labs, and both are widening that reach. Daloopa raised $47 million in Series C funding in May. The company said in a September blog post announcing a partnership with Model ML that its data covers more than 6,000 companies.
PitchBook, which Morningstar owns, put its private market data into ChatGPT in December, according to a Dec. 17 press release. It had already connected to Claude, Perplexity and Microsoft’s Copilot Studio. Rogo, Hebbia, Model ML and Farsight are also PitchBook integrations. The startups that compete with the labs buy from the same suppliers.
Payments companies are making a similar bet on proprietary data. Mastercard and Plaid are training foundation models on their own transaction data, an advantage general-purpose model builders lack, PYMNTS reported April 6.
Rogo and Hebbia Sell the Analyst’s Work
Rogo and Hebbia turn that data into finished work, such as financial models, memos and pitch decks. Both run on models from Anthropic, OpenAI and Google, according to the Sept. 11 PYMNTS report.
Rogo Co-Founder and CEO Gabe Stengel said in April that a lot of Wall Street’s analytical work is “done by a 21-year-old in tools from 40 years ago at 2 a.m.,” PYMNTS reported April 29, citing Bloomberg. Rogo’s software is meant to take that work off the analyst.
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Banks are buying. Rogo’s annual recurring revenue passed $50 million, up from $15 million at the end of 2025, PYMNTS said in the Sept. 11 report, citing The Information. Its clients include Lazard, J.P.Morgan, Bank of America and Wells Fargo, the April 29 PYMNTS report said.
Hebbia is approaching $50 million in annual recurring revenue and holds a contract covering about 5,000 Morgan Stanley investment banking employees, The Information reported Sept. 3.
The labs are selling the same work. Anthropic debuted 10 finance agents in May, PYMNTS reported May 5.
OpenAI followed in September with ChatGPT for Financial Services, built with Morgan Stanley and Evercore as design partners, according to the Sept. 11 PYMNTS report. The startups argue banks need them. Farsight CEO Samir Dutta, whose company also competes with Rogo and Hebbia, said most banks still lack the scoped use cases to build effective in-house tools.
Bloomberg and AlphaSense Build Their Own AI Interfaces
Content owners such as Bloomberg and AlphaSense are taking a third route and building the AI interface themselves. Bloomberg announced in an Oct. 15 press release that ASKB, its conversational AI tool, is now generally available to financial professionals. ASKB sits on top of the Bloomberg Terminal, the subscription platform where traders and analysts pull market data, news and analytics.
ASKB runs partly on Anthropic models, Fortune reported in April.
AlphaSense takes a similar approach with an AI-powered market intelligence platform built on company filings, earnings call transcripts and broker research. It raised $350 million in June at a $7.5 billion valuation.
The lines between the layers are already blurring. LSEG integrated its data into Model ML in July. Model ML partnered with Daloopa in September to pull its data. Hebbia partnered with Fitch Solutions in February to put Fitch’s credit research on its platform.
The biggest check so far, however, has gone to the data layer. LSEG agreed Thursday (Oct. 22) to buy credit data provider Octus for $3.5 billion, PYMNTS reported. The company said 80% of its AI agreements include purchases of additional datasets.
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