OpenAI Launches ChatGPT for Financial Services With Morgan Stanley, Evercore as Design Partners

OpenAI said Thursday it has launched ChatGPT for Financial Services, a version of its enterprise chatbot tailored to investment banking and equity research work, built with design partners Morgan Stanley and Evercore and running on the company's newest model, GPT-6 Astra, according to CNBC and Reuters, via WTAQ.

The product is pitched at some of Wall Street's most labor-intensive tasks: researching companies, analyzing financial data, and generating the pitchbooks and presentations that junior investment bankers typically produce, the outlets reported.

What OpenAI Is Bundling

ChatGPT for Financial Services is a tailored build of OpenAI's existing enterprise product, ChatGPT Work, according to Nick Turley, OpenAI's vice president of product, as cited by CNBC. What separates it from that base product, per CNBC, is native data access from LSEG, Daloopa and PitchBook, which supplies financial statements and earnings transcripts, along with automated access to firms' existing data subscriptions.

Reuters, via WTAQ, reported the bundle also includes datasets from Crunchbase and Quartr covering earnings transcripts, financial statements and company fundamentals, and that OpenAI said this data is indexed on its own infrastructure to improve retrieval and citation. Firms that already pay for other data services can connect them through integrations with FactSet, S&P Global, Preqin and Datasite, OpenAI told Reuters. LSEG was described in that report as the sole distributor of Reuters news, financial data and real-time alerts to global financial professionals.

On launch customers, OpenAI has not named any banks that have signed on. Turley told CNBC there was "a ton of demand" for the product but declined to identify specific institutions beyond confirming Morgan Stanley and Evercore as design partners that helped shape the offering.

How It's Meant to Work

Turley described the goal as training the model to operate like a junior analyst. "We're effectively teaching ChatGPT to research like an analyst and back up its conclusions like an analyst as well," he said during the briefing, according to CNBC.

In a live demonstration cited by CNBC, Turley showed the platform analyzing a potential M&A target, pulling financial figures from industry-standard data sources and producing a formatted PowerPoint deck built to a bank's own style guide. Reuters reported that OpenAI says users can conduct research across multiple sources, build financial models, and generate client materials such as pitchbooks using a firm's own templates.

GPT-6 Astra, the model underpinning the product, was built to improve retrieval across financial data tools, financial reasoning, and the accuracy of generated content, OpenAI told Reuters.

Compliance and Audit Controls

For regulated users, Reuters reported that the product builds on ChatGPT Enterprise's existing security controls, including role-based access and encryption, and allows compliance teams to export workspace logs into audit workflows. CNBC added that finance-specific features include citations letting users trace data back to source filings and audit charts, as well as administrative controls for sensitive deal materials.

Reuters framed the launch as part of a broader push by AI companies to build products for regulated industries, where data governance and security are central concerns.

Competitive and Market Context

The launch lands in a crowded enterprise AI market where OpenAI is competing with Anthropic and Google, CNBC reported. Anthropic introduced its own Wall Street-focused tool, Claude for Financial Services, last year. OpenAI's finance chief, Sarah Friar, told investors in August that the company's enterprise business now generates more revenue than its consumer business, which took off after ChatGPT's 2022 launch, according to CNBC. The outlet also noted that the enterprise push comes as OpenAI gears up for what is widely expected to be a blockbuster initial public offering; no timeline or valuation was disclosed.

OpenAI told Reuters it plans to expand the underlying financial dataset and extend the product beyond investment banking and equity research to the broader financial services sector. Turley also told CNBC that OpenAI intends to release similarly tailored tools for "a number of sectors" beyond finance, without specifying which ones or when.

The Junior Banker Question

Asked whether the tool would reduce banks' need to hire junior bankers, Turley compared its potential impact to that of a familiar office tool rather than framing it as a replacement. "If you study the life of an analyst or of a banker, depending on the industry, they're working 100-hour weeks," he told CNBC. "I think in the same way that Microsoft Excel transformed the industry and allowed them to produce better analysis faster, you will see technology like this do the same."

That framing sits alongside a caution raised last month by Chris Churchman, the Goldman Sachs partner overseeing one of the bank's flagship AI projects, who warned that automating the tasks junior bankers traditionally use to learn the trade risks producing "cognitive atrophy" in the next generation of financiers, CNBC reported. The comment was not made in direct response to OpenAI's launch, but CNBC's reporting places it in the same industry debate over how far AI should go in handling entry-level analytical work.

Bottom Line

OpenAI has put a name and a data stack behind its push into Wall Street workflows, with Morgan Stanley and Evercore attached as design partners and a roster of data vendors, including LSEG, Daloopa, PitchBook, Crunchbase and Quartr, feeding the system. What remains unconfirmed publicly is which banks beyond the two named design partners will actually deploy it, how pricing is tiered, and how the product performs against Anthropic's already-established Claude for Financial Services once it reaches broader use. For now, the launch is best read as an opening bid in a fast-moving contest among AI vendors to embed themselves in regulated, data-intensive corners of the finance industry, rather than a settled shift in how banks staff their deal teams.