OpenAI launches ChatGPT for Financial Services with Morgan Stanley, Evercore

A year before this launch, OpenAI ran a secretive internal effort called Project Mercury, paying over 100 former investment bankers from firms like JPMorgan, Goldman Sachs, and Morgan Stanley roughly $150 an hour to build industry-standard Excel financial models ^[1][2][3]. It was, in effect, manufacturing the training data an AI would need to think like a junior banker before any product existed to sell. That investment shows up in the benchmark numbers OpenAI is now citing: on OfficeQA Pro, a test built from 133 questions drawn from roughly 89,000 pages of Treasury Bulletin filings, the new GPT-6 Astra model scored 69.9% correctness, ahead of OpenAI's own GPT-5.6 Sol at 60.2% and Anthropic's Claude at 62.4% ^[4]. OpenAI also reported sharp drops in data-connector error rates across several third-party providers - Daloopa's error rate fell from 7.53% to 2.57%, and S&P Global's from 6.84% to 2.66% ^[4]. The Project Mercury story matters because it reframes this launch: it is not a clever prompt wrapped around a general model, but the payoff of a deliberate, expensive data-acquisition campaign built specifically to reproduce the judgment of the people it may ultimately displace.

The product's out-of-the-box skills - valuation analysis, LBO modeling, buyer screening, earnings analysis, pitchbook preparation - map almost exactly onto the work traditionally assigned to first- and second-year investment banking analysts ^[5]. In a live demonstration, the platform analyzed a potential M&A target, pulled figures from standard industry data sources, and produced a fully formatted PowerPoint deck matching a bank's house style ^[5]. OpenAI's Nick Turley pushed back on the idea that this simply eliminates jobs, arguing instead that bankers already work 100-hour weeks and that the tool will do for financial analysis what Excel did for the industry decades ago - letting people produce better work faster rather than replacing them outright ^[6]. Forbes framed the launch more bluntly as OpenAI targeting junior-banker work directly ^[7]. Both framings can be true at once: a tool built to compress a 100-hour week is, by definition, a tool that needs fewer hours - and fewer hours, multiplied across an entire analyst class, is exactly the apprenticeship pipeline Wall Street has relied on to train its next generation of dealmakers.

This launch functions as OpenAI's direct answer to Anthropic's Claude for Financial Services, which shipped in 2025 and effectively opened the AI-in-finance category first ^[4][8]. The stakes go beyond bragging rights: enterprise and business customers now account for roughly half of OpenAI's total revenue, according to CFO Sarah Friar, and that reliance is growing as both OpenAI and Anthropic separately prepare for potential public listings ^[9]. Landing named partners like Morgan Stanley and Evercore, plus reported usage from BNY, Fidelity International, MUFG, and Commonwealth Bank ^[5], gives OpenAI logos it can point to as proof of enterprise traction in a vertical it needs to win. The race also explains the product's shape: heavy investment in citation-level traceability and compliance controls is less about user delight and more about clearing the bar that regulated financial institutions - not consumer users - actually set.

To meet Wall Street's compliance bar, OpenAI built in granular line-item citations that let users trace any generated figure back to its source document ^[9][10], alongside enterprise controls like single sign-on, provisioning, and audit-exportable logs ^[10]. Yet the rollout itself undercuts some of the launch-day fanfare: OpenAI has disclosed neither pricing nor minimum seat requirements, and access starts with a limited set of organizations rather than a general release ^[4]. That gap between marketing and actual access shows up in how practitioners are reacting. On finance-focused communities, several self-identified FP&A professionals argued the tool's real value is capped by how clean a firm's own financial data already is - messy internal records and undocumented institutional knowledge, not model capability, are the actual bottleneck. Others were skeptical that elite banks would route client financial data through a third-party API at all, given how tightly firms like JPMorgan and Goldman Sachs guard their own technology stacks, though one counterpoint noted that mid-tier firms without the resources to build in-house may have little choice but to adopt tools like this. That tension between headline AI capability and the unglamorous reality of data governance is likely to decide how far this spreads beyond its initial design partners.