OpenAI Targets Wall Street Junior Bankers With New ChatGPT for Financial Services

OpenAI for Financial Services is taking aim at some of the most time-consuming tasks performed by junior investment bankers and equity researchers, as the artificial intelligence company expands its enterprise offerings with a finance-specific version of ChatGPT.

OpenAI unveiled ChatGPT for Financial Services on Thursday, September 10, introducing a tailored enterprise product designed to help financial professionals research companies, analyse financial information, work with market data and generate presentation decks.

The platform was developed with “design partners” including Morgan Stanley and Evercore, according to OpenAI Vice President of Product Nick Turley.

The launch represents a significant step deeper into the financial-services industry, where investment banks employ thousands of analysts and associates to conduct company research, prepare financial analysis and produce pitchbooks and other materials for clients.

OpenAI said the product is initially focused on investment banking and equity research, two areas where employees routinely spend long hours collecting information, checking figures and turning analysis into client-ready presentations.

OpenAI brings AI into investment banking workflows

OpenAI for Financial Services is based on the company’s enterprise offering, ChatGPT Work, but has been specifically adapted for financial professionals.

During a demonstration, Turley showed the system analysing a potential mergers and acquisitions target. The platform pulled financial information from industry-standard data sources, selected relevant comparable companies, organised pricing information and generated a formatted PowerPoint presentation using a bank’s existing style guide.

The demonstration highlighted the ability of the system to complete several connected tasks rather than simply answer individual questions.

According to Turley, producing a useful financial presentation requires much more than making visually attractive slides. The system needs to determine which companies are relevant peers, retrieve the appropriate financial and market data, place that information into a spreadsheet, verify charts against the underlying figures and provide an explanation for movements such as a market sell-off and subsequent rebound.

“We’re effectively teaching ChatGPT to research like an analyst and back up its conclusions like an analyst as well,” Turley said during the briefing.

That approach is designed to move AI further into the day-to-day workflow of investment banks.

Access to financial data

One of the major differences between ChatGPT for Financial Services and the underlying enterprise product is its access to specialised financial information.

OpenAI said the new platform provides native access to data from LSEG, Daloopa and PitchBook.

Those sources can provide information including financial statements, earnings transcripts and other company and market data used by financial professionals.

The platform can also connect to users’ existing data subscriptions, potentially reducing the need for bankers to manually move information between multiple financial-data platforms and other software.

The integration is particularly important for investment banking because analysts frequently need to combine information from company filings, market-data services, earnings materials and internal research before preparing an analysis.

OpenAI is also adding finance-specific features intended to make AI-generated work easier to verify.

The system includes citations that allow users to trace information back to source filings. It can also audit charts against underlying data, an important feature for financial institutions where inaccurate numbers can have significant consequences.

Administrative controls are designed to provide additional safeguards around sensitive deal materials and other confidential information.

A potential challenge to junior banking roles

The launch inevitably raises questions about how artificial intelligence could affect entry-level jobs on Wall Street.

Investment banks have traditionally relied heavily on junior bankers to perform research, update financial models, collect comparable-company data and create pitchbooks. These responsibilities can involve long hours, particularly during live transactions and periods of intense deal activity.

The ability of an AI system to complete several of these tasks within minutes could potentially change how banks allocate work among employees.

When asked whether ChatGPT for Financial Services could reduce investment banks’ need to hire junior bankers, Turley described the technology primarily as a productivity tool.

He compared the potential impact of AI with the introduction of Microsoft Excel, which transformed financial analysis by allowing professionals to perform calculations and produce analysis much faster.

“If you study the life of an analyst or of a banker, depending on the industry, they’re working 100-hour weeks,” Turley said.

Turley argued that AI could similarly allow financial professionals to produce better analysis more quickly, potentially increasing the amount of work each employee can accomplish.

However, the implications may extend beyond simply reducing workloads.

Wall Street’s apprenticeship model faces questions

Junior investment banking positions have traditionally served as an apprenticeship system.

New analysts and associates learn how to evaluate companies, structure arguments, interpret financial statements and communicate investment ideas partly by performing repetitive tasks under the supervision of more experienced bankers.

If AI takes over a significant portion of those tasks, banks could face a difficult question: How do future senior bankers acquire the experience they traditionally gain from doing junior-level work?

That concern has already been raised within the industry.

Last month, Chris Churchman, a Goldman Sachs partner responsible for one of the bank’s major AI initiatives, warned that automating tasks used to train junior bankers could contribute to what he described as “cognitive atrophy” among the next generation of financiers.

Churchman stressed that reasoning remains important even as technology becomes capable of performing more complex tasks.

The concern is that delegating too much of the analytical process to AI could reduce opportunities for junior employees to develop the judgment required to become senior dealmakers.

This creates a potential tension for investment banks. AI can make employees substantially more productive, but the same productivity gains could eliminate some of the hands-on work through which inexperienced employees traditionally develop their skills.

OpenAI’s enterprise ambitions

The financial-services launch also underscores OpenAI’s broader effort to expand beyond consumer use of ChatGPT.

OpenAI has spent much of the past year competing aggressively for enterprise customers, facing growing competition from companies such as Anthropic and Google.

Anthropic has already introduced a financial-services-focused version of its own AI offering, Claude for Financial Services, aimed at similar Wall Street workflows.

OpenAI Chief Financial Officer Sarah Friar told investors in August that the company’s enterprise business had surpassed its consumer business in revenue.

That marks an important shift for a company whose global growth was initially driven by consumers following ChatGPT’s public launch in 2022.

OpenAI is now attempting to turn AI into an essential tool across major industries, with financial services representing one of the most valuable and demanding enterprise markets.

More industry-specific AI products expected

Turley said OpenAI expects to introduce tailored solutions for a number of industries beyond financial services.

The approach reflects a broader movement in enterprise AI toward specialised systems rather than general-purpose chatbots.

For financial institutions, a dedicated AI product can be designed around specific data sources, compliance requirements, security controls and workflows.

That could make AI more useful for professional tasks where accuracy, traceability and confidentiality are critical.

Investment banking is particularly suited to this approach because much of the work involves structured information and repeatable processes, including company research, comparable-company analysis, earnings analysis and presentation creation.

At the same time, the sector requires human judgment, particularly when evaluating transactions, interpreting market conditions and advising clients.

The future of banking work

The introduction of OpenAI for Financial Services is unlikely to immediately eliminate the need for junior bankers. Instead, it could begin changing what junior bankers spend their time doing.

If AI handles more data gathering, document review and presentation preparation, analysts could potentially devote more time to higher-value activities such as interpreting results, developing investment arguments and working directly with senior bankers and clients.

But that transition will depend on how financial institutions deploy the technology.

Banks may choose to use AI primarily to reduce workloads and improve employee productivity. Others could use it to increase output without proportionally increasing headcount.

The distinction could have major consequences for hiring.

If banks can generate more research and presentations with smaller teams, demand for entry-level employees could eventually decline. Conversely, if AI becomes a productivity multiplier that allows existing teams to handle substantially more transactions, banks could maintain or even increase hiring while changing the responsibilities of junior employees.

The longer-term impact may therefore depend less on whether AI can perform junior-level tasks and more on how financial institutions restructure their organisations around the technology.

For OpenAI, the launch represents another major push into professional services. For Wall Street, it presents both an opportunity to automate some of the industry’s most exhausting workflows and a challenge to an apprenticeship system that has shaped investment banking for decades.

As AI becomes capable of conducting research, analysing financial data, checking its work and producing client-ready presentations, the definition of the junior banker’s job may be entering a fundamental period of change.