Artificial intelligence is gradually changing the way investment banks operate by taking over some of the traditional tasks performed by financial analysts. The preparation of presentations, processing of financial statements, research on companies and analysis of market data are increasingly being supported by AI tools. The trend is examined in an analysis by Rebellion Research dedicated to the application of artificial intelligence in investment banking and its impact on the profession.
The technological transformation is no longer limited to experimental projects. Major international financial institutions, including JPMorgan Chase, Goldman Sachs and Morgan Stanley, are expanding their use of AI in their day-to-day operations in order to increase productivity and reduce the time required to complete routine tasks.
In May 2026, JPMorgan announced that it was beginning to roll out artificial intelligence tools across its investment banking operations worldwide. According to the bank's management, the technology enables faster collection and summarization of information, preparation of materials for clients and more efficient service for a larger number of companies.
Which tasks are already being automated?
One of the main areas of application is financial analysis. AI systems can extract information from corporate reports, compare financial indicators, summarize quarterly results and assist in developing scenarios for valuing companies.
In investment banking, a significant portion of junior analysts' work has traditionally involved preparing financial models, presentations for potential deals and informational materials for corporate clients.
Artificial intelligence can already accelerate many of these activities, particularly when it comes to processing large volumes of structured and unstructured data.
This does not mean that the technology is capable of independently carrying out every stage of an investment deal. Assessing the value of a business, conducting negotiations, managing client relationships and making strategic decisions still require human experience and accountability.
Banks Are Changing Their Requirements for Employees
The rise of AI is calling into question the traditional training model at investment banks, under which young professionals learn the trade through extended work with spreadsheets, presentations and corporate documents.
Automation may free up time for more complex tasks, but at the same time there is a risk that junior analysts will gain less practical experience in building financial models and checking data.
According to representatives of Goldman Sachs, young bankers will need to combine artificial intelligence skills with a solid understanding of financial principles, business models and the market environment.
The ability to critically assess information, strategic thinking, communication with clients and the recognition of errors in automatically generated analyses will become increasingly important.
The change is already affecting recruitment plans as well. JPMorgan's management said in 2026 that it expected to hire more artificial intelligence specialists and fewer employees for some traditional banking functions.
Higher Productivity, but Also New Risks
The expanded use of AI in the financial sector creates opportunities for lower operating costs and faster service for corporate clients.
According to a KPMG survey cited by Reuters in July 2026, 51% of the banks surveyed are already experimenting with AI agents – systems that can perform sequential tasks with limited human intervention.
Their applications include asset management, client verification, financial operations and document processing.
Alongside the benefits, serious challenges are also emerging. These include protecting confidential corporate information, the quality of the data used, the possibility of inaccurate results and the need for clear oversight of automated decisions.
In investment banking, even a minor error in a financial model or company valuation can have significant consequences. That is why AI tools are primarily viewed as a means of assisting experts, rather than as a complete replacement for their professional judgment.