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3 Ways Generative AI is Transforming Investment Banking

Investment banking has always relied on data, analysis, and deep industry expertise. But with M&A activity increasing — North America deal value topped $2 trillion in 2024, marking a 16.4% rise in deal value and a 9.8% jump in deal count — firms will need to execute transactions with greater speed and precision. Generative AI is emerging as a critical tool, streamlining deal-making, automating risk assessments, and enhancing market intelligence.

Here are three key ways genAI is transforming investment banking:

Research and Market Intelligence

Investment banking relies on vast amounts of real-time financial data. By integrating genAI into financial research, firms can make more informed decisions, respond to market developments faster, and gain a competitive edge in deal-making and advisory work. To support this, investment banks are deploying a mix of internally developed genAI tools, enterprise-grade platforms, and, in some cases, consumer-grade applications — each suited for different use cases and expectations around security, transparency, and verifiability.

In October 2024, Morgan Stanley announced the launch of AskResearchGPT, an internal genAI-powered assistant designed to help Morgan Stanley’s Investment Banking, Sales & Trading, and Research staff by instantly searching and summarizing insights from more than 70,000 proprietary reports.

On the enterprise side, AlphaSense’s Generative Search and Generative Grid empower banking professionals to quickly find reliable information from premium content sources, such as expert transcripts, broker research, company documents, and news, streamlining the research process. Results are returned in text or tabular formats, making it easy for users to quickly scan and compare information across many documents.

Additionally, companies such as Google, OpenAI, and Perplexity are launching advanced genAI deep research tools that allow banking professionals and others to explore complex topics more thoroughly than with a standard genAI tool. OpenAI's deep research tool impressed Deutsche Bank analysts by answering complex academic and industry questions. Deutsche Bank tested the system by asking it to analyze the impact of new U.S. steel and aluminum tariffs. In eight minutes, it produced a 9,000-word report citing 22 sources. AlphaSense is also developing its own deep research functionality that can generate comprehensive responses from its premium business content, which will help bankers synthesize complex information faster.

Deal-Making

Investment banking deals — whether mergers and acquisitions, IPOs, or capital raising — often require intensive research, careful analysis, and meticulous documentation. Bankers traditionally spend significant amounts of time manually conducting due diligence and drafting deal-related documents, but now genAI is streamlining these tasks.

A recent Bain & Company report highlights the growing role of genAI in M&A, with adoption rising from 16% in 2023 to 21% in 2024 — and it’s expected to surpass 50% by 2027. Nearly 80% of companies using genAI in their M&A processes report reduced manual efforts, while more than half say genAI accelerated deal timelines.

Bain estimates that genAI-driven due diligence can condense a week’s worth of analysis into a single day. Within the next 12 months, Bain expects early adopters will use genAI to draft integration workplans and transition service agreements (TSAs) — in less than 20% of the time previously required.

Deloitte estimates that IBD productivity can be improved by 34% with genAI, as the technology assists with generating initial deal structures and conducting due diligence, compliance, and valuation. Additionally, in the areas of underwriting and issuance, genAI can help draft prospectuses, term-sheets, and legal documentation.

A leading investment bank built a genAI tool to help analysts draft pitch books, reducing the time spent on these materials by 30%. Meanwhile, Goldman Sachs is developing an AI tool that can transfigure a lengthy PowerPoint document into a formal S-1.

Additionally, advanced deep research tools like AlphaSense’s will be particularly valuable in deal-making, swiftly processing and synthesizing company financials, industry trends, and competitor insights into coherent narratives, such as industry primers, investment memos, and competitive landscapes.

Risk and Compliance

Investment banking involves complex risk modeling and regulatory compliance to ensure deals are executed within legal and financial constraints. GenAI enables firms to simulate countless market conditions and hypothetical scenarios to stress-test deal structures. Among bank leaders that have integrated genAI, 88% are seeing gains in risk management and compliance.

Yet, despite the technology's potential, only 17% of banks currently use genAI for risk management, with U.S. banks leading in adoption compared to their EMEA and APAC counterparts. One example: Goldman Sachs uses a genAI risk management system specifically designed to spot and mitigate emerging risks.

On the compliance side, JP Morgan’s contract intelligence platform COiN, which was trained on hundreds of thousands of loan contracts, interprets commercial loan agreements, accomplishing what previously took lawyers 360,000 hours annually in seconds. Citibank uses genAI to automatically process and summarize a wide range of documents related to risk and compliance, improving both efficiency and accuracy of internal processes.

Looking Ahead: GenAI’s Next Chapter in Investment Banking

What started as an experimental technology has quickly proven its value in real-world use cases, transforming critical functions such as deal execution, risk management, and market intelligence. For investment banking professionals, the implications are clear: proficiency with genAI tools is quickly becoming essential.

As these AI solutions evolve from assisting analysts and bankers to acting as increasingly autonomous AI agents, the roles and skills required in investment banking will shift. Goldman Sachs, for example, envisions that its internal AI assistant will eventually act like a seasoned banker, autonomously performing multi-step processes with minimal human intervention.

GenAI-driven deep research tools will continue to improve, offering more accurate and sophisticated insights into market trends, transaction structures, and client needs. Rather than relying solely on question-and-answer formats, next-generation genAI systems will anticipate user needs, proactively surfacing relevant data and recommendations.

As genAI tools become more advanced, investment banking will see new levels of efficiency and innovation. The next era of AI in investment banking promises not just smarter tools but a fundamental shift in how firms originate deals, evaluate risk, and create value for clients in an increasingly complex financial landscape.

About the Author
  • Sarah Hoffman

    Sarah Hoffman is Director of Research, AI at AlphaSense, where she explores artificial intelligence trends that will matter most to AlphaSense’s customers. Previously, Sarah was Vice President of AI and ML Research for Fidelity Investments, led FactSet’s ML and Language Technology team and worked as an Information Technology Analyst at Lehman Brothers. With a career spanning two decades in AI, ML, natural language processing, and other technologies, Sarah’s expertise has been featured in The Wall Street Journal, CNBC, VentureBeat, and on Bloomberg TV. Sarah holds a master's degree from Columbia University in computer science with a focus on natural language processing, and a B.B.A. from Baruch College in computer information systems. Sarah is based in New York.

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3 Ways Generative AI is Transforming Investment Banking | AlphaSense