Generative AI in Investment Banking

While they are not newcomers to digital transformation, investment banks (IBs) are increasingly adopting generative artificial intelligence (genAI) across their organizations. In the last couple of decades, investment banks have adopted AI for customer-centric interfaces and to automate data functions.

With genAI as the new face of digital transformation, IBs stand to gain significant benefits with its uses. They are uniquely positioned to leverage genAI to streamline their investment and operational functions, across all aspects of dealmaking and research and due diligence conducted by analysts.

It is predicted that among large global investment banks, genAI can boost their front-office productivity by 27% to 35%, resulting in additional revenue of $3.5 million per employee by 2026. In addition to realized efficiencies and reduction in spend, the adoption of genAI technology is proving to play a role in talent retention and employee engagement. 

Below, we’ll discuss the evolution of AI and genAI in investment banking, how genAI is revolutionizing workflows by enhancing productivity, how IBs can position themselves for success in this next chapter of digital transformation, and how generative AI (genAI) tools streamline internal knowledge with leading market intelligence to drive informed, confident decisions.

Evolution of AI in Investment Banking

Investment banks are no stranger to cutting edge technology and for decades have pioneered various forms of automations and advancements throughout their organizations. The advent of genAI is therefore an organic progression along this path of digital transformation. 

Within their retail divisions, banks introduced ATMs in the 1960’s, and in the decade that followed, electronic card-based payments. Banks also adopted algorithmic trading strategies to minimize trading transaction costs and to execute trades at lighting speed. 

In the 2010’s AI and machine learning capabilities began to expand their footprint across bank functions. On the retail side, they emerged as customized banking and financial tools such as virtual assistants, fraud detection services, and risk management tools. 

Beyond customer service and operational enhancements, IBs started to harness AI for their investment functions with sophisticated trading algorithms, performing market analysis, and automating processes and tasks. For decades, investment banks incrementally laid the foundation for the revolutionary technological advancements that were to come in the form of generative AI.

GenAI in Investment Banking: Use Cases

GenAI has remarkably intricate, time-saving, and intuitive everyday use cases to optimize workflows. Some of these include: extracting insights from large bodies of content to accelerate research, structuring and organizing data, surfacing trends and disruptors in the marketplace more quickly, reducing time spent on manual tasks and conserving resources, compounding knowledge faster than competitors, and back-office operations.

Investment banking teams can use genAI to streamline deal research and due diligence by automating data gathering and analysis from various sources including financial statements, earnings call transcripts, market data, and news articles. GenAI is also streamlining IB’s trading models and is creating efficiencies through lower transaction costs, capturing market sentiment, and removing inconsistencies. 

Equity research is an area of focus greatly optimized by genAI’s evolving technology. An example is with quarterly earnings reports. Prior to genAI, analysts and associates would have to manually sort through earnings reports issued by the companies they track to obtain the information and insights they needed. 

Research teams now benefit from summarizations that capture company sentiment and market analysis to accelerate their fundamental research process, and drive sound, timely investment recommendations. The ability to comb through an infinite amount of content, information, and data to extract the most meaningful and relevant insights is a game-changer.

Positioning for Success with GenAI

Studies show that major US firms suffer annual losses exceeding $40 million as a result of everyday operational inefficiencies due to inadequate knowledge sharing. As investment banks continue to implement generative AI capabilities, it is important to develop a technology roadmap to ensure success and organizational alignment, which involves identifying a best-in-class solution.

Often, firms face a common dilemma when evaluating and potentially implementing a genAI technology solution— to build, buy, or partner? 

  • Building: While a customized option, developing a proprietary solution is a significant undertaking that requires significant investment of time, resources, and funds in order to effectively sanitize and curate large data sets. Additionally, it requires sophisticated technical resources, personnel onboarding, and maintenance considerations. It is, however, the most secure route to ensure intellectual property is safe and compliant. 
  • Buying: A pre-built out-of-the-box model offers quick use and implementation, with no heavy lifting required, and is typically a more cost-effective route. However, pre-built solutions are not trained on verified, business-specific datasets and can pose reputational risks.
  • External Partnering: This option marries the best qualities of an in-house build with the appeal of a pre-built genAI offering. An external partner will have already invested the time, resources, and have an existing development team in place to maintain and optimize their proprietary interface. It enables investment teams to have quick set-up and connectivity to access the most cutting-edge technology, and will come equipped with data and security standards.

Exploring potential genAI configuration and gathering consensus to determine the best solution is an important step in the technology infrastructure process. It ensures that firms are competitively positioned to enable knowledge sharing within their organization, and optimize processes to prevent lost time and resources, and prevent analyst burnout.  

Related Reading: Enterprise GenAI: To Build or Buy a GenAI LLM?

The Next Chapter of Digital Transformation

Poor technological infrastructure results in knowledge, research, and content lost in a sea of disparate systems and teams working in silos. Critical components of a firm’s market intelligence—internal research, investment memos, client deliverables, strategy presentations, and meeting notes—are fragmented and inaccessible, resulting in lost opportunities and doubled work. 

As a result, firms lose the ability to swiftly pivot in response to market conditions and struggle to maintain a competitive edge. And equally detrimental— research teams get burned out trying to find a needle in a haystack in a high stakes, time-sensitive environment. 

Tools like AlphaSense’s Enterprise Intelligence solution solve this challenge, unlocking the value of your firm’s prized internal knowledge with an end-to-end search and discovery solution. Our purpose-built AI searches, summarizes, and interrogates your proprietary internal data alongside a vast repository of 300M+ premium external documents to surface the most valuable insights. 

It allows you to automatically integrate and tag your PDFs, SharePoint documents, CIMs, Excel sheets, and more. You can also interrogate long documents with natural-language chat that go straight to the source to surface the most relevant insights. Our solution also enables you to maintain permissions on your content, so only the right teams have access to specific documents and folders. 

With our Enterprise Intelligence solution, you can instantly discover and verify insights while removing inefficiencies, potential blind spots, and reputational risk. When equipped with the in-house intelligence and knowledge they need, firms can streamline knowledge and proprietary intelligence, retain engaged employees, and significantly reduce talent attrition.

Gain the Competitive Edge with AlphaSense

AlphaSense’s leading AI-search driven market intelligence platform equips you with the insights, market intelligence, trends, and expert opinions to remove the complexity and guesswork of conducting your research. Paired with our Enterprise Intelligence solution, investment banks can unlock and integrate their valuable internal knowledge seamlessly and with ease, for the most comprehensive internal and external knowledge set.

Learn how our industry-leading genAI platform can help you sift through the noise, accelerate your research, and bring efficiencies to your workflow.

Learn about technology’s evolving role in investment banking and its broader implications for the industry’s future in our newest webinar: Technology Adoption as a Revenue Driver in Investment Banking with Huw Richards.

Harness the power of genAI and competitively position your team—start your free trial of AlphaSense today.

Barbara Tague
Barbara Tague
Content Marketing Manager

Barb is a Content Marketing Manager covering the financial services segment at AlphaSense. Previously, she managed the content program at a global financial services firm.

Read all posts written by Barbara Tague