In financial services, AI is edging ever closer towards an inflection point. A 2023 survey from Ernst & Young revealed that banking and capital markets leaders are proving the least skeptical about the technology’s use cases, with banks such as Citi and J.P Morgan already leveraging AI to enhance customer experience and unlock hidden insights to inform trading decisions.
The generative AI revolution has accelerated this process following advancements in machine and deep learning, which has made it increasingly clear that 2024 has heralded a new era for the adoption of AI across banks and capital markets. How is this likely to pan out?
Director (Product Management), Digital Assets and Blockchain at Lab49.
Transforming the front office with generative AI
Having spread across almost all businesses, the question for banks is not “if” AI will impact their operations, but how. For example, generative AI is anticipated to save banks considerable time and effort, with a 2024 Accenture report highlighting how the predicted impact on the banking sector includes lower costs, faster revenue growth and more powerful contact center processes.
However, the real “sweet spot” is in the front-office. In 2023, Deloitte predicted that the top 14 global investment banks could boost front-office productivity by 27%–35% by using generative AI. This includes saving time on creating documents such as pitches, industry reports and due diligence. Through its ability to consolidate information at pace, generative AI can help reduce the costs of content creation, enhance analytical capabilities and improve electronification.
Before widescale adoption of generative AI however, organizations should carefully consider the associated risks to ensure their systems are protected, and to also boost stakeholder trust in their approach. Firms will need to ensure their current digital transformation strategies are sophisticated enough to both interoperate and take full advantage of these new systems, else risk a time-consuming implementation process and the need for costly revisions.
AI-driven sales and trading
Today, banks are increasingly leveraging AI as a “co-pilot”, which allows them to capture and synthesize customer intelligence in a timely way. A great example of this was the 2023 launch of an internal initiative at Morgan Stanley Wealth Management with OpenAI, to provide more personalized client insights. Several large tier 1 banks also use AI to assist their commercial bankers with what products to offer each client, and cross-selling efforts. A particular innovation is the ability for AI systems to recommend pricing for bankers in real-time to secure the best possible deal price for a client while effectively understanding client needs.
From a skills perspective, this will have far reaching implications in reshaping roles and responsibilities for bankers. 2023 research from PWC has already indicated the concerns of employees linked to digital upskilling, with 39% of employees indicating they currently lack sufficient training in digital and technology skills from their employer.
An expanded roll out of AI across banking organizations will also raise questions around the traditional apprenticeship model within IB and commercial banking, which currently relies on significant amounts of research from associates and analysts. There are already signs of significant banking vacancy cuts across 2024 and 2025, and LLMs will increasingly pick up the slack where access to deep and high-quality fundamentals analysis on companies, commodity markets and other sectors is required. Increasingly, promotions to VP and Director level will focus on interpersonal skills, where connecting with clients will become more valuable as modelling and product knowledge is taken over by AI models.
Connections for financial services
Think of AI as a flashlight that you are holding in a dark room. The room is filled with information and actionable insights hidden in the darkness that AI can help to illuminate. As banks and financial institutions increasingly re-orient technology stacks and data infrastructure around generative AI and other variants, they will be able to start feeding LLMs large amounts of proprietary data which will allow for connections and inferences that would not have been possible previously. The use of AI in this way is already present outside financial services – for example, US space agency NASA is tapping AI generated insights to design components that are lighter, and stronger, than any human could design.
A similar type of insight discovery could be achieved within banks and market groups. By feeding AI models holistic data sets consolidated from various systems and formats – which could include synthetic datasets generated by generative AI – financial services organizations can potentially uncover insights that are not visible to the naked eye. This becomes even more true when attempting to model different scenarios for use in risk management or asset price forecasting, or to refine existing models based on certain conditions, such as macroeconomic indicators including job numbers and inflation, or company-specific earning reports. In future, AI may even be able to suggest rebalancing portfolios based on inputs in ways that are unexpected and effective.
In 2024, there is no doubt that the gap will increasingly widen between those companies that embrace AI and the productivity gains it produces and those who do not. As we look ahead, we will see the generative AI offerings of companies including OpenAI and Microsoft become increasingly combined with other forms of AI in financial services. As banks reap the potential benefits from the tech, AI will leave no department in financial services organizations untouched. Ultimately, getting the thinking right on AI now will generate substantial for the financial services sector throughout this year and beyond.
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