Wholesale banks have vast expertise in traditional AI. It’s a sound investment that is paying off again as generative AI takes hold.
“It sure is a hell of a lot easier to just be first.” That’s one of many memorable lines from Margin Call, a 2011 movie about Wall Street. And it’s a good summary of wholesale banking’s stance on AI and its subset machine learning. Corporate and investment banks (CIB) first adopted AI and machine learning decades ago, well before other industries caught on. Trading teams have used machine learning models to derive and predict trading patterns, and they’ve used natural-language processing (NLP) to read tens of thousands of pages of unstructured data in securities filings and corporate actions to figure out where a company might be headed.
Today, some CIB institutions are using AI at scale and reaping enormous benefits. But much of the industry lags behind the leading CIB institutions; many banks are using bespoke, artisan-like approaches that are inherently less productive. Another problem: bankers often see areas across the front, middle, and back offices as too complex to use machine learning. A few leading banks have made AI-related progress on some of these areas, including relationship manager (RM) support and advisory, compliance and risk decisions, and client service on complex bespoke products (think foreign-exchange hedges on forward commodities agreements).
Now comes generative AI: you may have heard of it (ahem). The McKinsey Global Institute (MGI) estimates that across all of banking, wholesale, and retail, gen AI could add between $200 billion and $340 billion in value—for example, through greater productivity.1 The technology has huge potential for the full CIB business system. As the name suggests, the new tools are incredibly adept at coming up with content that can serve as a first draft in many areas. But they’re also adroit at understanding previously published content; gen AI adds a new element of natural-language understanding (NLU) that can take NLP-based applications to an entirely different level.
Consider a couple of examples. CIB banks can strengthen their compliance work by using gen AI to sort through regulators’ reports, read them intelligently in the way that a junior compliance officer would, find the most relevant report, and then write a synopsis for a senior officer to act on. They can improve their competitiveness in client servicing by using the technology to write documents that are currently produced by hand. And they can tap tools such as Broadridge’s BondGPT2 to offer investors and traders answers to bond-related questions, insights on real-time liquidity, and more.
Some banks are already starting to capture the opportunity from gen AI. JPMorgan Chase has filed a patent application for a gen AI service that can help investors select equities.3 Morgan Stanley has built a tool to help RMs deliver relevant ideas to customers in real time.4 Many other banks, however, are just tinkering at the edges. Still others are hung up on concerns about computing cost or stalled because of intellectual-property constraints.
We firmly believe that banks need to work through their challenges and avail themselves of the significant benefits to be gained from gen AI. In our experience, depending on the application, gen AI can improve productivity in core CIB activities by 30 to 90 percent. All told, productivity and other benefits might add 9 to 15 percent to CIB operating profits, in MGI’s estimate. (We should note that those are the kinds of efficiencies we’re seeing in early use of our own new gen AI tool, Lilli.)
In this article, we look at the areas where gen AI has the most potential for corporate and investment banks, and the risks that banks need to watch for. We conclude with an outline of the capabilities that banks will need if they are to thrive in the era of gen AI.
Where to apply gen AI
Corporate and investment banks are putting gen AI to work across the business system (see sidebar, “Potential applications of gen AI in wholesale banking”). They’re making the most progress in three areas: new product development, customer operations, and marketing and sales.
In new product development, banks are using gen AI to accelerate software delivery using so-called code assistants. These tools can help with code translation (for example, .NET to Java), and bug detection and repair. They can also improve legacy code, rewriting it to make it more readable and testable; they can also document the results. Plenty of financial institutions could benefit. Exchanges and information providers, payments companies, and hedge funds regularly release code; in our experience, these heavy users could cut time to market in half for many code releases.
For many banks that have long been pondering an overhaul of their technology stack, the new speed and productivity afforded by gen AI means the economics have changed. Consider securities services, where low margins have meant that legacy technology has been more neglected than loved; now, tech stack upgrades could be in the cards. Even in critical domains such as clearing systems, gen AI could yield significant reductions in time and rework efforts.
In customer operations, banks are using gen AI to extract, search, and summarize unstructured servicing information and translate it into machine-readable instructions. That comes as a big relief: up to 60 percent of CIB servicing is done through email and manual documentation. In post-trade services, banks are using gen AI to read the documentation on corporate actions—and, critically, banks are using NLU to assess the implications of corporate actions across clients and products. And in the middle office, banks are automating manual tasks. Gen AI is proving capable of writing technical documents such as financial; environmental, social, and governance (ESG); and audit reports. It’s also being used to write loan contracts such as mortgages.
A leading Asian corporate bank, for example, had a problem: its RMs were spending a lot of time painstakingly summarizing the bank’s sustainability performance...
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