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. 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 BondGPT to offer investors and traders answers to bond-related questions, insights on real-time liquidity, and more.