By now you are of course well aware of the phrase ‘greenwashing’, but beware: there is a new term in town. Last month, Chair of the Securities and Exchange Commission Gary Gensler publicly warned businesses against ‘AI washing’, whereby firms make false claims over their use of artificial intelligence in much the same way others have over their unfounded sustainability assertions. The warning comes after the Federal Trade Commission in February cautioned companies that it would be on the lookout for bogus AI claims.

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With so much hype around the potential for AI, it is easy to see why companies may be tempted to jump on the bandwagon. Indeed, even the mere mention of AI integration can turn investors’ heads. But executives operating in the finance sector must approach this new and exciting technology with caution. Despite the media frenzy surrounding new developments in artificial intelligence, the number of proven use cases for the technology are relatively few at present – particularly in the investment banking arena. Nevertheless, one compelling use case is emerging, and it isn’t where you might expect.

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Historically, whenever a breakthrough technology has arrived on Wall Street, cash equities trading has been the first desk to adopt it. The asset class is exchange traded, highly liquid and transparent, which lends itself well to the integration of cutting-edge tech. Take algorithmic trading and transaction cost analysis, for instance. Meanwhile, just as cash equities has often led the pack, fixed income has almost always lagged it. Unlike equities, the vast majority of the fixed income market is traded over the counter (OTC). This presents challenges with regards to technology implementation. However, with AI, we could be on the cusp of a paradigm shift.

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Given fixed income is mostly traded OTC, the market is highly opaque, with different pockets of illiquidity scattered across all parts – particularly in corporate bonds. This is largely responsible for the wide range of different views when it comes to prices for multiple instruments. In addition, bond markets have grown increasingly complex and nuanced in recent years, while the processes that determine valuation have remained relatively simplistic. Given weeks can sometimes pass by without a block of specific high-yield bonds hitting the market, a considerable number of instruments may be left without market-based prices. It is here that AI could prove highly advantageous. By gathering all the disparate data relevant to the fixed income desks, and then deploying analytical AI software on top, banks may be able to solve some of the long-standing pricing and illiquidity issues in the market.

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While identifying valid use cases for AI is a tough challenge, it is only half of the battle for senior banking executives. The hardest part is of course executing on it. Unless financial institutions seek to develop their own AI program in-house – which is usually a highly costly and resource-intensive endeavor – it will be necessary to partner with a tech vendor. Here, it is worth keeping in mind Gary Gensler’s warning around AI washing.

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Although the new year has now come and gone, the hype around AI remains as frenzied as it was last year, with the latest headline being that Big Four accounting firm Deloitte is rolling out a generative artificial intelligence chatbot to 75,000 employees across Europe and the Middle East. Amid the continuing hysteria, AI washing will likely proliferate. Financial institutions should subsequently look to establish relationships with technology vendors that have specialized in integrating AI long before the ChatGPT-driven AI mania erupted in late 2022. Aside from such a vendor being immune to the AI washing label, they are likely to possess a proven track record of success in the field. Indeed, as new use cases for AI appear across capital markets, banks must ensure they are insulated against the growing risk of AI washing and select vendors wisely.