Every man, woman and their dogs are talking about AI right now – but it seems they are only doing so in the abstract. Worryingly, even global institutional investors appear to be gripped by the constant hype and hyperbole. Nvidia ($NVDA) is a prime case in point. In February, the semiconductor vendor added a staggering $220bn to its market cap in just one hour after reporting earnings. Basically, this meant Nvidia overnight added more market cap than the entire value of Intel and Micron Technology combined. Then, in late May, the firm became the first computer chip producer to reach a $1tn valuation. Indeed, even the Bank of England The Bank of England and FCA recently published a discussion paper Artificial Intelligence and Machine Learning, to further their understanding and to deepen dialogue on how Artificial Intelligence (AI) may affect their respective objectives for the prudential and conduct supervision of financial firms.

There is not a planet big enough, nor an investor pocket deep enough, for all of the blue-sky thinking around AI. The reality is, right now, very few people understand what AI actually means, let alone how it can be implemented to help drive greater efficiency in their business. It’s high time we identified some practical use cases to cut through all this noise. As a first step, we want to help answer a fairly simple question: if you are a senior executive within an investment bank being bombarded with all this AI literature, where on earth should you start?

Well, whenever there has been a technological innovation in capital markets – the starting point has traditionally been cash equities trading. Exchange traded and, by definition, highly liquid and transparent technology has historically driven equities to lead and others to follow. One of the most prominent followers has been fixed income. Over the years, the bond market structure reform dog has been well and truly wagged by the cash equities tail. From algo trading to transaction cost analysis, fixed income trading desks have historically adopted equities tech innovation. However, with AI, we could very well be on the cusp of a paradigm shift.

Unlike equities, the vast majority of the fixed income market is traded over the counter (OTC). As a consequence, the market is highly opaque with different pockets of illiquidity scattered across all parts – particularly in corporate bonds. This is why there is such a wide range of different views when it comes to prices for multiple instruments. Bond markets have also become more complex and highly nuanced over the past decade, while valuation processes are still relatively simplistic. Days or sometimes weeks can go by without a block of specific high-yield bonds hitting the market, meaning a greater number of instruments are left without market-based prices. By gathering all the disparate data relevant to the FI desks, and then deploying AI on top, this is a realistic way to solve some of the long-standing pricing and illiquidity issues in this market.

Looking beyond the fixed income space, investment banks – along with a wide variety of financial institutions – can also utilise AI to enhance key business operations. What’s more, they can do so without allocating the significant time and capital required to develop their own AI-powered software. A growing number of tech providers like VoxSmart are empowering financial institutions across the world with AI-driven tech that can quickly and seamlessly integrate with their fundamental day-to-day business processes. At VoxSmart, we draw on AI to enable financial firms to reliably adhere to fast-changing regulations, easily monitor staff communications and unearth winning data-driven insights.

Learn more about how we harness the power of AI across our solutions here.

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