Navigating regulatory challenges in artificial intelligence-driven finance while maintaining compliance and trust

Financial markets have a long storied history of being some of the earliest adopters of emerging technologies, with the introduction of the telegraph allowing for faster access to trading information providing more equal participating conditions to traders and thus increasing market efficiency. The invention of the computer brought about a whole new era in algorithmic trading which has been around in some form as early as the mid-1970s. With the rise of algo-trading, and the associated efficiency gains derived from it, traders started to search for more complex market signals that computers could better decipher than the human brain, allowing for even speedier arbitraging of mis-pricings acrhaving the ability to decode and analyze them to supplement established trading strategies seemed a holy grail moment for quantitative traders (Bisht et al., 2022; Boute et al., 2022; Kim et al., 2022). Not surprisingly, the floodgates opened to a plethora of AI-driven hedge funds and trading firms attracting untold amounts of capital, with many of the corporates focused on a certain level of secrecy around their operations. However, the use of AI tools and the underlying data has since burst the bubble and democratized hedge fund trading as alternative investment products become open to institutional and retail investors alike. These developments have allowed for further scrutiny into the pitfalls of lack of regulation and oversight on these funds. Using the classical financial formulation of creating alpha through active investment management, this paper will analyze the AI-involving driving forces within the financial industry, ranging from high-frequency trading, risk management, quantitative asset management, robo-advisors, sovereign credit rating, to the credit credit sector, crowdsourcing, and AI-specific funds.