The Stanford One Hundred Year Study on artificial intelligence by and large does an excellent job of articulating what the current and future roles of AI look like in our society. It explores what technologies have been instrumental in accelerating growth in the field and what barriers exist to gain full integration in the real world. There is, however, one main issue with the study: it fails to mention the financial sector among the eight domains with the potential to be most significantly revolutionized by artificial intelligence. 

While it may seem like a stretch to argue that leaving out the financial sector is truly a major issue with this study, the panel’s attempt to comprehensively evaluate the future of AI falls flat without, at the very least, an acknowledgement of the game-changing potential of large-scale machine learning applications in financial markets. This snub indicates that the panel did not believe AI to be nearly as impactful in global/domestic finance as the other areas, such as education for example, which is an extremely narrow-minded perspective. At the center of AI development in the financial sector are quants, or quantitative analysts, who specialize in applying statistical methods to financial problems. The presence of quants on Wall St has exploded, particularly in the last five years. In 2013, they were responsible for about 14% of all US stock trades, that figure is now up to 27% in 2017. For reference, trades made by individual investors account for approximately 29% of all trading volume. Not only has the quantity of quant-based trades increased, the quality has as well. Over the past five years, quant-focused hedge funds gained about 5.1% a year on average, while the average hedge fund rose 4.3% during the same time period.

The true future of financial artificial intelligence, however, lies in machine learning which will eventually enable computers to develop their own predictive algorithms for the market without reliance on human support. This technology would be incredibly disruptive in the financial sector and has the potential to structurally alter the way we trade/invest. AI research and development in finance is supported heavily by ample funding and access to massive amounts of useful data. Funding for finance-focused artificial intelligence ventures are plentiful due to the high potential for value creation and the nature of the industry. More importantly perhaps, the financial sector creates and logs an enormous amount of data every day. Much of this data is accessible and complete making it optimal for use in training large-scale machine learning algorithms. The main factor inhibiting AI from being even more present in modern-day financial markets is its technical limitations. The fact is, current-day machine learning is still not at a point, from both a development perspective and an effectiveness perspective, that it could truly start to be the dominant form of stock trading in the US. However, based on the field’s exponential growth, that may not necessarily be the case five years from now.

In conclusion, the One Hundred Year Study panel’s decision to not include the financial sector as one of the eight main areas revolutionized by AI is a significant flaw in the study. This is based on the sheer magnitude of the opportunity to disrupt an institution as fundamental as the US stock market and the potential socioeconomic impacts that could come along with it.