Alex Floate, a member of our Emerging Fellows program examines the use of AI in fintech through his new blog post. The views expressed are those of the author and not necessarily those of the APF or its other members.

“Here is a summary of your weekly investment activity. Maynard has made the following changes to your investment portfolio,” my assistant announced. Maynard is the name I gave to my investing AI, who oversees my portfolio and makes decisions about where and how to invest my small nest egg.

“There were changes to twenty-three individual stock and seven bond positions. Analysis of long-range weather forecasts, with adjustments for anticipated climate changes, resulted in a modification to the commodity strategy with modified positions in Ethiopian coffee and Phillipino cocoa. The latest climate assessments have also caused modification to real estate investments.”

“Currency swaps from national currencies to Amazonians and Alibablers have also occurred. Twenty-seven micro-investments in the Lagos and Kinshasha metroplexes have been created, with twelve micro-investments recouped and closed. Your investment return averages seven-point-three percent, and your social scoring has increased thirty-one points.”

Maynard has, once again, pleased me with my return and the socially responsible method by which he achieved it.

Of course, it wasn’t always like this. True AI advisors have to assimilate vast amounts of data, analyze it, learn from it, and formulate a strategy that takes advantage of various possibilities. The analysis moves beyond basic linear financial analysis and into the multiple and complicated systems that support the investment and ultimately create value. The more it looks into these systems, the more it will learn about them and how they affect investments and strategies to gain value. Maynard also had to learn my personal ethical preferences to ensure transactions would not endanger my social credit score.

When AI advisors first came on-line, there were fears that it could cause trouble in the markets. Most concerning was that those AI agents could all come to the same conclusions and make the same trades at the same time, destroying value in some assets and over-inflating value in others. There were also concerns that AI advisors would learn to game the system and make moves that circumvent legal or ethical standards while covering their tracks. Although that has occurred, it has not been as widespread as discovered in the court case Global Securities Exchange Commission vs. “Alladin” (AI Agent # 234GXE36576). By tying AI agents to their human’s social score, it helped ally the fear of widespread ethical lapses.

What has occurred is an increase in the choices for the average investor. Before the internet, the majority of investing was placed through brokers, who often manipulated investors by steering them towards items that were more profitable for the brokers than the client. Average investors were also unable to enter other investment vehicles such as commodities, real estate, and small business start-ups. Although the brokerage model was partly to blame, it was usually due to a lack of knowledge; AI solved this last issue.

AI promised to even the odds that average investors could compete with the largest funds and firms, and for those who could afford a good AI agent, it did. Initially, it required champions and interventions by authorities to ensure widespread access to the various exchanges at reasonable fees. However, once set in motion, the financialization of the economy became everybody’s business, and business was good.