Over the past few years, machine learning in finance has become more prominent because of recent advances in available computing power. Because of this, leading banks and financial services are starting to develop AI technology in order to give them an edge in competition. The AI will streamline their processes by doing things such as optimising portfolios and underwriting loans.
One example of machine learning optimising finance are robo-advisors. Robo-advisors are an online application that provides the user with suggestions regarding finance. Furthermore, the robo-advisors provide portfolio management services that use algorithms and statistics to automatically establish and manage the investment portfolio of a client. The benefits of these programs include that they are usually much cheaper than consulting with a human financial advisor.
Another example of machine learning in finance is algorithmic trading, or the use of algorithms to conduct trades autonomously. In algorithmic trading, programs with a predetermined set of instructions are executed for actions such as placing trades on behalf of a trader. The instructions usually require user input such as timing, price, and quantity.
A final example of machine learning in finance is AI-drive hedge fund. What the machine learning program can do in this situation is automatically make stock trades without human interference. The reason that this is so beneficial is that an AI can make split-second decisions on when to buy or sell stocks which would enable it to get better trades than a human. This is because humans have to think out their decisions then take time to log on and either buy or sell the stock, while the AI can just do all that immediately.
Source: https://algorithmxlab.com/blog/applications-machine-learning-finance/