Machine learning have proved to be one of the fruitful applications in finance industry. In fact, this has effected the entire genre, even before the advent of mobile banking apps, or the proficient chat bots. Owing to the high volume, accurate historical records, and as well as the quantitative nature of the finance world, it is better suited for artificial intelligence.
It has been estimated that today, machine learning has come to play, in an integral role in the various phases of the financial ecosystem. Its wings are spread across the genre like; approving loans, to even managing assets.
Today we will explore both the current and future applications of artificial intelligence in finance. It is also intended as an executive overview rather than just being a granular look at all applications in this genre.
The Role of Machine Learning in Finance Industry
Current Applications
Let us have a look at some of the examples of machine learning being put to use actively today.
The Portfolio Management
The very famous term robo-advisor, was essentially unheard, just five years ago, but it now holds a commonplace in the financial landscape. The term is also misleading and does not involve robots at all. Rather, the term is related to algorithms, built to calibrate the financial portfolio to the goals and also to the risk tolerance of the user.
The system is said to calibrates to changes in the user’s goals and as well to the real-time changes in the market, aiming always to find the best suited solution for the user’s original goals. These also have gained significant traction over human advisors.
The Algorithmic Trading
Going back to the 1970’s, the algorithmic trading, sometimes also called “Automated Trading Systems,” used to involve the use of complex AI systems. This was done to make extremely fast trading decisions. These algorithmic systems helped in making thousands or millions of trades per day, hence, they were given the special term “High-Frequency Trading” (HFT).
Most of the hedge funds and as well as the financial institutions do not openly disclose any of their AI approaches to trading (for good reason), but it has been believed that machine learning and the process of deep learning are playing an integral role in calibrating trading decisions in the real time.
The Fraud Detection
The process of combining more accessible computing power, internet is becoming more and more common. Along with this there has also been an increase in the amount of valuable company data being stored online, and then you have a “perfect storm” for the data security risk.
While the previous financial fraud detection systems were majorly depended on the complex and robust sets of rules, the modern fraud detection goes beyond. This is the one that actively learns and calibrates to all the new potential (or real) security threats. This is hence, the place of machine learning in finance for fraud.
