By Akul Ranjan. Edited by Arjun Chandrasekar.
Machine learning is a subset of Artificial Intelligence(A.I.) which focuses on understanding and analyzing data/statistics in order to make informed decisions. It takes multiple variations of structured or unstructured data and computes a suitable algorithm or plan of action utilizing the input data. Machine learning is widespread, playing a huge role in our lives as it runs the Instagram auto friend features, Google recommendations, and much more.
Machine Learning vs A.I.
Machine Learning is a subcategory of A.I. and differs in a few ways. Machine Learning is maintained to take in data and analyze it based on patterns, while A.I. serves to make human-like decisions based on its own observations. A.I. is able to compute numerous complex tasks and is continually upgrading its intelligence, while Machine Learning has a smaller scope: doing only the tasks it was programmed for. Their end goals are similar, as both try to find the highest probability of an event occurring, and one is not better than another as they both have their uses.
Machine Learning in Finance
The finance industry uses Machine Learning heavily in order to manage risk, create algorithms for trading securities, prevent fraud, analyze loans, and even customer service. Most businesses use Machine Learnings in order to form and answer frequently asked questions from clients, or to give automated responses to customer complaints based on previous customer behavior. Machine Learning can identify irregular activity in bank accounts of businesses and consumers, such as unusually large transactions, unassociated IP addresses, and firewall breaches. Banks check a potential lender’s ability to pay a loan back using Machine learning, which analyzes credit score history, previous loan paybacks, and even criminal records to justify a client taking a loan. The most profitable usage of Machine learning is High-Frequency Trading (H.F.T.).
High-Frequency Trading (H.F.T.)
H.F.T. is used by hedge funds and large financial institutions, in which large amounts of underlying security are bought and sold in fractions upon fractions of a second. An example is a million apple shares being bought and sold in 0.001 of a second by these complex Machine Learning algorithms. These algorithms use their knowledge and input of previous data to determine how a stock may move in the next second, day, or even year and place trades accordingly. The creators and uses of Machine Learning in H.F.T. are called Quantitative Traders or Quants for short. Some prestigious H.F.T. firms include Jain Street and Goldman Sachs.