Machine learning will continue to change how financial institutions operate now and in the future.
Machine learning was part of the finance industry’s future well before mobile banking came on the scene.
Machine learning, a subset of artificial intelligence, has helped tackle complex issues in natural language processing and image and speech recognition. Most recently, in-depth learning, also known as neural networks, has emerged as one of the most powerful methods for learning tasks. The financial industry has been a part of this new wave.
The terms, artificial intelligence and machine learning, are typically used interchangeably as both methods convey the same general idea that software can make intelligent decisions. Some tactics such as unsupervised learning require little guidance, while others such as support vector machines require extensive training.
The Current State and Applications of Machine Learning
Nearly every application used in finance—including credit, operations, and trading cycles—have a different but important role to play. For example, operational and trade execution functions can take advantage of automation and algorithms to recognize changes in many standard markets. Some banking functions are automated by software that considers past trades and investment preferences of a customer. Credit verification already uses a high degree of automation. With machine learning, systems can detect unique activities and raise red flags for possible security threats. Loan and insurance underwriting is also another field in which machine learning is making advancements.
Futuristic Applications in Machine Learning
Applications that are going to change face of finance.
Finance specific chat bots which ask and answer customers’ questions through a chat conversation, keep logs of customer’s financial transactions like expenditures and savings. In the future, banks and financial institutions that allow for rapid querying and interactions might pick up customers from conventional banks that require users to login to their online banking portals. The chat experience is not custom today in banking or finance, but it might be a feasible option for millions in the near future.
Sensitive Data Security
In the future, maintaining sensitive account information may not be required. Future security measures may include facial recognition, voice recognition, and scans of fingerprints. ATM machines too may be redesigned as well for users to show their face before checking account balance or withdrawing money.
Machine Learning in Banking
The “Robo Advisor” is an algorithm built to calibrate a financial portfolio to the goals and risk tolerance of the user. In the future, increasingly personalized and regulated apps may be considered more reliable, objective, and trustworthy than real people.
Machine Learning in Trading
Many future machine learning applications will use social media, news trends, and other data sources, not just stock prices. The stock market moves in response to countless human-related factors that have nothing to do with ticker symbols. The hope is that machine learning will be able to replicate human intuition in financial activity by discovering new trends.
Machine Learning in the Financial Industry
Machines today are performing trades worth billions of dollars every day in response times measured in microseconds, popularly called high-trading frequency. Nearly 73% of everyday trading is executed by the machines. Based on very complex data, these machines consider all the historical financial data available.
Just as machines in other areas have not fully replaced people, machine learning algorithms are unlikely to replace people altogether. Software will, however, make significant changes to some tasks. The one certainty is that today’s landscape requires that we do more to bridge technology with every sector of society.