For years, large amounts of data have been compiled by trained financial professionals. To assess the financial status of companies. A lot of time was spent organizing, comparing, and checking all that data. In some companies, humans are still responsible for much of this work. A growing number of companies rely on machines to handle large amounts of financial data.
Machine learning has enabled companies to find lost returns, and improve financial planning.
Key Uses of Machine Learning in Fintech
Portfolio management – Many companies help consumers manage their portfolios through “robotic advisers”. These are algorithms that create a portfolio based on the goals of the individual.
Trading- With the use of algorithms; systems learn to make thousands of transactions per day. High-frequency trading uses machine learning to analyze financial markets in real-time. Thereby making trading decisions in seconds.
Fraud and Security – With the rise of the Internet, and online information provision, security risks have increased. We hear about how data is stolen or leaked. How someone’s identity has been stolen, or how your credit card can be hacked by using it in certain locations. In the field of fraud, systems equipped with machine learning can recognize patterns in the use of the card. Because of machine learning, fraud detection has improved by 60%.
Insurance – If consumers want to take out a loan or a certain type of insurance. A financial advisor will test the demographics and behavior against a standard risk estimate. Using machine learning, computers could access millions of information sources about the customer. Including different trends in a particular area on base decisions. These could be age and job, as well as demographic trends in the area or outside influences.
Customer Service – High-quality customer service is often reserved for those who use many financial services. Companies are trying to change this by using machine learning in their customer service software. Algorithms in live chat can teach computers to respond more humanely when interacting with customers.
Conclusion
As computer algorithms can mimic human language more and more accurately. customer service will become less and less dependent on people. Security will become less dependent on alphanumeric passwords and rely more on biometric input.
In terms of trading, machines will become capable of interpreting human behavior and world events. Thereby predicting how markets will respond.
The possibilities seem to be endless as machine learning becomes more sophisticated. The above key uses of machine learning in fintech are enough proof. And one thing that is easy to predict is that machine learning will not disappear.
