Machine learning (ML) is a facet of data science that uses experience instead of programming to learn patterns and improve performance. There is no doubt that it offers an exciting opportunity for the financial industry, where data rules the roost.
Machine learning, like artificial intelligence
, is designed with automation as the end goal. Ideally, machine learning will enable computer systems to access data and improve experiences based on them. All of this happens in the background. As such, there is no room for human intervention and, thus, no human errors or bias. Subsequently, this brings precision and accuracy in data insights, even when the volume of data is tremendous.
With the inexplicable power that machine learning brings to finance, many fintech and financial institutions are implementing it in a multitude of ways.
From an economic point of view, it is very premature to predict what we have for the next months and years, but within all the effects that this pandemic brought, it is notable that people have increased the desire to have their own companies and go digital.”
Oscar Jofre Jr., Co-Founder, President/CEO at KoreConX
Here are some of the applications that are currently in use.
1 Trading based on Algorithms
Traditionally, traders rely on mathematical models that are based on constant monitoring of trade news and real-time market activity. These models use some predetermined criteria like timing, quantity, pricing, and other such factors to make automated trades
Algorithmic trading involves the use of trained algorithms to make informed and precise trading decisions. However, it takes the analysis and monitoring aspects to the next level. It is able to process enormous amounts of data and make near-instantaneous decisions, which gives traders an edge over the market. It also eliminates emotion-based decisions, making the process truly revolutionary.
2 Detecting and Preventing Fraud
A fortified digital infrastructure
is a must for banking and financial institutions to operate in the modern world. However, fraud and cybersecurity threats continue to grow in significance. Traditional fraud systems are susceptible to modern fraudsters. However, with machine learning at the helm, organizations can sift through a massive amount of data in less time to identify anomalies or unusual activities. This way, security teams can be instantly alerted to act.
3 Computed Portfolio Management
Machine learning is taking portfolio management by storm with the introduction of robo-advisors. Robo-advisors are online applications that use machine learning to deliver financial insights to investors. They use algorithms that assess the investor’s goals and risks and provide a financial custom financial portfolio tailored to their risk tolerance. Not only are robo-advisors cheaper, but they also require low account minimums.
4 What’s the Writing on the Wall for Financial Services?
AI, machine learning, and deep learning are the heart of the technology-driven innovation in finance and banking. Fintech applications like Signifyd, that uses ML to spot fraud transactions during checkout stages on eCommerce sites are paving the way for multi-industry digital security. Another ML startup called Zest is empowering lenders with credit scoring solutions that help in risk management. These examples only illustrate the many innovations that are driving the exponential growth and use of machine learning in trading, investments, and finance.