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How Machine Learning is Changing Finance

Most, if not all, of us use computers in our jobs every day. While we may not think about it too often, those computers are getting smarter. We are not even talking about machines as advanced as the robotics side of AI. Normal everyday computers are learning faster and sometimes completely without human intervention.

These developments in machine learning are impacting computer learning algorithms in all areas, including the finance industry.

Lees ook: The Fundamentals of Machine Learning [whitepaper] 


Machine Learning

In general, machine learning refers to the ability for computers to learn new skills and actions without being programmed to perform those actions. These algorithms allow computers to analyze new information by comparing it with existing information.

This process of learning means that the computer can acquire new ways of analyzing data without the need for changes to the algorithms. The system can alter its own code based on the new information is comes across.

Machine Learning in Finance

For many years, high qualified financial professionals compiled and analyzed large amounts of data to evaluate the financial state of companies. Much time went into organizing, comparing, and checking all that data.
In some companies, humans are still responsible for much of that work, but a growing number of companies are now relying on machines to handle large amounts of financial data.

Machine learning has helped companies find missed revenue, maker better financial plans, and use their time more effectively. In fact, a 2015 McKinsey & Company report showed that out of a sample of 12 European banks who made the change to machine learning, some of them increased their sales by 10% and decreased expenses by 20%.

Current Applications of Machine Learning

Machine learning has already made a big impact on the finance industry and this does not look to change anytime soon. At the current pace of technology, experts predict that machine learning will make an even bigger impact.

Portfolio management – Using machine learning, many companies help customers manage their portfolios with the help of robo-advisors. These are not actual robots but algorithms that create a portfolio based on the goals and risk tolerance of each individual.

Trading – Using algorithms, systems can learn to make thousands or even millions of trades in a day. High-frequency trading, as it is called, requires machine learning to analyze financial markets in real time in order to make trading decisions within seconds.

Fraud and security – With the rise of the internet, online data, and wireless systems also comes the rise of security risks. We often hear about how data is stolen and leaked, how someone’s identity has been stolen, or how your credit card can be hacked simply by using it at certain locations. Old systems used a certain set of parameters and rules to study and respond to security risks, systems equipped with machine learning can adapt to threats as they evolve in real time. In terms of fraud, these systems can what for patterns in card use to predict when fraud is being committed. Machine learning fraud detection has been shown to improve detection rates by 15%, reduce false positives by 50%, and increase savings by 60%.

Underwriting – When customers want to get a loan or certain type of insurance, a financial agent will compare their demographics and behaviors with a standard to assess risk and give the customer a certain price or interest percentage based on these factors. Using machine learning, computers could have access to millions of pieces of data about the customer and about various trends within an area to make decisions. These could include things like age, marital status, and job, but also demographic trends in the area or other outside factors that could impact the individual.

Customer serviceQuality customer service is often reserved to those people who invest a lot of money or who use a lot of financial services. Companies are trying to change that by incorporating machine learning into their customer service programs. Algorithms in live chat functions can help computers interact with customers more like humans. The algorithms help the computer analyze words, compare the interaction with past experiences, and respond in a way that will help the customer.

The Future of Machine Learning

In 2016, Gartner described machine learning as a top 10 strategic technology. The stated that companies need to learn how to use it effectively if they want to remain competitive. A McKinsey report stated that financial institutions need to digitize their main processes by 2025.

Customer service will rely less on humans as computer algorithms mimic human speech at more accurate levels. Security will rely less on alphanumeric passwords and more on facial recognition, voice recognition, and other biometric inputs. In terms of trading, machines will be able to interpret human behavior and world events with greater accuracy in order to predict how markets will react.

The possibilities are seemingly endless as machine learning becomes more advanced. One thing that is easy to predict is that machine learning is not going away. Financial institutions need to invest in this technology in order to compete with other companies.

Tjip-MachineLearning-whitepaper

Lees ook: The Fundamentals of Machine Learning [whitepaper]

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Bram Nawijn

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