What is Machine Learning (in Finance)?
Machine learning in finance is now considered an essential aspect of several financial services and applications, including managing assets, evaluating levels of risk, calculating credit scores, and even approving loans. Machine learning is a subset of data science that provides the ability to learn and improve from experience without being programmed.
As an application of artificial intelligence, machine learning focuses on developing systems that can access data pools, and the system automatically adjusts its parameters to improve experiences. Computer systems run operations in the background and produce outcomes automatically according to how it is trained.
Machine learning tends to be more accurate in drawing insights and making predictions when large volumes of data are fed into the system. For example, the financial services industry encounters enormous volumes of data relating to daily transactions, bills, payments, vendors, and customers, which are perfect for machine learning.
Many leading fintech and financial services companies are incorporating machine learning into their operations, resulting in a better-streamlined process, reduced risks, and better-optimized portfolios.
How Machine Learning is Used in Finance
There are several ways machine learning and other tenets of artificial intelligence (AI) are being employed in the finance industry. Some of the applications of machine learning in finance include:
Algorithmic trading refers to the use of algorithms to make better trade decisions. Usually, traders build mathematical models that monitor business news and trade activities in real-time to detect any factors that can force security prices to rise or fall. The model comes with a predetermined set of instructions on various parameters – such as timing, price, quantity, and other factors – for placing trades without the trader’s involvement.
Unlike human traders, algorithmic trading can simultaneously analyse large volumes of data and make thousands of trades every day. Machine learning makes fast trading decisions, which gives human traders an advantage over the market average.
Also, algorithmic trading does not make trading decisions based on emotions, which is a standard limitation among human traders whose judgement may be affected by emotions or personal aspirations. Hedge fund managers and financial institutions mainly employ the trading method to automate trading activities.
Fraud detection and prevention
Fraud is a significant problem for banking institutions and financial services companies, and it accounts for billions of dollars in losses each year. Usually, finance companies keep a large amount of their data stored online, increasing the risk of a security breach. With increasing technological advancement, fraud in the financial industry is now considered a high threat to valuable data.
Fraud detection systems in the past were designed based on a set of rules, which modern fraudsters could easily bypass. Therefore, most companies today leverage machine learning to flag and combat fraudulent financial transactions. Machine learning works by scanning through large data sets to detect unique activities or anomalies and flags them for further investigation by security teams.
It works by comparing a transaction against other data points – such as the customer’s account history, IP address, location, etc. – to determine whether the flagged transaction is parallel to the account holder’s behaviour. Then, depending on the nature of a transaction, the system can automatically decline a withdrawal or purchase until a human makes a decision.
Portfolio management (Robo-advisors)
Robo-advisors are online applications built using machine learning, and they provide automated financial advice to investors. The applications use algorithms to establish a financial portfolio according to an investor’s goals and risk tolerance.
Robo-advisors require low account minimums and are usually cheaper than human portfolio managers. When using robo-advisors, investors must enter their investment or savings goal into the system, and the system will automatically determine the best investment opportunities with the highest returns.
For example, an investor who is 30 years of age with a savings goal of $500,000 by the time they retire can enter these goals into the application. The application then spreads the investments across different financial instruments and asset classes – such as stocks, bonds, real estate, etc. – to achieve the investor’s long-term goals. The application optimises the investor’s objectives according to real-time market trends to find the best diversification strategy.
In the banking and insurance industry, companies access millions of consumer data, with which machine learning can be trained to simplify the underwriting process. Machine learning algorithms can make quick decisions on underwriting and credit scoring and save companies both time and financial resources used by humans.
Data scientists can train algorithms on how to analyse millions of consumer data to match data records, look for unique exceptions, and decide whether a consumer qualifies for a loan or insurance.
For example, the algorithm can be trained on how to analyse consumer data, such as age, income, occupation, and the consumer’s credit behaviour – history of default, if they paid on loans, history of foreclosures, etc. – so that it can detect any outcomes that might determine if the consumer qualifies for a loan or insurance policy.