As we know, machine learning in finance is now considered a key 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 pools of data, 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 tends to encounter enormous volumes of data relating to daily transactions, bills, payments, vendors, and customers, which are perfect for machine learning.
Nowadays, 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.
• Machine learning is a branch of artificial intelligence that uses statistical models to make predictions;
• In finance, machine learning algorithms are used to detect fraud, automate trading activities, and provide financial advisory services to investors;
• Machine learning can analyze millions of data sets within a short time to improve the outcomes without being explicitly programmed.
How the Machine Learning is used in Finance?
There are several ways in which 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
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 active involvement.
Unlike human traders, algorithmic trading can simultaneously analyze large volumes of data and make thousands of trades every day. Machine learning makes faster 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 common limitation among human traders whose judgment may be affected by emotions or personal aspirations. The trading method is mostly employed by hedge fund managers and financial institutions to automate trading activities.
Fraud is a major 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, and it increases 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 and prevention
Fraud detection systems in the past were designed based on a set of rules, which could be easily bypassed by modern fraudsters. 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 behavior of the account holder. 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 that are 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 their risk tolerance.
Robo-advisors require low account minimums and are usually cheaper than human portfolio managers. When using robo-advisors, investors are required to 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 with a savings goal of $200,000 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 optimizes the investor’s goals according to real-time market trends to find the best diversification strategy.
Loan underwriting
In the banking & insurance industry, companies access to millions of consumer data, with which machine learning can be trained in order 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 that are used by humans.
Data scientists can train algorithms on how to analyze millions of consumer data to match data records, look for unique exceptions, and make a decision on whether a consumer qualifies for a loan or insurance.
The algorithm can be trained on how to analyze consumer data, such as age, income, occupation, and the consumer’s credit behavior – 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.
