Machine Learning In Finance – How Is It Used

Machine learning and AI has become a hot topic in recent years. Many different business sectors are investigating how machine learning can help their company to be more efficient and effective and finance is no different.

Machine Learning In Finance - How Is It Used

Machine learning can be of great benefit to the financial services industry. Finance often requires processing and analyzing large amounts of data and this is ideal for machine learning.

Many leading financial services companies regularly use machine learning in their daily operations due to the numerous benefits it provides. In this article, we will take a closer look at machine learning in finance and how it is used.

What Is Machine Learning?

Let’s begin by defining what machine learning is. Machine learning is a type of artificial intelligence (AI) that makes predictions through the use of statistical models. It can process enormous volumes of data to make the most accurate predictions possible.

Machine learning can help a financial service company reduce its risk by providing these better predictions. It can also streamline and speed up processes and ensure that the company has a portfolio that is better optimized and has more chance of a high return.

How Is Machine Learning Used In Finance?

Machine learning is very flexible and can be used by financial services companies in several different ways. Whatever the focus of the finance company is, there is sure to be a way that machine learning can benefit the company.

Let’s have a look at some of the most common ways that machine learning is utilized in the financial industry.

Algorithmic Trading

Algorithms are present throughout the finance industry. Their primary purpose is to help a company make better decisions and traders frequently build their own algorithms to help them process data.

These mathematical models can be used for a variety of purposes but are frequently used to monitor the market so that they can become aware of any factors that may cause security prices to either rise or fall.

The models can be trained to look out for various relevant parameters, such as the price or quantity of trades, and then be used to make automated trades that don’t need any human input.

The benefit of using machine-driven algorithms is that they can process enormous amounts of data that are far beyond the capacity of humans. When the data is processed, algorithmic trading can then make thousands of trades each day at a rate that human traders cannot keep up with.

Acting fast is often imperative with trading and algorithmic trading can make these decisions in a fraction of a second, giving traders that use this trading a huge advantage over traders that don’t.

As well as the ability of algorithmic trading to operate quickly and on large amounts of data, it also has the benefit of being impartial. Human traders can have their decisions clouded by emotion.

Traders may choose riskier or more cautious approaches depending on their ambitions and can also be affected by external factors such as personal or emotional problems.

This is not an issue with machine learning and is a large factor in why algorithmic trading is popular with so many financial institutions and hedge fund managers.

Fraud Prevention

One of the largest problems that financial services and institutions face is how to combat fraud. Fraud is rife wherever large amounts of money are concerned and it leads to losses of billions of dollars every year.

Although there are many different forms and sources of financial fraud, much of it comes from security breaches and important online data being stolen. The value of data held by financial institutions makes them a prime target for fraudsters.

Before machine learning, financial institutions protected themselves against fraud by looking for already identified patterns and rules. Fraudsters could easily bypass these by changing their approach and financial companies were constantly playing catch up.

However, machine learning means that every single transaction that is made can be checked for fraudulent activity.

It can compare each transaction against other data held, such as the historic transactions of that customer and the location of the transaction. AI can then flag the transaction for human review and follow-up or instantly block the transaction.

Portfolio Management

Many companies in the financial services industry make great use of robo-advisors. These are portfolio advisors that are trained using machine learning and use algorithms to advise customers and investors on how to manage their portfolios.

They can be adjusted to take into account each individual investor’s investment styles and goals.

In most cases, robo-advisors are far cheaper than their human counterparts. Investors can consult with a robo-advisor whenever they want by simply entering a few details into the system.

The robo-advisor will then assess this information and quickly respond with advice on the best investment opportunities for the criteria given. Robo-advisors are great for investors that have lower investment capital.

Loan Underwriting

Machine learning algorithms can also be used for loan underwriting. They can help financial service companies make quick decisions when it comes to credit scores and underwriting loans.

The algorithms are trained to look at enormous amounts of data in order to see if an individual would qualify for a financial product. Instead of customers waiting for a human to assess their information an algorithm can do it in moments.

Automated Customer Onboarding

Machine learning can help to automate many different processes that financial institutions have to do and one of the most common is customer acquisition and onboarding.

As so many customers are turning to online services to manage their investments, the expectation is that their accounts will be processed and arranged in mere moments. Without machine learning and AI, this is impossible.

Machine learning can give customers quick decisions on whether they will be accepted for an account. It can create their account and invest their money in mere moments as well as ensure that all of the legal paperwork is completed and accounted for.

Improve The Customer Experience

Whether it is providing 24/7 customer support, financial advice, or simply providing personalized service, machine learning is invaluable. AI is always working and always present so it can keep the same hours as your customers, regardless of how unsocial they may be. 

Machine learning can quickly react to any changes in the market and have answers and recommendations for your customers within seconds of their request. This is a quick and efficient level of service that humans cannot compete with.

Data Input

There are many processes behind the scene that are very time-consuming and repetitive. Financial companies need to manage a large amount of data and keep comprehensive records for both their customers and of the market.

Data is key in finance, and managing and updating data is made much easier with machine learning.

Final Thoughts

In this article, we listed several ways that machine learning is used in finance. Machine learning and AI can make several processes quicker and more efficient and it is of benefit to both companies and investors.

It can help to reduce fraud and predict the markets as well as deliver a higher level of customer service to investors.

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