4 Use Cases of Machine Learning in the Financial Industry

The financial sector has never been static and continues to develop, innovate and adapt to an ever-changing business world and volatile customer needs. The enormous volumes of data currently being generated daily bring with it the pressing demand for more advanced techniques to analyze and extract value from it.

As financial institutions increasingly focus on digital transformation initiatives, machine learning (ML) is a promising technology that many firms are considering implementing. According to Nvidia’s 2022 State of AI in Financial Services survey report, 58 percent of respondents (500 financial professionals) have already incorporated machine learning in their daily practice. The same survey also revealed that ML is the most popular technology within the AI domain.

But where and how can it be used? To understand this for your particular business case, you’ll need to contact a mature software development company with ML experience like Yalantis. In this article, we explore a non-exhaustive list of typical machine learning applications in finance. But let’s first discuss the benefits of ML adoption.

How finance companies can benefit from machine learning

The goal of machine learning is not to replace humans but rather to assist people in their decision-making processes. Machine learning enables computers to learn from experience and adapt their behavior so that they can perform tasks more efficiently than when relying on pre-programmed rules or instructions.

The following are some of the most common benefits of machine learning in the financial industry:

Faster decision-making. Machine learning enables companies to process huge amounts of data at high speeds, enabling them to make faster decisions on all business levels: strategic, tactical, and operational. This helps them provide enhanced services to clients and understand their needs much more precisely.

Improved customer experience. By analyzing large volumes of customer data, different finance firms can provide personalized offers to customers based on what they need. Machine learning can help financial companies identify patterns in how people use their accounts and where they spend money to tailor offers to specific groups of customers. For example, if a customer only makes transactions on particular days, a bank can send them a reminder when the time of the next transaction is approaching so the customer doesn’t miss it.

Increased service accuracy. The most obvious benefit of using machine learning is improved accuracy. Machine learning algorithms are designed to perform tasks like identifying credit card fraud or predicting stock market movements with a high degree of precision. These algorithms are also adaptive — if they make an incorrect prediction, they’ll recalculate based on new information and improve their predictions over time.

Reduced costs. Machine learning can help financial companies to reduce their expenses by automating certain tasks that have traditionally been performed by humans, such as customer support.

Let’s now move on to the actual financial applications of machine learning aimed at enhancing financial services.

Use case #1. Financial forecasting and monitoring

One of the most common use cases for ML in finance is forecasting and monitoring. Financial institutions need to forecast future cash flows so they can make better business decisions. They also want to monitor customers’ portfolios in real-time to detect any anomalies that could indicate fraud or other issues.

Traditional methods for financial forecasting and monitoring have been based on historical data, which only provides a limited view of what may happen in the future. Machine learning can be used to supplement traditional forecasting models with data generated by algorithms that learn from historical data, enabling businesses to better predict future trends and make more informed decisions.

Use case #2. Customer data analysis

The process of analyzing financial customer data can be done manually or using machine learning algorithms. The advantage of machine learning is that it can automate the process of analyzing large volumes of data and finding patterns that humans might not be able to spot right away.

A prime example of how financial firms can use machine learning to analyze customer data is fraud detection. Let’s consider an example from machine learning for banks. If you’re a bank and need to know whether someone is trying to use a credit card illegally, you can analyze transaction trends by means of ML algorithms and determine if there are any suspicious activities.

Financial institutions can also use customer data analysis with machine learning to identify trends in customer behavior such as changes in spending habits and interest rate changes that may affect profitability. This information can then be used to adjust lending policies or marketing strategies to better meet the needs of individual customers or groups of customers who share similar characteristics.

Use case #3. Investment predictions

Stock markets are extremely volatile and can change dramatically overnight due to various reasons such as economic news or political events. Machine learning algorithms can help predict how these changes will affect stock prices so that investors can take advantage of them when they happen.

Machine learning algorithms can analyze historical data to find patterns that indicate when certain value assets will perform well in the future (or poorly). These patterns can then be used as the basis for your investment strategy. For example, if you know that investing in stocks during periods when oil prices are high tends to lead to negative returns in the long run, then you may refuse this idea.

Use case #4. Credit scoring

Another area where machine learning has shown great promise is credit risk assessment — determining whether someone should be granted a loan or credit card based on their financial history. This type of risk assessment system needs to take into account many different factors such as income level, employment status, and payment history when making its decision about whether or not someone should be allowed access to credit.

Future prospects of machine learning adoption in the finance industry

Machine learning is steadily winning global financial markets. In 2022, the Bank of England and the Financial Conduct Authority (FCA) conducted a joint survey of 71 financial institutions to learn how far they’re on their journey of adopting machine learning. The survey discovered that 79 percent of financial firms had most of their ML applications in the final stages of development and expected them to significantly improve their daily operations. The Bank of England and FCA expect that this machine learning proliferation will only continue across the UK financial industry.

And we think that it’s only a matter of time till we witness a massive rise in machine learning adoption across financial institutions worldwide. When more financial institutions implement ML systems properly, they have a lot to gain. Operating on large amounts of data, ML can fine-tune strategies, reduce worst-case scenarios, lower operational costs and save time in the process.

Hopefully, we’ve helped you understand how machine learning can be used in finance. Adopting machine learning processes into your daily routine won’t be particularly easy. But when devoting enough time and effort, you’ll be able to better understand ML techniques and their benefits for your business. So if you want to kickstart your machine learning adoption journey as smoothly as possible, find a reliable software development partner with solid machine learning expertise in finance.

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