Top 5 Machine Learning Use Cases for the Financial Industry

by Elena Moldavskaya,
Business Analyst, Intetics Inc.

Over the last several years, the financial services industry has been focusing on how to use Artificial Intelligence (AI) to exceed customer expectations, reduce operational costs, and make smarter overall business decisions. Machine Learning is an important aspect of AI that is helping finance companies reach these important business goals.

Because the finance industry collects a high volume of Big Data gathered from its customers, it is perfectly suited for the benefits of Data Mining. There are several new financial applications based on Machine Learning algorithms that are already being utilized by banks and financial organizations in order to gain a competitive edge.

In this post, we will cover five of the top Machine Learning use cases for the financial services industry and how they are changing the way we think about banking and finance in general.

1. Security

The highest level of security is crucial to banking and finance applications. Consumers are becoming more and more concerned about online and mobile application security when it comes to their personal information. They need more than just hard-to-guess passwords to protect them from increasingly sophisticated hackers. A new level of security is required.

A prominent and rising trend for financial applications is biometrics. Voice and facial recognition based on Machine Learning models is a much safer method than passwords alone for proving identification when accessing financial applications.

2. Personalized marketing

Financial organizations already have a ton of customer data. In addition to general banking information, such as account balances and transactions, they can collect data on purchases, spending habits, channel usage, and geo-locational preferences in order to create a 360-degree view of the customer.

Machine Learning algorithms can be used very efficiently for data analyses and personalized marketing offers. This allows marketing teams to deliver the right product to the right person at the right time and on the preferred device. Dynamic personalized pricing based on Data Mining models is also a good way to increase sales.

3. Customer service

Consumers are always looking for financial advice and personal recommendations. Even more importantly, they want it fast and they want it convenient. Today, more and more individuals prefer using voice-activated solutions, such as Amazon Alexa or Apple’s Siri. It’s easier for a customer to ask, “What was the balance of my credit card account 35 days ago?” or “How much did I spend on my trip last month?” than having to go through and analyze historical account records.

Voice-activated financial applications and Machine Learning enhanced chatbots are rapidly expanding and are some of the most promising short-term AI applications. Virtual customer service assistants have to be built with robust natural language processing engines and data science techniques to analyze finance-specific customers interactions. It’s very likely that chat or voice-activated personal assistants will be a viable option for millions of customers in the next few years.

4. Personal recommendations

Another aspect of Machine Learning for customer service is the ability to provide personalized financial advice for customers on how to save money, how to detect unusual transactions, and the ability to notify the customer with special offers or messaging.

For example, a robot-advisor can suggest a financial portfolio that matches the goals and risk tolerance of a particular client. Other examples include insurance recommendation sites that help users choose the most relevant plan and banking services that suggest a certain type of credit card or deposit account based on specific needs.

In the future, financial applications using Machine Learning technology will become increasingly personalized, highly-secured and user-friendly. They will also be considered more trustworthy, more objective, and more reliable than human advisors.

5. Sentiment analysis

Many future Machine Learning applications will be related to understanding social media, news trends, and other data sources.

For example, credit scoring systems and fraud protection techniques are well-known applications of Data Mining analytics in the banking industry. This trend is based on the volume of information collected and used as predictors in Data Mining models. Social media interactions and other data could also be used as additional sources of information in risk management.

Applications that can help make predictions for the investment market are another important use case for Machine Learning. Fluctuations in the stock market are caused by many human-related factors. Social media, news, and other types of Big Data collected and analyzed can help make smart predictions for investments. These models should be able to replicate and enhance human “intuition” and discover new trends and investment possibilities.

Conclusion

Advancements in Machine Learning algorithms and Artificial Intelligence are having a huge impact on the financial industry. Moreover, the number of uses cases will be increasing in the very near future. This list is simply the tip of the iceberg when it comes to all the ways AI technology will enhance and secure our financial transactions over the next several years.

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