financial institutions

5 big data use cases in financial institutions

Financial companies rely heavily on data. We could even say it’s their most valuable resource. That’s why modern financial companies and institutions use big data analytics on a daily basis. According to Seagate, we can expect to see the growth of big data analytics in financial services at 26% CAGR up to 2025. In this article, we are going to examine five of the most common and useful use cases of big data in financial institutions.

Of course, the list is much longer, and the way a specific company uses big data depends on their strategy and approach to data. However, we can indicate at least five applications that seem to be widely used in the whole sector worldwide.

How is big data used in financial institutions?


As we’ve already told you, data is the most precious resource in financial companies. Thanks to data, they can target customers, segment and profile their marketing campaigns, and devise new products and offers. It all starts with accurate and effective customer data management. You see, banks have a lot of information about their customers, especially regarding their expenses and everyday habits. If you have your favorite coffee shop where you buy coffee every morning, your bank knows about it. And that’s just the tip of the iceberg!

With big data in finance, financial institutions can keep their customer data clean and organized. This way, conducting analysis on these datasets can be effective and used with the company’s growth in mind. Of course, big data is not the only technology used in managing customer data. For instance, one of the most extensively used technologies is machine learning. These algorithms can, e.g., analyze the influence of some specific financial trends and market conditions on the company’s development by learning from customers’ financial and historical data.


Banks manage and process customer data for a number of reasons. Targeting customers with an adequate message and offer is one of them. Thanks to customer targeting (supported with segmentation and personalization, two of the crucial marketing techniques nowadays) banks and insurers can make tailor-made products and services that match market demand. This, obviously, directly translates to higher sales and faster growth.


Insurance and financial companies deal with a great deal of risk. If the risk assessment wasn’t done more thoroughly, unpaid mortgages and loans would become a serious problem, adversely affecting the bank’s cash flow. It’s a similar story in the insurance sector. According to Insurance Information Institute, 76% of insurance companies report problems in data integration. Without an accurate risk assessment, fraudulent claims would become a plague. That’s why effective risk assessment is of paramount importance, and big data analytics is the technology that makes it accessible.


Today, fraud detection is far more effective than it was just several years ago. Thanks to big data in finance, cybersecurity measures, and machine learning, the banking and finance sector are using big data to predict and prevent frauds such as identity theft, money laundering, and other frauds that can cost banks and their customers millions of dollars every year. That’s why financial institutions use customer data to analyze financial operations in order to indicate normal and safe ones and distinguish them from suspicious and risky ones. That’s a base for fraud detection machine learning algorithms to do their job.


The last application of big data in finance that we want to talk about today relates to the investment market. With thousands of investment opportunities out there, even the largest companies have a hard time tracking everything manually. This is where big data in finance steps into the game. Thanks to big data analytics, both customers and financial institutions can facilitate the investment process, primarily by indicating opportunities worth pursuing.

Also, when it comes to smart investments, so-called Robo-advisors are more and more popular. Today, you can find them, e.g., in an application called Betterment, but we sincerely believe they will be prevalent in the future investment market. Robo-advisors provide automated portfolio management services and thorough financial advice. This way, both companies and individual investors can assess which investment is worth their interest without the need to spend hours analyzing tables and overviews.

To sum up, we could say that big data in financial institutions is a true game-changer that allows these organizations to work and grow in a more effective way. However, the benefits of big data analytics are by no means limited to the financial sector! In fact, almost every medium-sized and large company can use it to grow business. If you want to find out more, take a look at our big data consulting services and reach out to see what we can do togethe

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