Easternpeak.com - Have you ever thought of the amount of data you create every day? Every credit card transaction, every message you send, even every web page you open… It all sums up to a total of 2.5 quintillion bytes of data that the global population produces on a daily basis.

This opens endless opportunities for the most forward-thinking businesses across a number of domains to capitalize on that data, and the banking industry is no exception.

While digital banking is used by almost half of the world’s adult population, financial institutions have enough data at hand to rethink the way they operate, to become more efficient, more customer-centric, and, as a result, more profitable. The question is: how do you get the most out of your data to keep up with the competition?

In this article, we will talk about common use cases for big data in banking (with real-life examples). To start with, let’s take a step back to see the bigger picture and talk about the role of big data in banking.

According to the study by IDC, the worldwide revenue for big data and business analytics solutions is expected to reach $260 billion by 2022. This year, the projected numbers will hit $166 billion, up 11.7% compared to 2017.

It comes as no surprise that banking is one of the business domains that makes the highest investment in big data and BA technologies.

The benefits of big data in banking are pretty clear:

• Big data gives you a full view on your business: from customer behavior patterns to internal process efficiency and even broader market trends. This means you can make informed, data-driven decision and, subsequently, obtain business results.
• It allows you to optimize and streamline your internal processes with the help of machine learning and AI. As a result, you get a significant performance boost and reduced operating costs.
• Big data analytics in banking can be used to enhance your cybersecurity and reduce risks. By using intelligent algorithms, you can detect fraud and prevent potentially malicious actions.

On the other hand, there are certain roadblocks to big data implementation in banking. Namely, some of the major big data challenges in banking include the following:

1. Legacy systems struggle to keep up

The banking sector has always been relatively slow to innovate: 92 of the top 100 world leading banks still rely on IBM mainframes in their operations. No wonder fintech adoption is so high. Compared to the customer-centric and agile startups, traditional financial institutions stand no chance.

However, when it comes to big data, things get even worse: most legacy systems can’t cope with the growing workload. Trying to collect, store, and analyze the required amounts of data using an outdated infrastructure can put the stability of your entire system at risk.

As a result, organizations face the challenge of growing their processing capacities or completely re-building their systems to take up the challenge.

2. The bigger the data, the higher the risk

Secondly, where there’s data there’s risk (especially taking into account the legacy problem we’ve mentioned above). It is clear that banking providers need to make sure the user data they accumulate and process remains safe at all times.

Yet, only 38% of organizations worldwide are ready to handle the threat, according to ISACA International. That is why cybersecurity remains one of the most burning issues in banking.

Plus, data security regulations are getting stringent. The introduction of GDPR has placed certain restrictions on businesses worldwide that want to collect and apply users’ data. This should also be taken into account.

3. Big data is getting too big 

With so many different kinds of data and its total volume, it’s no surprise that businesses struggle to cope with it. This becomes even more obvious when trying to separate the valuable data from the useless.

While the share of potentially useful data is growing, there is still too much irrelevant data to sort out. This means that businesses need to prepare themselves and bolster their methods for analyzing even more data, and, if possible, find a new application for the data that has been considered irrelevant.

Despite the mentioned challenges, the advantages of big data in banking easily justify any risks. The insights it gives you, the resources it frees up, the money it saves – data is a universal fuel that can propel your business to the top.

The question is how to use big data in banking to its full potential.