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Big Data in the Banking Industry – The Main Challenges and Use Cases


The quantity of data you generate each day is mind-boggling. Every purchase you make with your credit card, every email you write, and even every website you visit. The world population generates 2.5 quintillion bytes of data each day, all told.

The most forward-thinking organizations across a wide range of industries can make use of this data, and the banking sector is no exception.

In an age when over half of the world’s adults use digital banking solutions, banks have the data needed to reimagine how they do business in order to become more efficient, customer-focused, and ultimately more lucrative. In order to stay up with the competition, the issue is: how can you maximize the value of your data?

Big data in banking has a variety of applications, and we’ll explore a few of them here (with real-life examples).

As a starting point, consider the significance of big data in banking from a broader perspective.

The importance of big data in banking: The main benefits and challenges for your business

IDC predicts a global revenue of $260 billion for big data and business analytics solutions by 2022. The estimated total for 2018 is $166 billion, an increase of 11.7% over 2017.

Investing in big data and business analytics (BA) is no surprise in banking.

In banking, the advantages of big data are obvious:

1. Big data gives you a full view of your business

From consumer behavior patterns to internal process efficiency and even larger industry trends, we can see how these factors influence the whole market. Because of this, you’ll be able to make data-driven business decisions and reap the benefits.

2. It allows you to optimize and streamline your internal processes

A combination of Oracle digital banking experience, machine learning and artificial intelligence (AI) can do this. The consequence is a big increase in performance and a large reduction in operational expenses.

3. Big data analytics in banking can be used to enhance your cyber security and reduce risks

Detecting and preventing fraud and other potentially harmful behaviors may be done by using sophisticated algorithms.

To be sure, there are certain impediments to big data application in the financial sector. Big data in banking is posing a number of issues, some of which are listed below:

1. Legacy systems struggle to keep up

The banking industry has historically been a hesitant adopter of new technologies, with 92 of the top 100 global banks still using IBM mainframes for the majority of their operations. Therefore, it’s no surprise that fintech acceptance is on the rise. Traditional financial institutions can’t compete with customer-focused and nimble startup companies.

On the other hand, big data makes matters worse: most outdated systems are unable to handle the increasing burden. Using an out-of-date infrastructure to gather, store, and analyze the necessary volumes of data might jeopardize your system’s overall stability.

In order to meet this challenge, firms must either expand or entirely re-engineer their systems.

2. The bigger the data, the higher the risk

The second thing to remember is that wherever there is information, there is danger. Financial institutions have an obligation to keep their customers’ personal information secure at all times.

Nevertheless, just 38% of businesses throughout the globe, according to ISACA International, are prepared to deal with the issue. As a result, one of the most pressing concerns in banking is cyber security.

Additionally, stricter data security requirements are being implemented in the United States. In light of the GDPR’s adoption, companies throughout the globe face new constraints when it comes to collecting and using customer data. This is an important consideration.

3. Big data is getting too big

It’s no wonder that companies are struggling to keep up with the sheer amount and variety of data. If you’re attempting to go through your data to see what’s important, this becomes much more apparent.

The amount of potentially usable data is increasing, yet we still have a problem sifting through all of the noise. Because of this, organizations must make sure that they have the necessary resources and capabilities in place to deal with an ever-growing volume of data.

There are several benefits to using big data in banking, notwithstanding the problems that have already been discussed. There are several benefits associated with using data, including the ability to gain new insights, the liberation of resources, and the cost savings that may result.

The issue is how to maximize the use of big data in banking.

The future of big data in banking looks bright: Make sure to keep up.

As you can see, there are several instances of how big data is being used in the banking industry. Despite this, all of these measures have only scraped the surface of the problem. The full potential of big data in the banking industry has yet to be fully realized.

Big data, according to a whitepaper published by Global Transaction Banking, is crucial to the success of banks. 62 percent of banks feel that big data is vital to their success. Despite this, just 29% of them believe they are obtaining enough commercial value out of their data.

To remain relevant and competitive, banks must rethink their business models and embrace data-driven approaches to customer service and operations. Furthermore, big data in the banking industry might assist you in improving and expanding your firm.

JMR Infotech can assist you if you are interested in exploring this possibility but are having difficulty identifying acceptable big data applications in the banking industry for your company’s specific needs.


Our staff has a wealth of expertise in deploying fintech products of all degrees of complexity, as well as in developing OBDX big data solutions from the ground up. Our work with several banks has included, among other things, the implementation of a sophisticated data mining system that collects, normalizes, visualizes, and analyzes diverse financial data on a regular basis.