Big Data in Banking – Spectacular Case Studies & Applications

Big Data in Banking – It’s High Time To Cash-in on Big Data

Big Data is renovating the world and it has left no industry untouched with its enormous benefits. It has emerged as a lifeguard for the Banking Industry. Big Data has saved a lot of revenues from the banking firms so far and has a lot more to offer in the coming years. It gives them a sigh of relief as running a banking firm is not as easy as it looks. Big Data in the banking industry helps banks in managing the risk, detecting frauds and in the contentment of customers.

This blog will give you an insight into how Big Data is saving millions of dollars for some of the largest banks in the world. I recommend you to learn more about Big Data through DataFlair’s FREE Big Data Tutorials Library.

So don’t even blink. Let’s start reading how Big Data helps Banking Sector.

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Applications of Big Data  in Banking Sector

These are some applications of Big Data in Banking sector-

  • Risk Management
  • Fraud Detection
  • Customer Contentment

Here is a detailed explanation of Big Data applications in the banking sector.

1. Risk Management

Establishing a robust risk management system is of utmost importance for banking organizations or else they have to suffer from huge revenue losses. To stay alive in the competitive world and increase their profit as much as they can, organizations have to keep innovating new things. Through Big Data Analysis, firms can detect risk in real-time and apparently saving the customer from potential fraud.

big data in banking helps in risk management

 

Big Data in Banking Case Study – Risk Management

UOB Singapore

The United Overseas Bank (UOB) Limited, the third-largest bank in SouthEast Asia, has leveraged Big Data to direct risk management, the biggest area of concern for any banking organization.

Keeping the same in mind, UOB took a gamble with employing a risk management system that is based on Big Data. Calculating the value of risk is a time-consuming effort, usually taking up to 20 hours. Through its Big Data risk management system, UOB was now able to do the same task in just a few minutes and with the aim of doing it in real-time pretty soon.

Isn’t it interesting? Explore more engrossing Big Data Case Studies at DataFlair.

2. Fraud Detection

Here is the second application of Big Data in Banking sector – Fraud Detection.

The rapidly growing digital world is furnishing us with numerous benefits but on the other hand, gives birth to various kinds of frauds as well. Our personal data is now more vulnerable to cyber attacks than ever before and it is the biggest challenge a banking organization faces. Employing Big Data Analytics with some Machine Learning Algorithms, organizations are now able to detect frauds before they can be placed. This is done by identifying unfamiliar spending patterns of the user, predicting unusual activities of the user, etc.

big data in banking - fraud detection

Big Data in Banking Case Study – Fraud Detection

Danske Bank

Danske Bank, with a customer base of more than 5 million, is the largest bank in Denmark. The bank was struggling with its fraud detection methods having a very low percentage i.e. only a 40% fraud detection rate and managing up to 1200 false positives per day. This was an alarming rate for them and immediate action was required.

They then decided to join hands with Teradata, a leading database and analytics service provider company, to employ some advanced Big Data analytics for improving their fraud detection techniques and soon observed some substantial results. The bank saw a 60% reduction in false positives, expecting it to soon reach an 80% mark and an increase in the true positive rate by 50%. They also observed a massive operating profit of $70 million in 2018.
This is how Big Data analytics provided succor to the lagging Danske Bank.

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3. Customer Contentment

Let’s look at the third application of Big Data in Banking industry – Customer Contentment.

Considering the high amount of risk involved when you deal with the banking firms, to ensure the satisfaction of a customer is one of the most challenging tasks for them. From ensuring the safety of their transactions to providing them the most relevant and beneficial offers, customer retention is a lifetime journey for the banking firms. The data that they collect from their customers is now more important than ever. Analyzing their customer’s data on the basis of different parameters helps them in targeting their customers in a much better way.

Big Data in Banking Case Studies – Customer Contentment

Below are the two case studies of Customer Contentment –

  • JP Morgan Chases Big Data
  • Bank of America Big Data Case Study

A. JPMorgan Chases Big Data

JPMorgan Chase and Co. is the largest bank in the United States and the sixth-largest in the world. Additionally, it is the world’s most valuable bank in terms of market capitalization. With a customer base of over 3 billion, the amount of data it generates is unimaginable including a vast amount of credit card information and other transactional data of its customers.

They have adopted Big Data technologies, mainly Hadoop, to deal with this data. By employing Big Data Analytics, they are now able to generate insights into customer trends and the same reports are offered to its clients. They are able to analyze a customer individually and these reports are generated within seconds.

B. Bank of America Big Data Case Study

This is another Customer Contentment case study of Big Data in the Banking sector.

Big Data Extricating The Bank of America

Bank of America is one of the largest banks in the United States. It has a customer base of around 70 million. In the year 2008, they realized that their customer base was declining at an alarming rate as they saw their customers shifting towards smaller banks. This left them clueless and they were desperately seeking the reasons for this sudden downfall.

Big Data Analytics then came to their rescue. Through analyzing their customer’s data from a variety of sources such as their website, call center logs and personal feedbacks, they discovered that their end-to-end cash management system was too stiff for the customers as it hindered their freedom to access trouble-free and flexible cash management system. Though smaller banks were offering an effortless solution to it. Ultimately, they decided to end their all-in-one offering.

Soon in the year 2009, as a solution to these problems, they launched a website that was a more flexible online product, CashPro Online, and its mobile version, CashPro Mobile later in the year 2010. This was developed with an aim to provide their customers with a one-stop solution for all the services they offer.

Explore some more Real-Time Applications of Big Data which are applicable in various domains.

Conclusion

With huge amounts of data comes endless opportunities for all kinds of businesses across different domains to exploit that data, and the banking sector is amongst the most benefitted ones. The data that the banking firms collect is as critical and as valuable as anything else for them.

Banking firms have now understood the value of their data and are capitalizing on it. Data is like a second currency for them. Big Data analytics has been the backbone behind the revolution of online banking in the industry. It is now an integral part of the biggest banking firms across the globe. Big Data analytics has now empowered them to save millions which previously seemed impossible to them.

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If you would like to add any other application of Big Data in Banking Sector, share through comments.

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