If you take the amount of data generated by yourself on a daily basis, you may be surprised at its significance. Every credit card transaction you make, every message you send and every web you visit will contribute to a total of 2.5 quintillion bytes of data created by the whole population of this world every day.
This results in endless chances for many businesses throughout different domains to capitalize on that data. And the banking is doing the same.
In spite of the fact that digital banking is used by around half of the world’s adult population, financial institutions have enough data to change the way they conduct tasks in order to become more effective, more customer-centric and more profitable. The question here is that how to use big data effectively to keep up with the competitors.
This post is going to make it clearer about common use cases for big data in banking. First and foremost, let’s get to know the role of big data in banking.
How big data plays an important role in banking: both advantages and challenges
It is no doubt that banking is among the business domains that creates the highest investment in big data technologies.
In terms of benefits, big data provides you with a full view on your business from customer behavior analytics to internal process effectiveness and predicting market trends. In other words, you can make informed decisions driven by data to achieve business results.
Also, it enables users to optimize and streamline the internal processes thanks to using machine learning and artificial intelligence technologies. Thus, you will have a chance to boost your performance while minimizing operating costs.
Finally, big data analytics in banking can be taken advantage to promote the security and reduce risks. By utilizing intelligent algorithms, you can recognize fraud and prevent possibly error-vulnerable actions.
On the contrary, there are some obstacles that prevent you from implementing big data in banking. Some of the most common big data challenges in banking include the following ones.
First of all, legacy systems have to make effort to keep up. The banking sector is innovating rather slowly. Currently, even 92 of the top 100 world leading banks now depend on IBM mainframes in their operation processes.
Regarding to big data, trying to gather, store and make analysis for the significant amounts of data with a conventional infrastructure may make you end up with risks.
Thus, companies are facing the difficulty of improving their processing capacities or entirely re-making their systems to deal with the challenge.
Secondly, the bigger the amount of data is, the higher the risks will be. In other words, if there is data, there is risk. It is obvious that banking services vendors have to guarantee that the user data they process is secured at any time.
However, only 38 percent of companies all over the world are willing to deal with the problem. This is the reason why cybersecurity is still among the most alerting problems in banking industry.
Moreover, data security regulations are becoming stricter than ever. The release of GDPR has imposed some certain limitations on business all over the world that would like to collect and make use of users’ data. This should be considered carefully.
In addition, big data today is becoming too big. Because there are a lot of different data types currently and its whole volume, it is no doubt that companies are trying to deal with it. This case is clearer when it comes to making effort to separate the valuable data from the useless one.
In spite of the fact that the share of potentially valuable data is increasing, there is still too much unused data to take out. In other words, businesses have to make a good preparation and take advantage of methods to analyze even more data and look for a new application for the irrelevant data.
In spite of coming with those challenges, the advantages of big data in banking easily justify any issues. The insights it provides you, the money it saves and many things have proved that data is a fuel that can boost your business to the top. Let’s see how to make use of big data effectively.
Big data use cases in banking
Data is always known as the most valuable asset that an enterprise could have. However, it is not just the data that matters. It is what you are going to do with it. In order to make the most out of this technology, you should understand how big data is applied in banking.
First of all, it is used to personalize customer experience. According to a survey, over 80 percent of companies agree that clients are seeking for a more customized experience. If a company can provide users with what they demand, they can increase their annual revenue by 18 percent.
Similar to other businesses throughout different domains, banks make use of big data in order to understand their users newer ways to deliver to them and finally offer more value in a meaningful manner.
What is more, your data can provide you with useful insights into user behavior and support you to optimize your customer experience properly. For instance, by getting a complete customer profile, you can predict what they are going to spend.
For instance, this approach is being used at American Express. The company’s branch in Australia depends on complicated predictive models to guess and prevent customer churn.
Through making analysis for the data about previous transactions, they are now able to realize the accounts that are most likely to close in a few months. Finally, they can take preventive measures to keep their clients from churning.
In conclusion, thanks to taking advantage of big data, companies and organizations can understand their clients’ needs better, recognize issues in their products and look for the most efficient way to solve the current problems in a proper way.