Big Data Use Cases in Banking

In the face of rapid and profound changes in the economic environment, the banking sector must change methods to get to know its customers better and interact with them promptly. The knowledge of people using banking services and the emergence of intelligent technologies have allowed these financial institutions to create massive datasets. Banks recognize big data as an opportunity to discover new ways to be more competitive in order to enhance customer relations and improve ROI. There is no doubt that BD has thoroughly changed the look of modern banking. Let’s take a look at a few use cases of big data in banking.

Banks can be very innovative in terms of technology. They have a lot of modern technologies such as AI, ML (machine learning) and IoT (Internet of Things) at their service. These innovations, which regularly make headlines in digital and banking news, always revolve around the same star: Big data. Due to the raw character of the data, this topic is both enthusiastic and controversial in the banking sector. However, there is no doubt that big data revolutionizes marketing in banks, allows financial institutions, among other things, to get to know their clients better, and enables more effective security management.

Big data and banking: What are we talking about?

The evolution of big data and analytics is directly related to the development of digital technology and the proliferation of interactions based on data exchange. Moreover, these datasets are diversified: They can be structured (customer profile or transactional data) or not (voice, video, text, or image). As a result, the amount of facts about the behavior of individuals in various aspects of their lives has never been as significant or as accessible as it is today.

How does this data translate into the banking sector? Bank customers use payment cards and many other banking services, thanks to which financial institutions can receive information about users, such as earnings, withdrawals, savings, latest travels and purchases, and even preferred hairdressers or clothing stores. Banks are, therefore, a vital source of market information and, at the same time, have countless opportunities to use it, which they increasingly undertake in cooperation with big data consulting companies. Below, we present some examples of such collaboration.

Big Data: use cases in banking

Perspectives mainly focus on behavioral aspects of customers along with the analysis of their transactions and geolocation. Banks continually try to offer better products and services that meet customer needs, while helping them save money and, of course, generate additional income for the bank.


Big Data allows banks to fight fraud. Organizations in the banking sector can now monitor all transactions made with a credit card and receive notifications when a user makes an unusual payment, especially in terms of amount or location. Collecting vast amounts of data enables real-time identification of any abnormal behavior, thus preventing fraudulent use of credit cards or transfers. For example, by using the customer’s geolocation data, banks may request additional authentication to execute a suspicious transaction in case of doubt.


Improving customer service and increasing satisfaction are the key points of banking activity. To remain relevant on the market, financial institutions have to provide customers with high-quality services tailored to their needs. Banking is one of the most competitive sectors. Indeed, competition is particularly tough between traditional and internet-based banks (frequently referred to as neo-banks). Getting to know the customer better enables banks to establish a closer relationship with them to better respond to their needs. Banks’ big data strategy aims to improve customer knowledge and thus customer satisfaction. Specifically, this involves immediate personalizing services and products offered using data sources to which the customer has authorized access.


Banks and insurers currently use back-testing systems to evaluate the performance of their internal rating models and risk assessments, although these traditional systems have many limitations. For example, sometimes the data is outdated or not always available. However, the emergence of a public cloud and big data predictive technologies makes it possible to find an effective solution to this problem. By providing open and up-to-date information, the ability to inject and process large amounts of data, and rapidly test various scoring algorithms, these technologies optimize calculations and thus improve the performance and resilience of rating models, enabling specialists to assess risk correctly.


The data at the disposal of banks is precious but also vulnerable, which is why banks that want to use big data solutions must ensure their highest security. More and more cybersecurity solutions are based on big data in order to help in limiting the risk of cyber threats related to customers’ personal data. Furthermore, the development of advanced security systems to reduce the risk of data intrusion and the identification of fraudulent access to customer accounts allows you to minimize the response time to compliance requests to use the stored and analyzed data transparently, and thus in a much more effective way to ensure that the data is not ended up with the wrong people.


Predictive analytics allows you to identify strong trends that enable banks to plan all activities and future products effectively. The use of big unstructured data enables the delivery of forecast analyzes that are precise enough to predict consumer behavior. In addition, improving customer knowledge allows you to optimize the effectiveness of advertising campaigns, translating into banks’ financial results.

Big data consulting in the service of banking

Of course, the above examples of using big data in banking are only a part of all known solutions, thanks to which data changes the image of banking. There is no doubt that managing so much sensitive data that banks have at their disposal is highly complex and requires specialist knowledge. Moreover, their analysis and appropriate use for the performance of individual tasks of banks is definitely even more difficult. That is why banks more and more often decide to use the services of companies specializing in big data consulting. Using advanced technologies and tools helps to process data sets and draw business conclusions from them. Thanks to big data and their analysis, banks can develop, and their clients have access to more advanced and personalized services.

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