The banking industry has witnessed the evolution of operations and delivery of services over the last few years. But it is a surprising fact that most banks have not been able to utilize the information in their own databases properly. This is gradually changing as the banking industry gears up to process the huge volume of data that is collected and created. The banking industry experts, in fact, say that there will be an immense increase in the volume of data before 2020. Therefore, big data in banking is a huge step towards the growth of the banking industry and will contribute immensely to its prosperity. Let’s take a look at the advantages of big data in the banking industry.
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Big Data in Banking
1. Prevention and Detection of Fraud
Big data in banking can help deal with one of the biggest challenges faced by banks today – frauds. Banks and financial organizations use analytics to find the difference between fraudulent interactions and legitimate business transactions. With the help of analytics and machine learning, banks can easily define normal activity on the basis of customer history and differentiate it from unusual behavior, indicating fraud. The use of big data in banking systems will help suggest immediate actions like blocking any irregular transactions. This stops fraud before it occurs and enhances profitability.
2. Customer Segmentation
The use of big data in banking provides comprehensive insights into customers’ spending patterns and helps in understanding their needs and requirements. Through the use of big data in banking, banks can also categorize their customers based on different parameters like preferred credit card expenditures, commonly accessed services, and even net worth. This form of customer segmentation can enable the banks to target their customers in a better way through relevant marketing campaigns.
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3. Personalized Service
Customer segmentation facilitated by big data in banking services can be used to create and deliver schemes and services designed for specific customers as per their requirements. The past and present expense analysis can clearly tell the banks how to get a higher response from their customers. Personalized products and services help the banks to create more meaningful relationships with their customers.
4. Risk Management
Detecting fraud at the early stage forms a large part of risk management. The use of big data in banking can help manage risk efficiently for the banks. It locates and presents huge data on a single platform, minimizing the number of risks to a manageable number. Big data plays a pivotal role in integrating the requirements of the banking industry into the centralized platform. This helps the banks to reduce the frauds and manage risk.
Companies in the banking industry, by keeping up with big data and other upcoming trends, can understand the client’s requirements in a better way. This can, therefore, help in improving banking services. The task of implementing big data in banking is taking shape and soon it will be adopted completely by the entire industry.
