Banking sectors have been utilizing the latest technologies for a very long time. There are several technologies that are developed specifically for the banking and financial sectors also. In fact, a lot of new technologies have been integrated into the tools and solutions to make the most of the new technology. Big data is one of them. Big data technology is being used by the banking and financial sectors for various reasons. It is turning out to be pretty beneficial for the banking sectors.
Big data is all about the processing huge volume of data in relatively lesser time. Big data analytics is the study of the data which is collected from a wide range of different sources. The banking sectors across the world have the potential to generate a huge quantity of data. And, all this data is now managed and analyzed in order to get useful insights which will turn out to be quite beneficial for the banking sectors.
Big data in banking sectors
Millions of transactions happen on a daily basis in the banking sectors. Most of the transactions are real-time transactions and thus, a huge amount of data is being generated quickly. Therefore, it is pretty difficult for big data specialists to capture the data without any hurdle. The banking sectors need a high-end big data tool that is capable of handling real-time data seamlessly as for the banking and financial sectors. Banking sectors perform a wide range of different analysis like the analysis related to the loans, time series analysis, analysis related to the past and present transactions of the customers, analysis for the identification of the probable fraudulent transaction. Also, banking sectors may perform analyses to improve their marketing and sales strategies.
Big data benefits for banking sectors
Segmentation of the customer
With the help of data analytics, the baking sectors can get a lot of information related to the clients. Be it, the pattern of handling their finances or managing their investments. Also, customer segmentation analysis helps to differentiate the customers as per their demands, needs, and interests. It empowers the banking sectors to track each and every client exchange. Based on the analysis, the banks will be able to do customer segmentation based on several parameters. They would know more about the frequently accessed services. At the same time, the big data experts would be able to get insights related to things like the net worth of the customers, the chosen credit card expenditures, etc. All this data helps to divide the customers into various groups and offer them different types of services.
Data analytics also help banks to lessen the possible risks. Additionally, with the help of the insights, the banks could also try to mitigate the losses. Basically, it gave them the power to foresee potential threats and take actions to avoid or prevent them. Risk management gives the banks the power and knowledge to reduce the possible credit loss by doing overdue payment recovery in a more efficient manner. Management of the risk enables the banking sectors to identify the appropriate credit exposure restrictions. Also, the analysis made it easier for banking professionals to sanction the appropriate loan amounts.
Some of the banks were also analyzing the dangerousness or probable threats via building analytical models. The insights also helped the banks to make risk scorecards. With the help of the scorecards, the banks were able to analyze the probable threat from customers. A few of the techniques involved in things like market liquidity, stress testing, etc.
Fraud detection is almost a part of risk management. Banks now have an intelligent fraud prevention team that identifies the possibility of any fraud based on the assessments derives from the data analytics. The insights highlighted the suspicious patterns of certain customers or their transactions. Additionally, the banks were able to find out the fraudsters or the possible fraudsters through the analysis. This was done to prevent any fraud attacks on the banks. The fraud detection teams had started using the latest technologies like Artificial Intelligence etc., to prevent the banks from fraudulent activities.
Let’s understand this with an example, Danske Bank, one of the premium banks in the world were facing issues due to possible frauds. Therefore, they collaborated with Teradata to prevent possible frauds. After the company started making the most of big data analysis, they saw an impressive 60% drop in the false positives. Therefore, fraud detection via big data analytics definitely turned out to be pretty helpful for the banking sectors.
For growth and progression
Big data analytics is being used to monitor and assess the growth of the banks. Various type of big data is being used for the analysis, in order to find out ways to determine the existing growth of the banks. Also, with the help of the analytics, the banking experts would be able to come up with the possible ways to boost the growth of the bank. Also, the banks are able to perform competitive analysis to understand how well the other banks are doing. After all, the analysis of the competitors is done to allow the banks to learn from other’s experiences. Also, it gives a clearer picture of the bank’s performance. Additionally, the banks may need to study their own market, past and present to identify possible ways to improve the existing position of the banks.
Banking sectors are using big data analytics to make plans to retain their existing set of customers. It is important for banks to retain their customers for a long period of time. Acquiring new and new customers is not easy. Also, getting more business from the existing set of customers is always beneficial for the banks. By offering personalized services, the banks are mostly able to retain their customers. American Express is a live example. By analyzing the past transactions and through predictive analysis also, Amex is able to conceptualize topnotch customer retention strategies.
Banking sectors are using big data developers India and the usage is only expected to be higher as it is quite beneficial for the banks.