Role of Big Data Analytics in Banking

During the late '90s, Banks became detached from their customers. Their focus was on pushing financial products to the customers and hitting the sales targets, without considering the needs of the customer. In Retail Banking, Big Data plays a crucial role in improving Customer Service. Nowadays many Banks are planning to undergo a re-privatization process, emphasizing a focus on Customer rather than financial products.


With the advent of Machine Learning and Artificial Intelligence technologies, Retail Banking has been evolved and started to emphasize more on customers. Using Big Data Analytics, they developed a concept called "Personology" - to better understand their customers and meet their needs. This helps to exploit the high richness of customer data the Bank has, which includes the customer behavior data, personal information as well as the transactional data. Wishing customers personally on their birthday is one of the examples for this scenario. That's not exactly Big Data Analytics but it's in line with the concept of Personology.


Nowadays customers will receive personalized recommendations about how they would benefit from deals and promotions being offered. Additionally, transactional data is analyzed to pinpoint occurrences of customers paying twice for financial products, for example paying for insurance or breakdown assistance that is already provided as part of a packaged bank account. By understanding customers better, Financial Institutions can position themselves to meet customer needs.




Techvantage's Customer Analytics frameworks help in understanding customer expectation and retaining customers. Our banking solutions help to clone potential customers to Cross/Up-Sell of different Banking products. Other solutions are like most advanced Credit Scoring Model, Banking Fraud Detection, Customer Lifetime Value, Churn Prediction, KYC Optimization etc.


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