BigData, the Differentiating Factor for Banking Industry
Processing high volumes banking data will increase by seven-fold with the inclusion of Bigdata analytics by 2020.
Bigdata analytics has revolutionized the way banks process their information database; monitoring clients' transactions in real-time has given them an edge over the traditional batch-processing methodology. Cloud data suits allow data aggregation without the need for a physical infrastructure, significantly minimizing cyber threats that feed on locally stored data. Also, the cloud-based operating model enables financial institutions to cater to a large target audience without having to worry about system overloading or compliance reporting, providing scope for increased revenue generation without incurring additionally.
With data-driven analytics becoming a mainstream trend among financial institutions, Bigdata data is tipped to be technology that bolsters security parameters without compromising on value-added services. Multiple surveys have found that most multinational banks are producing hundreds of proof-of-concept use cases to justify their services, providing evidence to applications of Bigdata; metric-based data analytics are balanced by actionable results obtained from compiling structured and unstructured available from various banking terminals.
A recent study shows that banks are using big data to determine customers' behavioral patterns at branches, online portals, ATMS, mobile applications, and customer support centers to provide the right value to the right customer. It is not remotely surprising for banks and non-banking financial institutions to cash in on Bigdata, given the architectural sophistication it brings to the table; leveraging the potential of structured and unstructured data never seemed easier with cloud-based operational models being readily available in the market through multiple PaaS (platform-as-a-Service) vendors.
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