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Big data analytics in retail banking

Banks are using big data analytics to predict customers’ behaviour and deliver the right products and services to the customer at the right time. Banks are analysing different factors such as the customers’ spending patterns and demographics or even social media behaviour and GPS-based location data. Banks are able to track decreases in spending to predict attrition and offer retention rewards, or to spot higher usage of cash withdrawals and late payments that could forecast credit risk and make earlier collections calls.

Also, banks are starting to use prescriptive analytics to determine what to recommend and how to convince customers to take specific actions. Prescriptive analytics looks at what should be done or how a bank can make an action happen, using a combination of graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristic, and machine learning.

That continuing increase in usage of big data analytics and capabilities is delivering a competitive advantage that enables banks to provide the same level of service customers receive from social media or technology giants, which in turn helps them retain their customers and add new ones.

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