Banking on Big Data
Nicolasi has a vision of running a nouveau French restaurant. His grandmother’s cooking, which has its origins in the French Basque Country, has taken on an avant-garde twist ever since she arrived in the US. His friends like her cooking style and Nicolas believes he is ready to give a shot at entrepreneurship on the back of his grandma’s creativity. At the moment, he is discussing a loan with his banker. But, this is where the “I-dream-of-owning-a-restaurant” story takes a turn. The banker has decided to advance Nicolas the working capital, but, in addition, has some fine business advice. Would Nicolas like to know the best locations in the city for a nouveau French restaurant and the price point at which it can garner customer loyalty much faster? Nicolas is all ears.
Many banks would be happy to give Nicolas a loan. Not many would be able to add an invaluable service that opens a new revenue stream for the bank. For Nicolas, the bank sifts through its growing stockpile of customer and transactions data – acquired largely due to regulatory requirements – and churns out remarkable insights. The bank can also help Nicolas acquire customers through targeted promotional campaigns. The bank examines demographic data, income groups, preferences and spend patterns, behavior indicators by area codes and mashes it with social and mobile data, to come-up with great business insights for Nicolas. This is the power of Big Data!
Regulatory pressure has been forcing banks to invest in more data acquisition, storage, licenses, security, AMCs and people to manage the data. Practically every bank today has a big data implementation in terms of Hadoop running on their IT systems. But not all can generate revenue from data – turning a liability into an asset.
Banks and themselves at the intersection of advanced technology and sophisticated customers in a world gone digital. The data coming-in from IVR, web, mobile devices, ATMs, kiosks, CRM, surveys, social networks and partner services can lead to superior personalization of services.
Using advanced analytics on top of Big Data, customer data can help retail banks solve business problems far more complex than those faced by Nicolas. Banks are in the process of transforming their traditional data warehouses into information delivery platforms or ‘Insights-as-a-Service’ – an area that can aid service diversification and improve profitability. ‘Insights-as-a-Service’ will help retail banks go beyond non-interest income products. Banks can also improve top-line growth by acquiring new customers, efficient customer servicing through customer lifetime value maximization, by cross-selling/up-selling new products and services, and preventing customer attrition.