Creating A Customized Banking Experience With Big Data

Customers have different expectations of their bank, and are willing to pay for different services. Big data opens the door for banks to group their customers according to their banking preferences, which can make customers more satisfied and more profitable.

For banks to realize these kinds of customer experience gains and cost savings from big data, data teams need to communicate better with the lines of business, Nicolacakis argues. Many people working on the business side of the bank aren’t knowledgeable about the analytics capabilities that data teams have now, he says. “The businesses just aren’t informed about what the current technology can do. They still think in terms of the old reporting paradigm. But now technology and analytics can use predictive modeling, and they can go and get, and process, data more easily,” he explains.

It’s up to the data team to reach out to the business side of the bank and demonstrate what they can offer for the business, Nicolacakis says. “The data side needs to do this. They need to show what can be done. They should go to the business side and help them understand that they can any questions that they’d like to have answered,” he relates.

This will require some initiative from the data team, but Nickolacakis contends that data teams need to be more experimental and creative. “They need to have a research and development mentality. Right now that simply doesn’t exist in most cases,” he reports. Unless the data team gets more creative and explains to the lines of business what they’re capable of, then the bank will never be able to fully take advantage of its data function.

One area where Nicolacakis thinks data teams could step in and do a lot of positive work for the business is in the mortgage signing process. The process from delivering the mortgage application to when the customer is actually on-boarded can take a significant amount of time, and a lot of documents are emailed back and forth between the customer, agent and bank. This leads to long drawn-out process that frustrates the customer and is inefficient for the bank’s staff. All of the emails that are sent back and forth are unstructured data, Nicolacakis points out, that can be analyzed by the data team. “They can pull those emails and figure out why those transactions are taking so long. They can look for things that are causing exceptions, or find out what the mortgage representatives are not doing well enough to speed the transaction along,” Nicolacakis says.

Jonathan Camhi has been an associate editor with Bank Systems & Technology since 2012. He previously worked as a freelance journalist in New York City covering politics, health and immigration, and has a master's degree from the City University of New York's Graduate School ... View Full Bio

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