"Banking is all about having great customer relationships, understanding customers and making good credit decisions, so naturally that's where we've focused our analytical efforts," says Sam Daish, Head of Enterprise Information at Kiwibank.
Kiwibank has been a fast mover in the data and analytics space, adopting a dual-pronged strategy focused on improving the customer experience and making more intelligent credit and lending decisions.
With an innovation process incorporating Kaizen business principles, Kiwibank looks to its employees at every level of the business to contribute to improvement through innovation.
"Kiwibank is pretty successful at having innovation as part of its DNA and it comes from everywhere within the bank," says Daish. "Because it's generally pretty clear as to what we're trying to achieve, assessing new ideas and choosing which to pursue rapidly becomes clear.
"The most advanced part of the bank is around portfolio analytics and credit analytics but beyond typical applications of this information, it's also improved the ability to process customers more efficiently. Twenty years ago it would take days before you'd get a credit decision; now it's a within a day, if not minutes."
For Kiwibank, part of the solution to making quick credit decisions is being able to predict what their customers wanted from them and when they would want it. "We should always be ready for customers to come to us for a conversation and be prepared for what we expect that conversation to be about."
The other part of the equation for Kiwibank is knowing when it is the right time to approach a customer directly. Recently, Kiwibank used predictive analytics to identify customers currently active, or soon to be active, in the home loan space.
"We started with over 100 sets of data points to try and figure out what was important, and that was rapidly brought down to 7 or 8 crucial factors," says Daish.
Through the use of their analytics and the ability to target the right customers at the right time, Kiwibank were able to reach out to customers about their home loans, 60 per cent of which resulted in a positive outcome for the ban - a staggering result when compared with a typical positive response rate of 10 per cent. "Customers appreciated the call and that was all down to having excellent insight into what customers wanted to achieve and having the analytics to support that.
"I think that is the direction we are going to be going in more and more, and I think that's pretty typical of what most organisations want to achieve from their data."